--- name: skill-creator description: Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy. --- # Skill Creator A skill for creating new skills and iteratively improving them. At a high level, the process of creating a skill goes like this: - Decide what you want the skill to do and roughly how it should do it - Write a draft of the skill - Create a few test prompts and run claude-with-access-to-the-skill on them - Help the user evaluate the results both qualitatively and quantitatively - While the runs happen in the background, draft some quantitative evals if there aren't any (if there are some, you can either use as is or modify if you feel something needs to change about them). Then explain them to the user (or if they already existed, explain the ones that already exist) - Use the `eval-viewer/generate_review.py` script to show the user the results for them to look at, and also let them look at the quantitative metrics - Rewrite the skill based on feedback from the user's evaluation of the results (and also if there are any glaring flaws that become apparent from the quantitative benchmarks) - Repeat until you're satisfied - Expand the test set and try again at larger scale Your job when using this skill is to figure out where the user is in this process and then jump in and help them progress through these stages. So for instance, maybe they're like "I want to make a skill for X". You can help narrow down what they mean, write a draft, write the test cases, figure out how they want to evaluate, run all the prompts, and repeat. On the other hand, maybe they already have a draft of the skill. In this case you can go straight to the eval/iterate part of the loop. Of course, you should always be flexible and if the user is like "I don't need to run a bunch of evaluations, just vibe with me", you can do that instead. Then after the skill is done (but again, the order is flexible), you can also run the skill description improver, which we have a whole separate script for, to optimize the triggering of the skill. Cool? Cool. ## Communicating with the user The skill creator is liable to be used by people across a wide range of familiarity with coding jargon. If you haven't heard (and how could you, it's only very recently that it started), there's a trend now where the power of Claude is inspiring plumbers to open up their terminals, parents and grandparents to google "how to install npm". On the other hand, the bulk of users are probably fairly computer-literate. So please pay attention to context cues to understand how to phrase your communication! In the default case, just to give you some idea: - "evaluation" and "benchmark" are borderline, but OK - for "JSON" and "assertion" you want to see serious cues from the user that they know what those things are before using them without explaining them It's OK to briefly explain terms if you're in doubt, and feel free to clarify terms with a short definition if you're unsure if the user will get it. --- ## Creating a skill ### Capture Intent Start by understanding the user's intent. The current conversation might already contain a workflow the user wants to capture (e.g., they say "turn this into a skill"). If so, extract answers from the conversation history first — the tools used, the sequence of steps, corrections the user made, input/output formats observed. The user may need to fill the gaps, and should confirm before proceeding to the next step. 1. What should this skill enable Claude to do? 2. When should this skill trigger? (what user phrases/contexts) 3. What's the expected output format? 4. Should we set up test cases to verify the skill works? Skills with objectively verifiable outputs (file transforms, data extraction, code generation, fixed workflow steps) benefit from test cases. Skills with subjective outputs (writing style, art) often don't need them. Suggest the appropriate default based on the skill type, but let the user decide. ### Interview and Research Proactively ask questions about edge cases, input/output formats, example files, success criteria, and dependencies. Wait to write test prompts until you've got this part ironed out. Check available MCPs - if useful for research (searching docs, finding similar skills, looking up best practices), research in parallel via subagents if available, otherwise inline. Come prepared with context to reduce burden on the user. ### Write the SKILL.md Based on the user interview, fill in these components: - **name**: Skill identifier - **description**: When to trigger, what it does. This is the primary triggering mechanism - include both what the skill does AND specific contexts for when to use it. All "when to use" info goes here, not in the body. Note: currently Claude has a tendency to "undertrigger" skills -- to not use them when they'd be useful. To combat this, please make the skill descriptions a little bit "pushy". So for instance, instead of "How to build a simple fast dashboard to display internal Anthropic data.", you might write "How to build a simple fast dashboard to display internal Anthropic data. Make sure to use this skill whenever the user mentions dashboards, data visualization, internal metrics, or wants to display any kind of company data, even if they don't explicitly ask for a 'dashboard.'" - **compatibility**: Required tools, dependencies (optional, rarely needed) - **the rest of the skill :)** ### Skill Writing Guide #### Anatomy of a Skill ``` skill-name/ ├── SKILL.md (required) │ ├── YAML frontmatter (name, description required) │ └── Markdown instructions └── Bundled Resources (optional) ├── scripts/ - Executable code for deterministic/repetitive tasks ├── references/ - Docs loaded into context as needed └── assets/ - Files used in output (templates, icons, fonts) ``` #### Progressive Disclosure Skills use a three-level loading system: 1. **Metadata** (name + description) - Always in context (~100 words) 2. **SKILL.md body** - In context whenever skill triggers (<500 lines ideal) 3. **Bundled resources** - As needed (unlimited, scripts can execute without loading) These word counts are approximate and you can feel free to go longer if needed. **Key patterns:** - Keep SKILL.md under 500 lines; if you're approaching this limit, add an additional layer of hierarchy along with clear pointers about where the model using the skill should go next to follow up. - Reference files clearly from SKILL.md with guidance on when to read them - For large reference files (>300 lines), include a table of contents **Domain organization**: When a skill supports multiple domains/frameworks, organize by variant: ``` cloud-deploy/ ├── SKILL.md (workflow + selection) └── references/ ├── aws.md ├── gcp.md └── azure.md ``` Claude reads only the relevant reference file. #### Principle of Lack of Surprise This goes without saying, but skills must not contain malware, exploit code, or any content that could compromise system security. A skill's contents should not surprise the user in their intent if described. Don't go along with requests to create misleading skills or skills designed to facilitate unauthorized access, data exfiltration, or other malicious activities. Things like a "roleplay as an XYZ" are OK though. #### Writing Patterns Prefer using the imperative form in instructions. **Defining output formats** - You can do it like this: ```markdown ## Report structure ALWAYS use this exact template: # [Title] ## Executive summary ## Key findings ## Recommendations ``` **Examples pattern** - It's useful to include examples. You can format them like this (but if "Input" and "Output" are in the examples you might want to deviate a little): ```markdown ## Commit message format **Example 1:** Input: Added user authentication with JWT tokens Output: feat(auth): implement JWT-based authentication ``` ### Writing Style Try to explain to the model why things are important in lieu of heavy-handed musty MUSTs. Use theory of mind and try to make the skill general and not super-narrow to specific examples. Start by writing a draft and then look at it with fresh eyes and improve it. ### Test Cases After writing the skill draft, come up with 2-3 realistic test prompts — the kind of thing a real user would actually say. Share them with the user: [you don't have to use this exact language] "Here are a few test cases I'd like to try. Do these look right, or do you want to add more?" Then run them. Save test cases to `evals/evals.json`. Don't write assertions yet — just the prompts. You'll draft assertions in the next step while the runs are in progress. ```json { "skill_name": "example-skill", "evals": [ { "id": 1, "prompt": "User's task prompt", "expected_output": "Description of expected result", "files": [] } ] } ``` See `references/schemas.md` for the full schema (including the `assertions` field, which you'll add later). ## Running and evaluating test cases This section is one continuous sequence — don't stop partway through. Do NOT use `/skill-test` or any other testing skill. Put results in `-workspace/` as a sibling to the skill directory. Within the workspace, organize results by iteration (`iteration-1/`, `iteration-2/`, etc.) and within that, each test case gets a directory (`eval-0/`, `eval-1/`, etc.). Don't create all of this upfront — just create directories as you go. ### Step 1: Spawn all runs (with-skill AND baseline) in the same turn For each test case, spawn two subagents in the same turn — one with the skill, one without. This is important: don't spawn the with-skill runs first and then come back for baselines later. Launch everything at once so it all finishes around the same time. **With-skill run:** ``` Execute this task: - Skill path: - Task: - Input files: - Save outputs to: /iteration-/eval-/with_skill/outputs/ - Outputs to save: ``` **Baseline run** (same prompt, but the baseline depends on context): - **Creating a new skill**: no skill at all. Same prompt, no skill path, save to `without_skill/outputs/`. - **Improving an existing skill**: the old version. Before editing, snapshot the skill (`cp -r /skill-snapshot/`), then point the baseline subagent at the snapshot. Save to `old_skill/outputs/`. Write an `eval_metadata.json` for each test case (assertions can be empty for now). Give each eval a descriptive name based on what it's testing — not just "eval-0". Use this name for the directory too. If this iteration uses new or modified eval prompts, create these files for each new eval directory — don't assume they carry over from previous iterations. ```json { "eval_id": 0, "eval_name": "descriptive-name-here", "prompt": "The user's task prompt", "assertions": [] } ``` ### Step 2: While runs are in progress, draft assertions Don't just wait for the runs to finish — you can use this time productively. Draft quantitative assertions for each test case and explain them to the user. If assertions already exist in `evals/evals.json`, review them and explain what they check. Good assertions are objectively verifiable and have descriptive names — they should read clearly in the benchmark viewer so someone glancing at the results immediately understands what each one checks. Subjective skills (writing style, design quality) are better evaluated qualitatively — don't force assertions onto things that need human judgment. Update the `eval_metadata.json` files and `evals/evals.json` with the assertions once drafted. Also explain to the user what they'll see in the viewer — both the qualitative outputs and the quantitative benchmark. ### Step 3: As runs complete, capture timing data When each subagent task completes, you receive a notification containing `total_tokens` and `duration_ms`. Save this data immediately to `timing.json` in the run directory: ```json { "total_tokens": 84852, "duration_ms": 23332, "total_duration_seconds": 23.3 } ``` This is the only opportunity to capture this data — it comes through the task notification and isn't persisted elsewhere. Process each notification as it arrives rather than trying to batch them. ### Step 4: Grade, aggregate, and launch the viewer Once all runs are done: 1. **Grade each run** — spawn a grader subagent (or grade inline) that reads `agents/grader.md` and evaluates each assertion against the outputs. Save results to `grading.json` in each run directory. The grading.json expectations array must use the fields `text`, `passed`, and `evidence` (not `name`/`met`/`details` or other variants) — the viewer depends on these exact field names. For assertions that can be checked programmatically, write and run a script rather than eyeballing it — scripts are faster, more reliable, and can be reused across iterations. 2. **Aggregate into benchmark** — run the aggregation script from the skill-creator directory: ```bash python -m scripts.aggregate_benchmark /iteration-N --skill-name ``` This produces `benchmark.json` and `benchmark.md` with pass_rate, time, and tokens for each configuration, with mean ± stddev and the delta. If generating benchmark.json manually, see `references/schemas.md` for the exact schema the viewer expects. Put each with_skill version before its baseline counterpart. 3. **Do an analyst pass** — read the benchmark data and surface patterns the aggregate stats might hide. See `agents/analyzer.md` (the "Analyzing Benchmark Results" section) for what to look for — things like assertions that always pass regardless of skill (non-discriminating), high-variance evals (possibly flaky), and time/token tradeoffs. 4. **Launch the viewer** with both qualitative outputs and quantitative data: ```bash nohup python /eval-viewer/generate_review.py \ /iteration-N \ --skill-name "my-skill" \ --benchmark /iteration-N/benchmark.json \ > /dev/null 2>&1 & VIEWER_PID=$! ``` For iteration 2+, also pass `--previous-workspace /iteration-`. **Cowork / headless environments:** If `webbrowser.open()` is not available or the environment has no display, use `--static ` to write a standalone HTML file instead of starting a server. Feedback will be downloaded as a `feedback.json` file when the user clicks "Submit All Reviews". After download, copy `feedback.json` into the workspace directory for the next iteration to pick up. Note: please use generate_review.py to create the viewer; there's no need to write custom HTML. 5. **Tell the user** something like: "I've opened the results in your browser. There are two tabs — 'Outputs' lets you click through each test case and leave feedback, 'Benchmark' shows the quantitative comparison. When you're done, come back here and let me know." ### What the user sees in the viewer The "Outputs" tab shows one test case at a time: - **Prompt**: the task that was given - **Output**: the files the skill produced, rendered inline where possible - **Previous Output** (iteration 2+): collapsed section showing last iteration's output - **Formal Grades** (if grading was run): collapsed section showing assertion pass/fail - **Feedback**: a textbox that auto-saves as they type - **Previous Feedback** (iteration 2+): their comments from last time, shown below the textbox The "Benchmark" tab shows the stats summary: pass rates, timing, and token usage for each configuration, with per-eval breakdowns and analyst observations. Navigation is via prev/next buttons or arrow keys. When done, they click "Submit All Reviews" which saves all feedback to `feedback.json`. ### Step 5: Read the feedback When the user tells you they're done, read `feedback.json`: ```json { "reviews": [ {"run_id": "eval-0-with_skill", "feedback": "the chart is missing axis labels", "timestamp": "..."}, {"run_id": "eval-1-with_skill", "feedback": "", "timestamp": "..."}, {"run_id": "eval-2-with_skill", "feedback": "perfect, love this", "timestamp": "..."} ], "status": "complete" } ``` Empty feedback means the user thought it was fine. Focus your improvements on the test cases where the user had specific complaints. Kill the viewer server when you're done with it: ```bash kill $VIEWER_PID 2>/dev/null ``` --- ## Improving the skill This is the heart of the loop. You've run the test cases, the user has reviewed the results, and now you need to make the skill better based on their feedback. ### How to think about improvements 1. **Generalize from the feedback.** The big picture thing that's happening here is that we're trying to create skills that can be used a million times (maybe literally, maybe even more who knows) across many different prompts. Here you and the user are iterating on only a few examples over and over again because it helps move faster. The user knows these examples in and out and it's quick for them to assess new outputs. But if the skill you and the user are codeveloping works only for those examples, it's useless. Rather than put in fiddly overfitty changes, or oppressively constrictive MUSTs, if there's some stubborn issue, you might try branching out and using different metaphors, or recommending different patterns of working. It's relatively cheap to try and maybe you'll land on something great. 2. **Keep the prompt lean.** Remove things that aren't pulling their weight. Make sure to read the transcripts, not just the final outputs — if it looks like the skill is making the model waste a bunch of time doing things that are unproductive, you can try getting rid of the parts of the skill that are making it do that and seeing what happens. 3. **Explain the why.** Try hard to explain the **why** behind everything you're asking the model to do. Today's LLMs are *smart*. They have good theory of mind and when given a good harness can go beyond rote instructions and really make things happen. Even if the feedback from the user is terse or frustrated, try to actually understand the task and why the user is writing what they wrote, and what they actually wrote, and then transmit this understanding into the instructions. If you find yourself writing ALWAYS or NEVER in all caps, or using super rigid structures, that's a yellow flag — if possible, reframe and explain the reasoning so that the model understands why the thing you're asking for is important. That's a more humane, powerful, and effective approach. 4. **Look for repeated work across test cases.** Read the transcripts from the test runs and notice if the subagents all independently wrote similar helper scripts or took the same multi-step approach to something. If all 3 test cases resulted in the subagent writing a `create_docx.py` or a `build_chart.py`, that's a strong signal the skill should bundle that script. Write it once, put it in `scripts/`, and tell the skill to use it. This saves every future invocation from reinventing the wheel. This task is pretty important (we are trying to create billions a year in economic value here!) and your thinking time is not the blocker; take your time and really mull things over. I'd suggest writing a draft revision and then looking at it anew and making improvements. Really do your best to get into the head of the user and understand what they want and need. ### The iteration loop After improving the skill: 1. Apply your improvements to the skill 2. Rerun all test cases into a new `iteration-/` directory, including baseline runs. If you're creating a new skill, the baseline is always `without_skill` (no skill) — that stays the same across iterations. If you're improving an existing skill, use your judgment on what makes sense as the baseline: the original version the user came in with, or the previous iteration. 3. Launch the reviewer with `--previous-workspace` pointing at the previous iteration 4. Wait for the user to review and tell you they're done 5. Read the new feedback, improve again, repeat Keep going until: - The user says they're happy - The feedback is all empty (everything looks good) - You're not making meaningful progress --- ## Advanced: Blind comparison For situations where you want a more rigorous comparison between two versions of a skill (e.g., the user asks "is the new version actually better?"), there's a blind comparison system. Read `agents/comparator.md` and `agents/analyzer.md` for the details. The basic idea is: give two outputs to an independent agent without telling it which is which, and let it judge quality. Then analyze why the winner won. This is optional, requires subagents, and most users won't need it. The human review loop is usually sufficient. --- ## Description Optimization The description field in SKILL.md frontmatter is the primary mechanism that determines whether Claude invokes a skill. After creating or improving a skill, offer to optimize the description for better triggering accuracy. ### Step 1: Generate trigger eval queries Create 20 eval queries — a mix of should-trigger and should-not-trigger. Save as JSON: ```json [ {"query": "the user prompt", "should_trigger": true}, {"query": "another prompt", "should_trigger": false} ] ``` The queries must be realistic and something a Claude Code or Claude.ai user would actually type. Not abstract requests, but requests that are concrete and specific and have a good amount of detail. For instance, file paths, personal context about the user's job or situation, column names and values, company names, URLs. A little bit of backstory. Some might be in lowercase or contain abbreviations or typos or casual speech. Use a mix of different lengths, and focus on edge cases rather than making them clear-cut (the user will get a chance to sign off on them). Bad: `"Format this data"`, `"Extract text from PDF"`, `"Create a chart"` Good: `"ok so my boss just sent me this xlsx file (its in my downloads, called something like 'Q4 sales final FINAL v2.xlsx') and she wants me to add a column that shows the profit margin as a percentage. The revenue is in column C and costs are in column D i think"` For the **should-trigger** queries (8-10), think about coverage. You want different phrasings of the same intent — some formal, some casual. Include cases where the user doesn't explicitly name the skill or file type but clearly needs it. Throw in some uncommon use cases and cases where this skill competes with another but should win. For the **should-not-trigger** queries (8-10), the most valuable ones are the near-misses — queries that share keywords or concepts with the skill but actually need something different. Think adjacent domains, ambiguous phrasing where a naive keyword match would trigger but shouldn't, and cases where the query touches on something the skill does but in a context where another tool is more appropriate. The key thing to avoid: don't make should-not-trigger queries obviously irrelevant. "Write a fibonacci function" as a negative test for a PDF skill is too easy — it doesn't test anything. The negative cases should be genuinely tricky. ### Step 2: Review with user Present the eval set to the user for review using the HTML template: 1. Read the template from `assets/eval_review.html` 2. Replace the placeholders: - `__EVAL_DATA_PLACEHOLDER__` → the JSON array of eval items (no quotes around it — it's a JS variable assignment) - `__SKILL_NAME_PLACEHOLDER__` → the skill's name - `__SKILL_DESCRIPTION_PLACEHOLDER__` → the skill's current description 3. Write to a temp file (e.g., `/tmp/eval_review_.html`) and open it: `open /tmp/eval_review_.html` 4. The user can edit queries, toggle should-trigger, add/remove entries, then click "Export Eval Set" 5. The file downloads to `~/Downloads/eval_set.json` — check the Downloads folder for the most recent version in case there are multiple (e.g., `eval_set (1).json`) This step matters — bad eval queries lead to bad descriptions. ### Step 3: Run the optimization loop Tell the user: "This will take some time — I'll run the optimization loop in the background and check on it periodically." Save the eval set to the workspace, then run in the background: ```bash python -m scripts.run_loop \ --eval-set \ --skill-path \ --model \ --max-iterations 5 \ --verbose ``` Use the model ID from your system prompt (the one powering the current session) so the triggering test matches what the user actually experiences. While it runs, periodically tail the output to give the user updates on which iteration it's on and what the scores look like. This handles the full optimization loop automatically. It splits the eval set into 60% train and 40% held-out test, evaluates the current description (running each query 3 times to get a reliable trigger rate), then calls Claude to propose improvements based on what failed. It re-evaluates each new description on both train and test, iterating up to 5 times. When it's done, it opens an HTML report in the browser showing the results per iteration and returns JSON with `best_description` — selected by test score rather than train score to avoid overfitting. ### How skill triggering works Understanding the triggering mechanism helps design better eval queries. Skills appear in Claude's `available_skills` list with their name + description, and Claude decides whether to consult a skill based on that description. The important thing to know is that Claude only consults skills for tasks it can't easily handle on its own — simple, one-step queries like "read this PDF" may not trigger a skill even if the description matches perfectly, because Claude can handle them directly with basic tools. Complex, multi-step, or specialized queries reliably trigger skills when the description matches. This means your eval queries should be substantive enough that Claude would actually benefit from consulting a skill. Simple queries like "read file X" are poor test cases — they won't trigger skills regardless of description quality. ### Step 4: Apply the result Take `best_description` from the JSON output and update the skill's SKILL.md frontmatter. Show the user before/after and report the scores. --- ### Package and Present (only if `present_files` tool is available) Check whether you have access to the `present_files` tool. If you don't, skip this step. If you do, package the skill and present the .skill file to the user: ```bash python -m scripts.package_skill ``` After packaging, direct the user to the resulting `.skill` file path so they can install it. --- ## Claude.ai-specific instructions In Claude.ai, the core workflow is the same (draft → test → review → improve → repeat), but because Claude.ai doesn't have subagents, some mechanics change. Here's what to adapt: **Running test cases**: No subagents means no parallel execution. For each test case, read the skill's SKILL.md, then follow its instructions to accomplish the test prompt yourself. Do them one at a time. This is less rigorous than independent subagents (you wrote the skill and you're also running it, so you have full context), but it's a useful sanity check — and the human review step compensates. Skip the baseline runs — just use the skill to complete the task as requested. **Reviewing results**: If you can't open a browser (e.g., Claude.ai's VM has no display, or you're on a remote server), skip the browser reviewer entirely. Instead, present results directly in the conversation. For each test case, show the prompt and the output. If the output is a file the user needs to see (like a .docx or .xlsx), save it to the filesystem and tell them where it is so they can download and inspect it. Ask for feedback inline: "How does this look? Anything you'd change?" **Benchmarking**: Skip the quantitative benchmarking — it relies on baseline comparisons which aren't meaningful without subagents. Focus on qualitative feedback from the user. **The iteration loop**: Same as before — improve the skill, rerun the test cases, ask for feedback — just without the browser reviewer in the middle. You can still organize results into iteration directories on the filesystem if you have one. **Description optimization**: This section requires the `claude` CLI tool (specifically `claude -p`) which is only available in Claude Code. Skip it if you're on Claude.ai. **Blind comparison**: Requires subagents. Skip it. **Packaging**: The `package_skill.py` script works anywhere with Python and a filesystem. On Claude.ai, you can run it and the user can download the resulting `.skill` file. **Updating an existing skill**: The user might be asking you to update an existing skill, not create a new one. In this case: - **Preserve the original name.** Note the skill's directory name and `name` frontmatter field -- use them unchanged. E.g., if the installed skill is `research-helper`, output `research-helper.skill` (not `research-helper-v2`). - **Copy to a writeable location before editing.** The installed skill path may be read-only. Copy to `/tmp/skill-name/`, edit there, and package from the copy. - **If packaging manually, stage in `/tmp/` first**, then copy to the output directory -- direct writes may fail due to permissions. --- ## Cowork-Specific Instructions If you're in Cowork, the main things to know are: - You have subagents, so the main workflow (spawn test cases in parallel, run baselines, grade, etc.) all works. (However, if you run into severe problems with timeouts, it's OK to run the test prompts in series rather than parallel.) - You don't have a browser or display, so when generating the eval viewer, use `--static ` to write a standalone HTML file instead of starting a server. Then proffer a link that the user can click to open the HTML in their browser. - For whatever reason, the Cowork setup seems to disincline Claude from generating the eval viewer after running the tests, so just to reiterate: whether you're in Cowork or in Claude Code, after running tests, you should always generate the eval viewer for the human to look at examples before revising the skill yourself and trying to make corrections, using `generate_review.py` (not writing your own boutique html code). Sorry in advance but I'm gonna go all caps here: GENERATE THE EVAL VIEWER *BEFORE* evaluating inputs yourself. You want to get them in front of the human ASAP! - Feedback works differently: since there's no running server, the viewer's "Submit All Reviews" button will download `feedback.json` as a file. You can then read it from there (you may have to request access first). - Packaging works — `package_skill.py` just needs Python and a filesystem. - Description optimization (`run_loop.py` / `run_eval.py`) should work in Cowork just fine since it uses `claude -p` via subprocess, not a browser, but please save it until you've fully finished making the skill and the user agrees it's in good shape. - **Updating an existing skill**: The user might be asking you to update an existing skill, not create a new one. Follow the update guidance in the claude.ai section above. --- ## Reference files The agents/ directory contains instructions for specialized subagents. Read them when you need to spawn the relevant subagent. - `agents/grader.md` — How to evaluate assertions against outputs - `agents/comparator.md` — How to do blind A/B comparison between two outputs - `agents/analyzer.md` — How to analyze why one version beat another The references/ directory has additional documentation: - `references/schemas.md` — JSON structures for evals.json, grading.json, etc. --- Repeating one more time the core loop here for emphasis: - Figure out what the skill is about - Draft or edit the skill - Run claude-with-access-to-the-skill on test prompts - With the user, evaluate the outputs: - Create benchmark.json and run `eval-viewer/generate_review.py` to help the user review them - Run quantitative evals - Repeat until you and the user are satisfied - Package the final skill and return it to the user. Please add steps to your TodoList, if you have such a thing, to make sure you don't forget. If you're in Cowork, please specifically put "Create evals JSON and run `eval-viewer/generate_review.py` so human can review test cases" in your TodoList to make sure it happens. Good luck! FILE:agents/analyzer.md # Post-hoc Analyzer Agent Analyze blind comparison results to understand WHY the winner won and generate improvement suggestions. ## Role After the blind comparator determines a winner, the Post-hoc Analyzer "unblids" the results by examining the skills and transcripts. The goal is to extract actionable insights: what made the winner better, and how can the loser be improved? ## Inputs You receive these parameters in your prompt: - **winner**: "A" or "B" (from blind comparison) - **winner_skill_path**: Path to the skill that produced the winning output - **winner_transcript_path**: Path to the execution transcript for the winner - **loser_skill_path**: Path to the skill that produced the losing output - **loser_transcript_path**: Path to the execution transcript for the loser - **comparison_result_path**: Path to the blind comparator's output JSON - **output_path**: Where to save the analysis results ## Process ### Step 1: Read Comparison Result 1. Read the blind comparator's output at comparison_result_path 2. Note the winning side (A or B), the reasoning, and any scores 3. Understand what the comparator valued in the winning output ### Step 2: Read Both Skills 1. Read the winner skill's SKILL.md and key referenced files 2. Read the loser skill's SKILL.md and key referenced files 3. Identify structural differences: - Instructions clarity and specificity - Script/tool usage patterns - Example coverage - Edge case handling ### Step 3: Read Both Transcripts 1. Read the winner's transcript 2. Read the loser's transcript 3. Compare execution patterns: - How closely did each follow their skill's instructions? - What tools were used differently? - Where did the loser diverge from optimal behavior? - Did either encounter errors or make recovery attempts? ### Step 4: Analyze Instruction Following For each transcript, evaluate: - Did the agent follow the skill's explicit instructions? - Did the agent use the skill's provided tools/scripts? - Were there missed opportunities to leverage skill content? - Did the agent add unnecessary steps not in the skill? Score instruction following 1-10 and note specific issues. ### Step 5: Identify Winner Strengths Determine what made the winner better: - Clearer instructions that led to better behavior? - Better scripts/tools that produced better output? - More comprehensive examples that guided edge cases? - Better error handling guidance? Be specific. Quote from skills/transcripts where relevant. ### Step 6: Identify Loser Weaknesses Determine what held the loser back: - Ambiguous instructions that led to suboptimal choices? - Missing tools/scripts that forced workarounds? - Gaps in edge case coverage? - Poor error handling that caused failures? ### Step 7: Generate Improvement Suggestions Based on the analysis, produce actionable suggestions for improving the loser skill: - Specific instruction changes to make - Tools/scripts to add or modify - Examples to include - Edge cases to address Prioritize by impact. Focus on changes that would have changed the outcome. ### Step 8: Write Analysis Results Save structured analysis to `{output_path}`. ## Output Format Write a JSON file with this structure: ```json { "comparison_summary": { "winner": "A", "winner_skill": "path/to/winner/skill", "loser_skill": "path/to/loser/skill", "comparator_reasoning": "Brief summary of why comparator chose winner" }, "winner_strengths": [ "Clear step-by-step instructions for handling multi-page documents", "Included validation script that caught formatting errors", "Explicit guidance on fallback behavior when OCR fails" ], "loser_weaknesses": [ "Vague instruction 'process the document appropriately' led to inconsistent behavior", "No script for validation, agent had to improvise and made errors", "No guidance on OCR failure, agent gave up instead of trying alternatives" ], "instruction_following": { "winner": { "score": 9, "issues": [ "Minor: skipped optional logging step" ] }, "loser": { "score": 6, "issues": [ "Did not use the skill's formatting template", "Invented own approach instead of following step 3", "Missed the 'always validate output' instruction" ] } }, "improvement_suggestions": [ { "priority": "high", "category": "instructions", "suggestion": "Replace 'process the document appropriately' with explicit steps: 1) Extract text, 2) Identify sections, 3) Format per template", "expected_impact": "Would eliminate ambiguity that caused inconsistent behavior" }, { "priority": "high", "category": "tools", "suggestion": "Add validate_output.py script similar to winner skill's validation approach", "expected_impact": "Would catch formatting errors before final output" }, { "priority": "medium", "category": "error_handling", "suggestion": "Add fallback instructions: 'If OCR fails, try: 1) different resolution, 2) image preprocessing, 3) manual extraction'", "expected_impact": "Would prevent early failure on difficult documents" } ], "transcript_insights": { "winner_execution_pattern": "Read skill -> Followed 5-step process -> Used validation script -> Fixed 2 issues -> Produced output", "loser_execution_pattern": "Read skill -> Unclear on approach -> Tried 3 different methods -> No validation -> Output had errors" } } ``` ## Guidelines - **Be specific**: Quote from skills and transcripts, don't just say "instructions were unclear" - **Be actionable**: Suggestions should be concrete changes, not vague advice - **Focus on skill improvements**: The goal is to improve the losing skill, not critique the agent - **Prioritize by impact**: Which changes would most likely have changed the outcome? - **Consider causation**: Did the skill weakness actually cause the worse output, or is it incidental? - **Stay objective**: Analyze what happened, don't editorialize - **Think about generalization**: Would this improvement help on other evals too? ## Categories for Suggestions Use these categories to organize improvement suggestions: | Category | Description | |----------|-------------| | `instructions` | Changes to the skill's prose instructions | | `tools` | Scripts, templates, or utilities to add/modify | | `examples` | Example inputs/outputs to include | | `error_handling` | Guidance for handling failures | | `structure` | Reorganization of skill content | | `references` | External docs or resources to add | ## Priority Levels - **high**: Would likely change the outcome of this comparison - **medium**: Would improve quality but may not change win/loss - **low**: Nice to have, marginal improvement --- # Analyzing Benchmark Results When analyzing benchmark results, the analyzer's purpose is to **surface patterns and anomalies** across multiple runs, not suggest skill improvements. ## Role Review all benchmark run results and generate freeform notes that help the user understand skill performance. Focus on patterns that wouldn't be visible from aggregate metrics alone. ## Inputs You receive these parameters in your prompt: - **benchmark_data_path**: Path to the in-progress benchmark.json with all run results - **skill_path**: Path to the skill being benchmarked - **output_path**: Where to save the notes (as JSON array of strings) ## Process ### Step 1: Read Benchmark Data 1. Read the benchmark.json containing all run results 2. Note the configurations tested (with_skill, without_skill) 3. Understand the run_summary aggregates already calculated ### Step 2: Analyze Per-Assertion Patterns For each expectation across all runs: - Does it **always pass** in both configurations? (may not differentiate skill value) - Does it **always fail** in both configurations? (may be broken or beyond capability) - Does it **always pass with skill but fail without**? (skill clearly adds value here) - Does it **always fail with skill but pass without**? (skill may be hurting) - Is it **highly variable**? (flaky expectation or non-deterministic behavior) ### Step 3: Analyze Cross-Eval Patterns Look for patterns across evals: - Are certain eval types consistently harder/easier? - Do some evals show high variance while others are stable? - Are there surprising results that contradict expectations? ### Step 4: Analyze Metrics Patterns Look at time_seconds, tokens, tool_calls: - Does the skill significantly increase execution time? - Is there high variance in resource usage? - Are there outlier runs that skew the aggregates? ### Step 5: Generate Notes Write freeform observations as a list of strings. Each note should: - State a specific observation - Be grounded in the data (not speculation) - Help the user understand something the aggregate metrics don't show Examples: - "Assertion 'Output is a PDF file' passes 100% in both configurations - may not differentiate skill value" - "Eval 3 shows high variance (50% ± 40%) - run 2 had an unusual failure that may be flaky" - "Without-skill runs consistently fail on table extraction expectations (0% pass rate)" - "Skill adds 13s average execution time but improves pass rate by 50%" - "Token usage is 80% higher with skill, primarily due to script output parsing" - "All 3 without-skill runs for eval 1 produced empty output" ### Step 6: Write Notes Save notes to `{output_path}` as a JSON array of strings: ```json [ "Assertion 'Output is a PDF file' passes 100% in both configurations - may not differentiate skill value", "Eval 3 shows high variance (50% ± 40%) - run 2 had an unusual failure", "Without-skill runs consistently fail on table extraction expectations", "Skill adds 13s average execution time but improves pass rate by 50%" ] ``` ## Guidelines **DO:** - Report what you observe in the data - Be specific about which evals, expectations, or runs you're referring to - Note patterns that aggregate metrics would hide - Provide context that helps interpret the numbers **DO NOT:** - Suggest improvements to the skill (that's for the improvement step, not benchmarking) - Make subjective quality judgments ("the output was good/bad") - Speculate about causes without evidence - Repeat information already in the run_summary aggregates FILE:agents/comparator.md # Blind Comparator Agent Compare two outputs WITHOUT knowing which skill produced them. ## Role The Blind Comparator judges which output better accomplishes the eval task. You receive two outputs labeled A and B, but you do NOT know which skill produced which. This prevents bias toward a particular skill or approach. Your judgment is based purely on output quality and task completion. ## Inputs You receive these parameters in your prompt: - **output_a_path**: Path to the first output file or directory - **output_b_path**: Path to the second output file or directory - **eval_prompt**: The original task/prompt that was executed - **expectations**: List of expectations to check (optional - may be empty) ## Process ### Step 1: Read Both Outputs 1. Examine output A (file or directory) 2. Examine output B (file or directory) 3. Note the type, structure, and content of each 4. If outputs are directories, examine all relevant files inside ### Step 2: Understand the Task 1. Read the eval_prompt carefully 2. Identify what the task requires: - What should be produced? - What qualities matter (accuracy, completeness, format)? - What would distinguish a good output from a poor one? ### Step 3: Generate Evaluation Rubric Based on the task, generate a rubric with two dimensions: **Content Rubric** (what the output contains): | Criterion | 1 (Poor) | 3 (Acceptable) | 5 (Excellent) | |-----------|----------|----------------|---------------| | Correctness | Major errors | Minor errors | Fully correct | | Completeness | Missing key elements | Mostly complete | All elements present | | Accuracy | Significant inaccuracies | Minor inaccuracies | Accurate throughout | **Structure Rubric** (how the output is organized): | Criterion | 1 (Poor) | 3 (Acceptable) | 5 (Excellent) | |-----------|----------|----------------|---------------| | Organization | Disorganized | Reasonably organized | Clear, logical structure | | Formatting | Inconsistent/broken | Mostly consistent | Professional, polished | | Usability | Difficult to use | Usable with effort | Easy to use | Adapt criteria to the specific task. For example: - PDF form → "Field alignment", "Text readability", "Data placement" - Document → "Section structure", "Heading hierarchy", "Paragraph flow" - Data output → "Schema correctness", "Data types", "Completeness" ### Step 4: Evaluate Each Output Against the Rubric For each output (A and B): 1. **Score each criterion** on the rubric (1-5 scale) 2. **Calculate dimension totals**: Content score, Structure score 3. **Calculate overall score**: Average of dimension scores, scaled to 1-10 ### Step 5: Check Assertions (if provided) If expectations are provided: 1. Check each expectation against output A 2. Check each expectation against output B 3. Count pass rates for each output 4. Use expectation scores as secondary evidence (not the primary decision factor) ### Step 6: Determine the Winner Compare A and B based on (in priority order): 1. **Primary**: Overall rubric score (content + structure) 2. **Secondary**: Assertion pass rates (if applicable) 3. **Tiebreaker**: If truly equal, declare a TIE Be decisive - ties should be rare. One output is usually better, even if marginally. ### Step 7: Write Comparison Results Save results to a JSON file at the path specified (or `comparison.json` if not specified). ## Output Format Write a JSON file with this structure: ```json { "winner": "A", "reasoning": "Output A provides a complete solution with proper formatting and all required fields. Output B is missing the date field and has formatting inconsistencies.", "rubric": { "A": { "content": { "correctness": 5, "completeness": 5, "accuracy": 4 }, "structure": { "organization": 4, "formatting": 5, "usability": 4 }, "content_score": 4.7, "structure_score": 4.3, "overall_score": 9.0 }, "B": { "content": { "correctness": 3, "completeness": 2, "accuracy": 3 }, "structure": { "organization": 3, "formatting": 2, "usability": 3 }, "content_score": 2.7, "structure_score": 2.7, "overall_score": 5.4 } }, "output_quality": { "A": { "score": 9, "strengths": ["Complete solution", "Well-formatted", "All fields present"], "weaknesses": ["Minor style inconsistency in header"] }, "B": { "score": 5, "strengths": ["Readable output", "Correct basic structure"], "weaknesses": ["Missing date field", "Formatting inconsistencies", "Partial data extraction"] } }, "expectation_results": { "A": { "passed": 4, "total": 5, "pass_rate": 0.80, "details": [ {"text": "Output includes name", "passed": true}, {"text": "Output includes date", "passed": true}, {"text": "Format is PDF", "passed": true}, {"text": "Contains signature", "passed": false}, {"text": "Readable text", "passed": true} ] }, "B": { "passed": 3, "total": 5, "pass_rate": 0.60, "details": [ {"text": "Output includes name", "passed": true}, {"text": "Output includes date", "passed": false}, {"text": "Format is PDF", "passed": true}, {"text": "Contains signature", "passed": false}, {"text": "Readable text", "passed": true} ] } } } ``` If no expectations were provided, omit the `expectation_results` field entirely. ## Field Descriptions - **winner**: "A", "B", or "TIE" - **reasoning**: Clear explanation of why the winner was chosen (or why it's a tie) - **rubric**: Structured rubric evaluation for each output - **content**: Scores for content criteria (correctness, completeness, accuracy) - **structure**: Scores for structure criteria (organization, formatting, usability) - **content_score**: Average of content criteria (1-5) - **structure_score**: Average of structure criteria (1-5) - **overall_score**: Combined score scaled to 1-10 - **output_quality**: Summary quality assessment - **score**: 1-10 rating (should match rubric overall_score) - **strengths**: List of positive aspects - **weaknesses**: List of issues or shortcomings - **expectation_results**: (Only if expectations provided) - **passed**: Number of expectations that passed - **total**: Total number of expectations - **pass_rate**: Fraction passed (0.0 to 1.0) - **details**: Individual expectation results ## Guidelines - **Stay blind**: DO NOT try to infer which skill produced which output. Judge purely on output quality. - **Be specific**: Cite specific examples when explaining strengths and weaknesses. - **Be decisive**: Choose a winner unless outputs are genuinely equivalent. - **Output quality first**: Assertion scores are secondary to overall task completion. - **Be objective**: Don't favor outputs based on style preferences; focus on correctness and completeness. - **Explain your reasoning**: The reasoning field should make it clear why you chose the winner. - **Handle edge cases**: If both outputs fail, pick the one that fails less badly. If both are excellent, pick the one that's marginally better. FILE:agents/grader.md # Grader Agent Evaluate expectations against an execution transcript and outputs. ## Role The Grader reviews a transcript and output files, then determines whether each expectation passes or fails. Provide clear evidence for each judgment. You have two jobs: grade the outputs, and critique the evals themselves. A passing grade on a weak assertion is worse than useless — it creates false confidence. When you notice an assertion that's trivially satisfied, or an important outcome that no assertion checks, say so. ## Inputs You receive these parameters in your prompt: - **expectations**: List of expectations to evaluate (strings) - **transcript_path**: Path to the execution transcript (markdown file) - **outputs_dir**: Directory containing output files from execution ## Process ### Step 1: Read the Transcript 1. Read the transcript file completely 2. Note the eval prompt, execution steps, and final result 3. Identify any issues or errors documented ### Step 2: Examine Output Files 1. List files in outputs_dir 2. Read/examine each file relevant to the expectations. If outputs aren't plain text, use the inspection tools provided in your prompt — don't rely solely on what the transcript says the executor produced. 3. Note contents, structure, and quality ### Step 3: Evaluate Each Assertion For each expectation: 1. **Search for evidence** in the transcript and outputs 2. **Determine verdict**: - **PASS**: Clear evidence the expectation is true AND the evidence reflects genuine task completion, not just surface-level compliance - **FAIL**: No evidence, or evidence contradicts the expectation, or the evidence is superficial (e.g., correct filename but empty/wrong content) 3. **Cite the evidence**: Quote the specific text or describe what you found ### Step 4: Extract and Verify Claims Beyond the predefined expectations, extract implicit claims from the outputs and verify them: 1. **Extract claims** from the transcript and outputs: - Factual statements ("The form has 12 fields") - Process claims ("Used pypdf to fill the form") - Quality claims ("All fields were filled correctly") 2. **Verify each claim**: - **Factual claims**: Can be checked against the outputs or external sources - **Process claims**: Can be verified from the transcript - **Quality claims**: Evaluate whether the claim is justified 3. **Flag unverifiable claims**: Note claims that cannot be verified with available information This catches issues that predefined expectations might miss. ### Step 5: Read User Notes If `{outputs_dir}/user_notes.md` exists: 1. Read it and note any uncertainties or issues flagged by the executor 2. Include relevant concerns in the grading output 3. These may reveal problems even when expectations pass ### Step 6: Critique the Evals After grading, consider whether the evals themselves could be improved. Only surface suggestions when there's a clear gap. Good suggestions test meaningful outcomes — assertions that are hard to satisfy without actually doing the work correctly. Think about what makes an assertion *discriminating*: it passes when the skill genuinely succeeds and fails when it doesn't. Suggestions worth raising: - An assertion that passed but would also pass for a clearly wrong output (e.g., checking filename existence but not file content) - An important outcome you observed — good or bad — that no assertion covers at all - An assertion that can't actually be verified from the available outputs Keep the bar high. The goal is to flag things the eval author would say "good catch" about, not to nitpick every assertion. ### Step 7: Write Grading Results Save results to `{outputs_dir}/../grading.json` (sibling to outputs_dir). ## Grading Criteria **PASS when**: - The transcript or outputs clearly demonstrate the expectation is true - Specific evidence can be cited - The evidence reflects genuine substance, not just surface compliance (e.g., a file exists AND contains correct content, not just the right filename) **FAIL when**: - No evidence found for the expectation - Evidence contradicts the expectation - The expectation cannot be verified from available information - The evidence is superficial — the assertion is technically satisfied but the underlying task outcome is wrong or incomplete - The output appears to meet the assertion by coincidence rather than by actually doing the work **When uncertain**: The burden of proof to pass is on the expectation. ### Step 8: Read Executor Metrics and Timing 1. If `{outputs_dir}/metrics.json` exists, read it and include in grading output 2. If `{outputs_dir}/../timing.json` exists, read it and include timing data ## Output Format Write a JSON file with this structure: ```json { "expectations": [ { "text": "The output includes the name 'John Smith'", "passed": true, "evidence": "Found in transcript Step 3: 'Extracted names: John Smith, Sarah Johnson'" }, { "text": "The spreadsheet has a SUM formula in cell B10", "passed": false, "evidence": "No spreadsheet was created. The output was a text file." }, { "text": "The assistant used the skill's OCR script", "passed": true, "evidence": "Transcript Step 2 shows: 'Tool: Bash - python ocr_script.py image.png'" } ], "summary": { "passed": 2, "failed": 1, "total": 3, "pass_rate": 0.67 }, "execution_metrics": { "tool_calls": { "Read": 5, "Write": 2, "Bash": 8 }, "total_tool_calls": 15, "total_steps": 6, "errors_encountered": 0, "output_chars": 12450, "transcript_chars": 3200 }, "timing": { "executor_duration_seconds": 165.0, "grader_duration_seconds": 26.0, "total_duration_seconds": 191.0 }, "claims": [ { "claim": "The form has 12 fillable fields", "type": "factual", "verified": true, "evidence": "Counted 12 fields in field_info.json" }, { "claim": "All required fields were populated", "type": "quality", "verified": false, "evidence": "Reference section was left blank despite data being available" } ], "user_notes_summary": { "uncertainties": ["Used 2023 data, may be stale"], "needs_review": [], "workarounds": ["Fell back to text overlay for non-fillable fields"] }, "eval_feedback": { "suggestions": [ { "assertion": "The output includes the name 'John Smith'", "reason": "A hallucinated document that mentions the name would also pass — consider checking it appears as the primary contact with matching phone and email from the input" }, { "reason": "No assertion checks whether the extracted phone numbers match the input — I observed incorrect numbers in the output that went uncaught" } ], "overall": "Assertions check presence but not correctness. Consider adding content verification." } } ``` ## Field Descriptions - **expectations**: Array of graded expectations - **text**: The original expectation text - **passed**: Boolean - true if expectation passes - **evidence**: Specific quote or description supporting the verdict - **summary**: Aggregate statistics - **passed**: Count of passed expectations - **failed**: Count of failed expectations - **total**: Total expectations evaluated - **pass_rate**: Fraction passed (0.0 to 1.0) - **execution_metrics**: Copied from executor's metrics.json (if available) - **output_chars**: Total character count of output files (proxy for tokens) - **transcript_chars**: Character count of transcript - **timing**: Wall clock timing from timing.json (if available) - **executor_duration_seconds**: Time spent in executor subagent - **total_duration_seconds**: Total elapsed time for the run - **claims**: Extracted and verified claims from the output - **claim**: The statement being verified - **type**: "factual", "process", or "quality" - **verified**: Boolean - whether the claim holds - **evidence**: Supporting or contradicting evidence - **user_notes_summary**: Issues flagged by the executor - **uncertainties**: Things the executor wasn't sure about - **needs_review**: Items requiring human attention - **workarounds**: Places where the skill didn't work as expected - **eval_feedback**: Improvement suggestions for the evals (only when warranted) - **suggestions**: List of concrete suggestions, each with a `reason` and optionally an `assertion` it relates to - **overall**: Brief assessment — can be "No suggestions, evals look solid" if nothing to flag ## Guidelines - **Be objective**: Base verdicts on evidence, not assumptions - **Be specific**: Quote the exact text that supports your verdict - **Be thorough**: Check both transcript and output files - **Be consistent**: Apply the same standard to each expectation - **Explain failures**: Make it clear why evidence was insufficient - **No partial credit**: Each expectation is pass or fail, not partial FILE:assets/eval_review.html Eval Set Review - __SKILL_NAME_PLACEHOLDER__

Eval Set Review: __SKILL_NAME_PLACEHOLDER__

Current description: __SKILL_DESCRIPTION_PLACEHOLDER__

Query	Should Trigger	Actions

FILE:eval-viewer/generate_review.py #!/usr/bin/env python3 """Generate and serve a review page for eval results. Reads the workspace directory, discovers runs (directories with outputs/), embeds all output data into a self-contained HTML page, and serves it via a tiny HTTP server. Feedback auto-saves to feedback.json in the workspace. Usage: python generate_review.py [--port PORT] [--skill-name NAME] python generate_review.py --previous-feedback /path/to/old/feedback.json No dependencies beyond the Python stdlib are required. """ import argparse import base64 import json import mimetypes import os import re import signal import subprocess import sys import time import webbrowser from functools import partial from http.server import HTTPServer, BaseHTTPRequestHandler from pathlib import Path # Files to exclude from output listings METADATA_FILES = {"transcript.md", "user_notes.md", "metrics.json"} # Extensions we render as inline text TEXT_EXTENSIONS = { ".txt", ".md", ".json", ".csv", ".py", ".js", ".ts", ".tsx", ".jsx", ".yaml", ".yml", ".xml", ".html", ".css", ".sh", ".rb", ".go", ".rs", ".java", ".c", ".cpp", ".h", ".hpp", ".sql", ".r", ".toml", } # Extensions we render as inline images IMAGE_EXTENSIONS = {".png", ".jpg", ".jpeg", ".gif", ".svg", ".webp"} # MIME type overrides for common types MIME_OVERRIDES = { ".svg": "image/svg+xml", ".xlsx": "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet", ".docx": "application/vnd.openxmlformats-officedocument.wordprocessingml.document", ".pptx": "application/vnd.openxmlformats-officedocument.presentationml.presentation", } def get_mime_type(path: Path) -> str: ext = path.suffix.lower() if ext in MIME_OVERRIDES: return MIME_OVERRIDES[ext] mime, _ = mimetypes.guess_type(str(path)) return mime or "application/octet-stream" def find_runs(workspace: Path) -> list[dict]: """Recursively find directories that contain an outputs/ subdirectory.""" runs: list[dict] = [] _find_runs_recursive(workspace, workspace, runs) runs.sort(key=lambda r: (r.get("eval_id", float("inf")), r["id"])) return runs def _find_runs_recursive(root: Path, current: Path, runs: list[dict]) -> None: if not current.is_dir(): return outputs_dir = current / "outputs" if outputs_dir.is_dir(): run = build_run(root, current) if run: runs.append(run) return skip = {"node_modules", ".git", "__pycache__", "skill", "inputs"} for child in sorted(current.iterdir()): if child.is_dir() and child.name not in skip: _find_runs_recursive(root, child, runs) def build_run(root: Path, run_dir: Path) -> dict | None: """Build a run dict with prompt, outputs, and grading data.""" prompt = "" eval_id = None # Try eval_metadata.json for candidate in [run_dir / "eval_metadata.json", run_dir.parent / "eval_metadata.json"]: if candidate.exists(): try: metadata = json.loads(candidate.read_text()) prompt = metadata.get("prompt", "") eval_id = metadata.get("eval_id") except (json.JSONDecodeError, OSError): pass if prompt: break # Fall back to transcript.md if not prompt: for candidate in [run_dir / "transcript.md", run_dir / "outputs" / "transcript.md"]: if candidate.exists(): try: text = candidate.read_text() match = re.search(r"## Eval Prompt\n\n([\s\S]*?)(?=\n##|$)", text) if match: prompt = match.group(1).strip() except OSError: pass if prompt: break if not prompt: prompt = "(No prompt found)" run_id = str(run_dir.relative_to(root)).replace("/", "-").replace("\\", "-") # Collect output files outputs_dir = run_dir / "outputs" output_files: list[dict] = [] if outputs_dir.is_dir(): for f in sorted(outputs_dir.iterdir()): if f.is_file() and f.name not in METADATA_FILES: output_files.append(embed_file(f)) # Load grading if present grading = None for candidate in [run_dir / "grading.json", run_dir.parent / "grading.json"]: if candidate.exists(): try: grading = json.loads(candidate.read_text()) except (json.JSONDecodeError, OSError): pass if grading: break return { "id": run_id, "prompt": prompt, "eval_id": eval_id, "outputs": output_files, "grading": grading, } def embed_file(path: Path) -> dict: """Read a file and return an embedded representation.""" ext = path.suffix.lower() mime = get_mime_type(path) if ext in TEXT_EXTENSIONS: try: content = path.read_text(errors="replace") except OSError: content = "(Error reading file)" return { "name": path.name, "type": "text", "content": content, } elif ext in IMAGE_EXTENSIONS: try: raw = path.read_bytes() b64 = base64.b64encode(raw).decode("ascii") except OSError: return {"name": path.name, "type": "error", "content": "(Error reading file)"} return { "name": path.name, "type": "image", "mime": mime, "data_uri": f"data:{mime};base64,{b64}", } elif ext == ".pdf": try: raw = path.read_bytes() b64 = base64.b64encode(raw).decode("ascii") except OSError: return {"name": path.name, "type": "error", "content": "(Error reading file)"} return { "name": path.name, "type": "pdf", "data_uri": f"data:{mime};base64,{b64}", } elif ext == ".xlsx": try: raw = path.read_bytes() b64 = base64.b64encode(raw).decode("ascii") except OSError: return {"name": path.name, "type": "error", "content": "(Error reading file)"} return { "name": path.name, "type": "xlsx", "data_b64": b64, } else: # Binary / unknown — base64 download link try: raw = path.read_bytes() b64 = base64.b64encode(raw).decode("ascii") except OSError: return {"name": path.name, "type": "error", "content": "(Error reading file)"} return { "name": path.name, "type": "binary", "mime": mime, "data_uri": f"data:{mime};base64,{b64}", } def load_previous_iteration(workspace: Path) -> dict[str, dict]: """Load previous iteration's feedback and outputs. Returns a map of run_id -> {"feedback": str, "outputs": list[dict]}. """ result: dict[str, dict] = {} # Load feedback feedback_map: dict[str, str] = {} feedback_path = workspace / "feedback.json" if feedback_path.exists(): try: data = json.loads(feedback_path.read_text()) feedback_map = { r["run_id"]: r["feedback"] for r in data.get("reviews", []) if r.get("feedback", "").strip() } except (json.JSONDecodeError, OSError, KeyError): pass # Load runs (to get outputs) prev_runs = find_runs(workspace) for run in prev_runs: result[run["id"]] = { "feedback": feedback_map.get(run["id"], ""), "outputs": run.get("outputs", []), } # Also add feedback for run_ids that had feedback but no matching run for run_id, fb in feedback_map.items(): if run_id not in result: result[run_id] = {"feedback": fb, "outputs": []} return result def generate_html( runs: list[dict], skill_name: str, previous: dict[str, dict] | None = None, benchmark: dict | None = None, ) -> str: """Generate the complete standalone HTML page with embedded data.""" template_path = Path(__file__).parent / "viewer.html" template = template_path.read_text() # Build previous_feedback and previous_outputs maps for the template previous_feedback: dict[str, str] = {} previous_outputs: dict[str, list[dict]] = {} if previous: for run_id, data in previous.items(): if data.get("feedback"): previous_feedback[run_id] = data["feedback"] if data.get("outputs"): previous_outputs[run_id] = data["outputs"] embedded = { "skill_name": skill_name, "runs": runs, "previous_feedback": previous_feedback, "previous_outputs": previous_outputs, } if benchmark: embedded["benchmark"] = benchmark data_json = json.dumps(embedded) return template.replace("/*__EMBEDDED_DATA__*/", f"const EMBEDDED_DATA = {data_json};") # --------------------------------------------------------------------------- # HTTP server (stdlib only, zero dependencies) # --------------------------------------------------------------------------- def _kill_port(port: int) -> None: """Kill any process listening on the given port.""" try: result = subprocess.run( ["lsof", "-ti", f":{port}"], capture_output=True, text=True, timeout=5, ) for pid_str in result.stdout.strip().split("\n"): if pid_str.strip(): try: os.kill(int(pid_str.strip()), signal.SIGTERM) except (ProcessLookupError, ValueError): pass if result.stdout.strip(): time.sleep(0.5) except subprocess.TimeoutExpired: pass except FileNotFoundError: print("Note: lsof not found, cannot check if port is in use", file=sys.stderr) class ReviewHandler(BaseHTTPRequestHandler): """Serves the review HTML and handles feedback saves. Regenerates the HTML on each page load so that refreshing the browser picks up new eval outputs without restarting the server. """ def __init__( self, workspace: Path, skill_name: str, feedback_path: Path, previous: dict[str, dict], benchmark_path: Path | None, *args, **kwargs, ): self.workspace = workspace self.skill_name = skill_name self.feedback_path = feedback_path self.previous = previous self.benchmark_path = benchmark_path super().__init__(*args, **kwargs) def do_GET(self) -> None: if self.path == "/" or self.path == "/index.html": # Regenerate HTML on each request (re-scans workspace for new outputs) runs = find_runs(self.workspace) benchmark = None if self.benchmark_path and self.benchmark_path.exists(): try: benchmark = json.loads(self.benchmark_path.read_text()) except (json.JSONDecodeError, OSError): pass html = generate_html(runs, self.skill_name, self.previous, benchmark) content = html.encode("utf-8") self.send_response(200) self.send_header("Content-Type", "text/html; charset=utf-8") self.send_header("Content-Length", str(len(content))) self.end_headers() self.wfile.write(content) elif self.path == "/api/feedback": data = b"{}" if self.feedback_path.exists(): data = self.feedback_path.read_bytes() self.send_response(200) self.send_header("Content-Type", "application/json") self.send_header("Content-Length", str(len(data))) self.end_headers() self.wfile.write(data) else: self.send_error(404) def do_POST(self) -> None: if self.path == "/api/feedback": length = int(self.headers.get("Content-Length", 0)) body = self.rfile.read(length) try: data = json.loads(body) if not isinstance(data, dict) or "reviews" not in data: raise ValueError("Expected JSON object with 'reviews' key") self.feedback_path.write_text(json.dumps(data, indent=2) + "\n") resp = b'{"ok":true}' self.send_response(200) except (json.JSONDecodeError, OSError, ValueError) as e: resp = json.dumps({"error": str(e)}).encode() self.send_response(500) self.send_header("Content-Type", "application/json") self.send_header("Content-Length", str(len(resp))) self.end_headers() self.wfile.write(resp) else: self.send_error(404) def log_message(self, format: str, *args: object) -> None: # Suppress request logging to keep terminal clean pass def main() -> None: parser = argparse.ArgumentParser(description="Generate and serve eval review") parser.add_argument("workspace", type=Path, help="Path to workspace directory") parser.add_argument("--port", "-p", type=int, default=3117, help="Server port (default: 3117)") parser.add_argument("--skill-name", "-n", type=str, default=None, help="Skill name for header") parser.add_argument( "--previous-workspace", type=Path, default=None, help="Path to previous iteration's workspace (shows old outputs and feedback as context)", ) parser.add_argument( "--benchmark", type=Path, default=None, help="Path to benchmark.json to show in the Benchmark tab", ) parser.add_argument( "--static", "-s", type=Path, default=None, help="Write standalone HTML to this path instead of starting a server", ) args = parser.parse_args() workspace = args.workspace.resolve() if not workspace.is_dir(): print(f"Error: {workspace} is not a directory", file=sys.stderr) sys.exit(1) runs = find_runs(workspace) if not runs: print(f"No runs found in {workspace}", file=sys.stderr) sys.exit(1) skill_name = args.skill_name or workspace.name.replace("-workspace", "") feedback_path = workspace / "feedback.json" previous: dict[str, dict] = {} if args.previous_workspace: previous = load_previous_iteration(args.previous_workspace.resolve()) benchmark_path = args.benchmark.resolve() if args.benchmark else None benchmark = None if benchmark_path and benchmark_path.exists(): try: benchmark = json.loads(benchmark_path.read_text()) except (json.JSONDecodeError, OSError): pass if args.static: html = generate_html(runs, skill_name, previous, benchmark) args.static.parent.mkdir(parents=True, exist_ok=True) args.static.write_text(html) print(f"\n Static viewer written to: {args.static}\n") sys.exit(0) # Kill any existing process on the target port port = args.port _kill_port(port) handler = partial(ReviewHandler, workspace, skill_name, feedback_path, previous, benchmark_path) try: server = HTTPServer(("127.0.0.1", port), handler) except OSError: # Port still in use after kill attempt — find a free one server = HTTPServer(("127.0.0.1", 0), handler) port = server.server_address[1] url = f"http://localhost:{port}" print(f"\n Eval Viewer") print(f" ─────────────────────────────────") print(f" URL: {url}") print(f" Workspace: {workspace}") print(f" Feedback: {feedback_path}") if previous: print(f" Previous: {args.previous_workspace} ({len(previous)} runs)") if benchmark_path: print(f" Benchmark: {benchmark_path}") print(f"\n Press Ctrl+C to stop.\n") webbrowser.open(url) try: server.serve_forever() except KeyboardInterrupt: print("\nStopped.") server.server_close() if __name__ == "__main__": main() FILE:eval-viewer/viewer.html Eval Review

Prompt

Output

No output files found

Your Feedback

No benchmark data available. Run a benchmark to see quantitative results here.

Review Complete

Your feedback has been saved. Go back to your Claude Code session and tell Claude you're done reviewing.

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We also recommend that a file or class name and description of purpose be included on the same "printed page" as the copyright notice for easier identification within third-party archives. Copyright 2026 Anthropic, PBC. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. FILE:references/schemas.md # JSON Schemas This document defines the JSON schemas used by skill-creator. --- ## evals.json Defines the evals for a skill. Located at `evals/evals.json` within the skill directory. ```json { "skill_name": "example-skill", "evals": [ { "id": 1, "prompt": "User's example prompt", "expected_output": "Description of expected result", "files": ["evals/files/sample1.pdf"], "expectations": [ "The output includes X", "The skill used script Y" ] } ] } ``` **Fields:** - `skill_name`: Name matching the skill's frontmatter - `evals[].id`: Unique integer identifier - `evals[].prompt`: The task to execute - `evals[].expected_output`: Human-readable description of success - `evals[].files`: Optional list of input file paths (relative to skill root) - `evals[].expectations`: List of verifiable statements --- ## history.json Tracks version progression in Improve mode. Located at workspace root. ```json { "started_at": "2026-01-15T10:30:00Z", "skill_name": "pdf", "current_best": "v2", "iterations": [ { "version": "v0", "parent": null, "expectation_pass_rate": 0.65, "grading_result": "baseline", "is_current_best": false }, { "version": "v1", "parent": "v0", "expectation_pass_rate": 0.75, "grading_result": "won", "is_current_best": false }, { "version": "v2", "parent": "v1", "expectation_pass_rate": 0.85, "grading_result": "won", "is_current_best": true } ] } ``` **Fields:** - `started_at`: ISO timestamp of when improvement started - `skill_name`: Name of the skill being improved - `current_best`: Version identifier of the best performer - `iterations[].version`: Version identifier (v0, v1, ...) - `iterations[].parent`: Parent version this was derived from - `iterations[].expectation_pass_rate`: Pass rate from grading - `iterations[].grading_result`: "baseline", "won", "lost", or "tie" - `iterations[].is_current_best`: Whether this is the current best version --- ## grading.json Output from the grader agent. Located at `/grading.json`. ```json { "expectations": [ { "text": "The output includes the name 'John Smith'", "passed": true, "evidence": "Found in transcript Step 3: 'Extracted names: John Smith, Sarah Johnson'" }, { "text": "The spreadsheet has a SUM formula in cell B10", "passed": false, "evidence": "No spreadsheet was created. The output was a text file." } ], "summary": { "passed": 2, "failed": 1, "total": 3, "pass_rate": 0.67 }, "execution_metrics": { "tool_calls": { "Read": 5, "Write": 2, "Bash": 8 }, "total_tool_calls": 15, "total_steps": 6, "errors_encountered": 0, "output_chars": 12450, "transcript_chars": 3200 }, "timing": { "executor_duration_seconds": 165.0, "grader_duration_seconds": 26.0, "total_duration_seconds": 191.0 }, "claims": [ { "claim": "The form has 12 fillable fields", "type": "factual", "verified": true, "evidence": "Counted 12 fields in field_info.json" } ], "user_notes_summary": { "uncertainties": ["Used 2023 data, may be stale"], "needs_review": [], "workarounds": ["Fell back to text overlay for non-fillable fields"] }, "eval_feedback": { "suggestions": [ { "assertion": "The output includes the name 'John Smith'", "reason": "A hallucinated document that mentions the name would also pass" } ], "overall": "Assertions check presence but not correctness." } } ``` **Fields:** - `expectations[]`: Graded expectations with evidence - `summary`: Aggregate pass/fail counts - `execution_metrics`: Tool usage and output size (from executor's metrics.json) - `timing`: Wall clock timing (from timing.json) - `claims`: Extracted and verified claims from the output - `user_notes_summary`: Issues flagged by the executor - `eval_feedback`: (optional) Improvement suggestions for the evals, only present when the grader identifies issues worth raising --- ## metrics.json Output from the executor agent. Located at `/outputs/metrics.json`. ```json { "tool_calls": { "Read": 5, "Write": 2, "Bash": 8, "Edit": 1, "Glob": 2, "Grep": 0 }, "total_tool_calls": 18, "total_steps": 6, "files_created": ["filled_form.pdf", "field_values.json"], "errors_encountered": 0, "output_chars": 12450, "transcript_chars": 3200 } ``` **Fields:** - `tool_calls`: Count per tool type - `total_tool_calls`: Sum of all tool calls - `total_steps`: Number of major execution steps - `files_created`: List of output files created - `errors_encountered`: Number of errors during execution - `output_chars`: Total character count of output files - `transcript_chars`: Character count of transcript --- ## timing.json Wall clock timing for a run. Located at `/timing.json`. **How to capture:** When a subagent task completes, the task notification includes `total_tokens` and `duration_ms`. Save these immediately — they are not persisted anywhere else and cannot be recovered after the fact. ```json { "total_tokens": 84852, "duration_ms": 23332, "total_duration_seconds": 23.3, "executor_start": "2026-01-15T10:30:00Z", "executor_end": "2026-01-15T10:32:45Z", "executor_duration_seconds": 165.0, "grader_start": "2026-01-15T10:32:46Z", "grader_end": "2026-01-15T10:33:12Z", "grader_duration_seconds": 26.0 } ``` --- ## benchmark.json Output from Benchmark mode. Located at `benchmarks//benchmark.json`. ```json { "metadata": { "skill_name": "pdf", "skill_path": "/path/to/pdf", "executor_model": "claude-sonnet-4-20250514", "analyzer_model": "most-capable-model", "timestamp": "2026-01-15T10:30:00Z", "evals_run": [1, 2, 3], "runs_per_configuration": 3 }, "runs": [ { "eval_id": 1, "eval_name": "Ocean", "configuration": "with_skill", "run_number": 1, "result": { "pass_rate": 0.85, "passed": 6, "failed": 1, "total": 7, "time_seconds": 42.5, "tokens": 3800, "tool_calls": 18, "errors": 0 }, "expectations": [ {"text": "...", "passed": true, "evidence": "..."} ], "notes": [ "Used 2023 data, may be stale", "Fell back to text overlay for non-fillable fields" ] } ], "run_summary": { "with_skill": { "pass_rate": {"mean": 0.85, "stddev": 0.05, "min": 0.80, "max": 0.90}, "time_seconds": {"mean": 45.0, "stddev": 12.0, "min": 32.0, "max": 58.0}, "tokens": {"mean": 3800, "stddev": 400, "min": 3200, "max": 4100} }, "without_skill": { "pass_rate": {"mean": 0.35, "stddev": 0.08, "min": 0.28, "max": 0.45}, "time_seconds": {"mean": 32.0, "stddev": 8.0, "min": 24.0, "max": 42.0}, "tokens": {"mean": 2100, "stddev": 300, "min": 1800, "max": 2500} }, "delta": { "pass_rate": "+0.50", "time_seconds": "+13.0", "tokens": "+1700" } }, "notes": [ "Assertion 'Output is a PDF file' passes 100% in both configurations - may not differentiate skill value", "Eval 3 shows high variance (50% ± 40%) - may be flaky or model-dependent", "Without-skill runs consistently fail on table extraction expectations", "Skill adds 13s average execution time but improves pass rate by 50%" ] } ``` **Fields:** - `metadata`: Information about the benchmark run - `skill_name`: Name of the skill - `timestamp`: When the benchmark was run - `evals_run`: List of eval names or IDs - `runs_per_configuration`: Number of runs per config (e.g. 3) - `runs[]`: Individual run results - `eval_id`: Numeric eval identifier - `eval_name`: Human-readable eval name (used as section header in the viewer) - `configuration`: Must be `"with_skill"` or `"without_skill"` (the viewer uses this exact string for grouping and color coding) - `run_number`: Integer run number (1, 2, 3...) - `result`: Nested object with `pass_rate`, `passed`, `total`, `time_seconds`, `tokens`, `errors` - `run_summary`: Statistical aggregates per configuration - `with_skill` / `without_skill`: Each contains `pass_rate`, `time_seconds`, `tokens` objects with `mean` and `stddev` fields - `delta`: Difference strings like `"+0.50"`, `"+13.0"`, `"+1700"` - `notes`: Freeform observations from the analyzer **Important:** The viewer reads these field names exactly. Using `config` instead of `configuration`, or putting `pass_rate` at the top level of a run instead of nested under `result`, will cause the viewer to show empty/zero values. Always reference this schema when generating benchmark.json manually. --- ## comparison.json Output from blind comparator. Located at `/comparison-N.json`. ```json { "winner": "A", "reasoning": "Output A provides a complete solution with proper formatting and all required fields. Output B is missing the date field and has formatting inconsistencies.", "rubric": { "A": { "content": { "correctness": 5, "completeness": 5, "accuracy": 4 }, "structure": { "organization": 4, "formatting": 5, "usability": 4 }, "content_score": 4.7, "structure_score": 4.3, "overall_score": 9.0 }, "B": { "content": { "correctness": 3, "completeness": 2, "accuracy": 3 }, "structure": { "organization": 3, "formatting": 2, "usability": 3 }, "content_score": 2.7, "structure_score": 2.7, "overall_score": 5.4 } }, "output_quality": { "A": { "score": 9, "strengths": ["Complete solution", "Well-formatted", "All fields present"], "weaknesses": ["Minor style inconsistency in header"] }, "B": { "score": 5, "strengths": ["Readable output", "Correct basic structure"], "weaknesses": ["Missing date field", "Formatting inconsistencies", "Partial data extraction"] } }, "expectation_results": { "A": { "passed": 4, "total": 5, "pass_rate": 0.80, "details": [ {"text": "Output includes name", "passed": true} ] }, "B": { "passed": 3, "total": 5, "pass_rate": 0.60, "details": [ {"text": "Output includes name", "passed": true} ] } } } ``` --- ## analysis.json Output from post-hoc analyzer. Located at `/analysis.json`. ```json { "comparison_summary": { "winner": "A", "winner_skill": "path/to/winner/skill", "loser_skill": "path/to/loser/skill", "comparator_reasoning": "Brief summary of why comparator chose winner" }, "winner_strengths": [ "Clear step-by-step instructions for handling multi-page documents", "Included validation script that caught formatting errors" ], "loser_weaknesses": [ "Vague instruction 'process the document appropriately' led to inconsistent behavior", "No script for validation, agent had to improvise" ], "instruction_following": { "winner": { "score": 9, "issues": ["Minor: skipped optional logging step"] }, "loser": { "score": 6, "issues": [ "Did not use the skill's formatting template", "Invented own approach instead of following step 3" ] } }, "improvement_suggestions": [ { "priority": "high", "category": "instructions", "suggestion": "Replace 'process the document appropriately' with explicit steps", "expected_impact": "Would eliminate ambiguity that caused inconsistent behavior" } ], "transcript_insights": { "winner_execution_pattern": "Read skill -> Followed 5-step process -> Used validation script", "loser_execution_pattern": "Read skill -> Unclear on approach -> Tried 3 different methods" } } ``` FILE:scripts/aggregate_benchmark.py #!/usr/bin/env python3 """ Aggregate individual run results into benchmark summary statistics. Reads grading.json files from run directories and produces: - run_summary with mean, stddev, min, max for each metric - delta between with_skill and without_skill configurations Usage: python aggregate_benchmark.py Example: python aggregate_benchmark.py benchmarks/2026-01-15T10-30-00/ The script supports two directory layouts: Workspace layout (from skill-creator iterations): / └── eval-N/ ├── with_skill/ │ ├── run-1/grading.json │ └── run-2/grading.json └── without_skill/ ├── run-1/grading.json └── run-2/grading.json Legacy layout (with runs/ subdirectory): / └── runs/ └── eval-N/ ├── with_skill/ │ └── run-1/grading.json └── without_skill/ └── run-1/grading.json """ import argparse import json import math import sys from datetime import datetime, timezone from pathlib import Path def calculate_stats(values: list[float]) -> dict: """Calculate mean, stddev, min, max for a list of values.""" if not values: return {"mean": 0.0, "stddev": 0.0, "min": 0.0, "max": 0.0} n = len(values) mean = sum(values) / n if n > 1: variance = sum((x - mean) ** 2 for x in values) / (n - 1) stddev = math.sqrt(variance) else: stddev = 0.0 return { "mean": round(mean, 4), "stddev": round(stddev, 4), "min": round(min(values), 4), "max": round(max(values), 4) } def load_run_results(benchmark_dir: Path) -> dict: """ Load all run results from a benchmark directory. Returns dict keyed by config name (e.g. "with_skill"/"without_skill", or "new_skill"/"old_skill"), each containing a list of run results. """ # Support both layouts: eval dirs directly under benchmark_dir, or under runs/ runs_dir = benchmark_dir / "runs" if runs_dir.exists(): search_dir = runs_dir elif list(benchmark_dir.glob("eval-*")): search_dir = benchmark_dir else: print(f"No eval directories found in {benchmark_dir} or {benchmark_dir / 'runs'}") return {} results: dict[str, list] = {} for eval_idx, eval_dir in enumerate(sorted(search_dir.glob("eval-*"))): metadata_path = eval_dir / "eval_metadata.json" if metadata_path.exists(): try: with open(metadata_path) as mf: eval_id = json.load(mf).get("eval_id", eval_idx) except (json.JSONDecodeError, OSError): eval_id = eval_idx else: try: eval_id = int(eval_dir.name.split("-")[1]) except ValueError: eval_id = eval_idx # Discover config directories dynamically rather than hardcoding names for config_dir in sorted(eval_dir.iterdir()): if not config_dir.is_dir(): continue # Skip non-config directories (inputs, outputs, etc.) if not list(config_dir.glob("run-*")): continue config = config_dir.name if config not in results: results[config] = [] for run_dir in sorted(config_dir.glob("run-*")): run_number = int(run_dir.name.split("-")[1]) grading_file = run_dir / "grading.json" if not grading_file.exists(): print(f"Warning: grading.json not found in {run_dir}") continue try: with open(grading_file) as f: grading = json.load(f) except json.JSONDecodeError as e: print(f"Warning: Invalid JSON in {grading_file}: {e}") continue # Extract metrics result = { "eval_id": eval_id, "run_number": run_number, "pass_rate": grading.get("summary", {}).get("pass_rate", 0.0), "passed": grading.get("summary", {}).get("passed", 0), "failed": grading.get("summary", {}).get("failed", 0), "total": grading.get("summary", {}).get("total", 0), } # Extract timing — check grading.json first, then sibling timing.json timing = grading.get("timing", {}) result["time_seconds"] = timing.get("total_duration_seconds", 0.0) timing_file = run_dir / "timing.json" if result["time_seconds"] == 0.0 and timing_file.exists(): try: with open(timing_file) as tf: timing_data = json.load(tf) result["time_seconds"] = timing_data.get("total_duration_seconds", 0.0) result["tokens"] = timing_data.get("total_tokens", 0) except json.JSONDecodeError: pass # Extract metrics if available metrics = grading.get("execution_metrics", {}) result["tool_calls"] = metrics.get("total_tool_calls", 0) if not result.get("tokens"): result["tokens"] = metrics.get("output_chars", 0) result["errors"] = metrics.get("errors_encountered", 0) # Extract expectations — viewer requires fields: text, passed, evidence raw_expectations = grading.get("expectations", []) for exp in raw_expectations: if "text" not in exp or "passed" not in exp: print(f"Warning: expectation in {grading_file} missing required fields (text, passed, evidence): {exp}") result["expectations"] = raw_expectations # Extract notes from user_notes_summary notes_summary = grading.get("user_notes_summary", {}) notes = [] notes.extend(notes_summary.get("uncertainties", [])) notes.extend(notes_summary.get("needs_review", [])) notes.extend(notes_summary.get("workarounds", [])) result["notes"] = notes results[config].append(result) return results def aggregate_results(results: dict) -> dict: """ Aggregate run results into summary statistics. Returns run_summary with stats for each configuration and delta. """ run_summary = {} configs = list(results.keys()) for config in configs: runs = results.get(config, []) if not runs: run_summary[config] = { "pass_rate": {"mean": 0.0, "stddev": 0.0, "min": 0.0, "max": 0.0}, "time_seconds": {"mean": 0.0, "stddev": 0.0, "min": 0.0, "max": 0.0}, "tokens": {"mean": 0, "stddev": 0, "min": 0, "max": 0} } continue pass_rates = [r["pass_rate"] for r in runs] times = [r["time_seconds"] for r in runs] tokens = [r.get("tokens", 0) for r in runs] run_summary[config] = { "pass_rate": calculate_stats(pass_rates), "time_seconds": calculate_stats(times), "tokens": calculate_stats(tokens) } # Calculate delta between the first two configs (if two exist) if len(configs) >= 2: primary = run_summary.get(configs[0], {}) baseline = run_summary.get(configs[1], {}) else: primary = run_summary.get(configs[0], {}) if configs else {} baseline = {} delta_pass_rate = primary.get("pass_rate", {}).get("mean", 0) - baseline.get("pass_rate", {}).get("mean", 0) delta_time = primary.get("time_seconds", {}).get("mean", 0) - baseline.get("time_seconds", {}).get("mean", 0) delta_tokens = primary.get("tokens", {}).get("mean", 0) - baseline.get("tokens", {}).get("mean", 0) run_summary["delta"] = { "pass_rate": f"{delta_pass_rate:+.2f}", "time_seconds": f"{delta_time:+.1f}", "tokens": f"{delta_tokens:+.0f}" } return run_summary def generate_benchmark(benchmark_dir: Path, skill_name: str = "", skill_path: str = "") -> dict: """ Generate complete benchmark.json from run results. """ results = load_run_results(benchmark_dir) run_summary = aggregate_results(results) # Build runs array for benchmark.json runs = [] for config in results: for result in results[config]: runs.append({ "eval_id": result["eval_id"], "configuration": config, "run_number": result["run_number"], "result": { "pass_rate": result["pass_rate"], "passed": result["passed"], "failed": result["failed"], "total": result["total"], "time_seconds": result["time_seconds"], "tokens": result.get("tokens", 0), "tool_calls": result.get("tool_calls", 0), "errors": result.get("errors", 0) }, "expectations": result["expectations"], "notes": result["notes"] }) # Determine eval IDs from results eval_ids = sorted(set( r["eval_id"] for config in results.values() for r in config )) benchmark = { "metadata": { "skill_name": skill_name or "", "skill_path": skill_path or "", "executor_model": "", "analyzer_model": "", "timestamp": datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ"), "evals_run": eval_ids, "runs_per_configuration": 3 }, "runs": runs, "run_summary": run_summary, "notes": [] # To be filled by analyzer } return benchmark def generate_markdown(benchmark: dict) -> str: """Generate human-readable benchmark.md from benchmark data.""" metadata = benchmark["metadata"] run_summary = benchmark["run_summary"] # Determine config names (excluding "delta") configs = [k for k in run_summary if k != "delta"] config_a = configs[0] if len(configs) >= 1 else "config_a" config_b = configs[1] if len(configs) >= 2 else "config_b" label_a = config_a.replace("_", " ").title() label_b = config_b.replace("_", " ").title() lines = [ f"# Skill Benchmark: {metadata['skill_name']}", "", f"**Model**: {metadata['executor_model']}", f"**Date**: {metadata['timestamp']}", f"**Evals**: {', '.join(map(str, metadata['evals_run']))} ({metadata['runs_per_configuration']} runs each per configuration)", "", "## Summary", "", f"| Metric | {label_a} | {label_b} | Delta |", "|--------|------------|---------------|-------|", ] a_summary = run_summary.get(config_a, {}) b_summary = run_summary.get(config_b, {}) delta = run_summary.get("delta", {}) # Format pass rate a_pr = a_summary.get("pass_rate", {}) b_pr = b_summary.get("pass_rate", {}) lines.append(f"| Pass Rate | {a_pr.get('mean', 0)*100:.0f}% ± {a_pr.get('stddev', 0)*100:.0f}% | {b_pr.get('mean', 0)*100:.0f}% ± {b_pr.get('stddev', 0)*100:.0f}% | {delta.get('pass_rate', '—')} |") # Format time a_time = a_summary.get("time_seconds", {}) b_time = b_summary.get("time_seconds", {}) lines.append(f"| Time | {a_time.get('mean', 0):.1f}s ± {a_time.get('stddev', 0):.1f}s | {b_time.get('mean', 0):.1f}s ± {b_time.get('stddev', 0):.1f}s | {delta.get('time_seconds', '—')}s |") # Format tokens a_tokens = a_summary.get("tokens", {}) b_tokens = b_summary.get("tokens", {}) lines.append(f"| Tokens | {a_tokens.get('mean', 0):.0f} ± {a_tokens.get('stddev', 0):.0f} | {b_tokens.get('mean', 0):.0f} ± {b_tokens.get('stddev', 0):.0f} | {delta.get('tokens', '—')} |") # Notes section if benchmark.get("notes"): lines.extend([ "", "## Notes", "" ]) for note in benchmark["notes"]: lines.append(f"- {note}") return "\n".join(lines) def main(): parser = argparse.ArgumentParser( description="Aggregate benchmark run results into summary statistics" ) parser.add_argument( "benchmark_dir", type=Path, help="Path to the benchmark directory" ) parser.add_argument( "--skill-name", default="", help="Name of the skill being benchmarked" ) parser.add_argument( "--skill-path", default="", help="Path to the skill being benchmarked" ) parser.add_argument( "--output", "-o", type=Path, help="Output path for benchmark.json (default: /benchmark.json)" ) args = parser.parse_args() if not args.benchmark_dir.exists(): print(f"Directory not found: {args.benchmark_dir}") sys.exit(1) # Generate benchmark benchmark = generate_benchmark(args.benchmark_dir, args.skill_name, args.skill_path) # Determine output paths output_json = args.output or (args.benchmark_dir / "benchmark.json") output_md = output_json.with_suffix(".md") # Write benchmark.json with open(output_json, "w") as f: json.dump(benchmark, f, indent=2) print(f"Generated: {output_json}") # Write benchmark.md markdown = generate_markdown(benchmark) with open(output_md, "w") as f: f.write(markdown) print(f"Generated: {output_md}") # Print summary run_summary = benchmark["run_summary"] configs = [k for k in run_summary if k != "delta"] delta = run_summary.get("delta", {}) print(f"\nSummary:") for config in configs: pr = run_summary[config]["pass_rate"]["mean"] label = config.replace("_", " ").title() print(f" {label}: {pr*100:.1f}% pass rate") print(f" Delta: {delta.get('pass_rate', '—')}") if __name__ == "__main__": main() FILE:scripts/generate_report.py #!/usr/bin/env python3 """Generate an HTML report from run_loop.py output. Takes the JSON output from run_loop.py and generates a visual HTML report showing each description attempt with check/x for each test case. Distinguishes between train and test queries. """ import argparse import html import json import sys from pathlib import Path def generate_html(data: dict, auto_refresh: bool = False, skill_name: str = "") -> str: """Generate HTML report from loop output data. If auto_refresh is True, adds a meta refresh tag.""" history = data.get("history", []) holdout = data.get("holdout", 0) title_prefix = html.escape(skill_name + " \u2014 ") if skill_name else "" # Get all unique queries from train and test sets, with should_trigger info train_queries: list[dict] = [] test_queries: list[dict] = [] if history: for r in history[0].get("train_results", history[0].get("results", [])): train_queries.append({"query": r["query"], "should_trigger": r.get("should_trigger", True)}) if history[0].get("test_results"): for r in history[0].get("test_results", []): test_queries.append({"query": r["query"], "should_trigger": r.get("should_trigger", True)}) refresh_tag = ' \n' if auto_refresh else "" html_parts = [""" """ + refresh_tag + """ """ + title_prefix + """Skill Description Optimization

""" + title_prefix + """Skill Description Optimization

Optimizing your skill's description. This page updates automatically as Claude tests different versions of your skill's description. Each row is an iteration — a new description attempt. The columns show test queries: green checkmarks mean the skill triggered correctly (or correctly didn't trigger), red crosses mean it got it wrong. The "Train" score shows performance on queries used to improve the description; the "Test" score shows performance on held-out queries the optimizer hasn't seen. When it's done, Claude will apply the best-performing description to your skill.

"""] # Summary section best_test_score = data.get('best_test_score') best_train_score = data.get('best_train_score') html_parts.append(f"""

Original: {html.escape(data.get('original_description', 'N/A'))}

Best: {html.escape(data.get('best_description', 'N/A'))}

Best Score: {data.get('best_score', 'N/A')} {'(test)' if best_test_score else '(train)'}

Iterations: {data.get('iterations_run', 0)} | Train: {data.get('train_size', '?')} | Test: {data.get('test_size', '?')}

""") # Legend html_parts.append("""

Query columns: Should trigger Should NOT trigger Train Test

""") # Table header html_parts.append("""

""") # Add column headers for train queries for qinfo in train_queries: polarity = "positive-col" if qinfo["should_trigger"] else "negative-col" html_parts.append(f' \n') # Add column headers for test queries (different color) for qinfo in test_queries: polarity = "positive-col" if qinfo["should_trigger"] else "negative-col" html_parts.append(f' \n') html_parts.append(""" """) # Find best iteration for highlighting if test_queries: best_iter = max(history, key=lambda h: h.get("test_passed") or 0).get("iteration") else: best_iter = max(history, key=lambda h: h.get("train_passed", h.get("passed", 0))).get("iteration") # Add rows for each iteration for h in history: iteration = h.get("iteration", "?") train_passed = h.get("train_passed", h.get("passed", 0)) train_total = h.get("train_total", h.get("total", 0)) test_passed = h.get("test_passed") test_total = h.get("test_total") description = h.get("description", "") train_results = h.get("train_results", h.get("results", [])) test_results = h.get("test_results", []) # Create lookups for results by query train_by_query = {r["query"]: r for r in train_results} test_by_query = {r["query"]: r for r in test_results} if test_results else {} # Compute aggregate correct/total runs across all retries def aggregate_runs(results: list[dict]) -> tuple[int, int]: correct = 0 total = 0 for r in results: runs = r.get("runs", 0) triggers = r.get("triggers", 0) total += runs if r.get("should_trigger", True): correct += triggers else: correct += runs - triggers return correct, total train_correct, train_runs = aggregate_runs(train_results) test_correct, test_runs = aggregate_runs(test_results) # Determine score classes def score_class(correct: int, total: int) -> str: if total > 0: ratio = correct / total if ratio >= 0.8: return "score-good" elif ratio >= 0.5: return "score-ok" return "score-bad" train_class = score_class(train_correct, train_runs) test_class = score_class(test_correct, test_runs) row_class = "best-row" if iteration == best_iter else "" html_parts.append(f""" """) # Add result for each train query for qinfo in train_queries: r = train_by_query.get(qinfo["query"], {}) did_pass = r.get("pass", False) triggers = r.get("triggers", 0) runs = r.get("runs", 0) icon = "✓" if did_pass else "✗" css_class = "pass" if did_pass else "fail" html_parts.append(f' \n') # Add result for each test query (with different background) for qinfo in test_queries: r = test_by_query.get(qinfo["query"], {}) did_pass = r.get("pass", False) triggers = r.get("triggers", 0) runs = r.get("runs", 0) icon = "✓" if did_pass else "✗" css_class = "pass" if did_pass else "fail" html_parts.append(f' \n') html_parts.append(" \n") html_parts.append("""

Iter	Train	Test	Description	{html.escape(qinfo["query"])}	{html.escape(qinfo["query"])}
{iteration}	{train_correct}/{train_runs}	{test_correct}/{test_runs}	{html.escape(description)}	{icon}{triggers}/{runs}	{icon}{triggers}/{runs}

""") html_parts.append(""" """) return "".join(html_parts) def main(): parser = argparse.ArgumentParser(description="Generate HTML report from run_loop output") parser.add_argument("input", help="Path to JSON output from run_loop.py (or - for stdin)") parser.add_argument("-o", "--output", default=None, help="Output HTML file (default: stdout)") parser.add_argument("--skill-name", default="", help="Skill name to include in the report title") args = parser.parse_args() if args.input == "-": data = json.load(sys.stdin) else: data = json.loads(Path(args.input).read_text()) html_output = generate_html(data, skill_name=args.skill_name) if args.output: Path(args.output).write_text(html_output) print(f"Report written to {args.output}", file=sys.stderr) else: print(html_output) if __name__ == "__main__": main() FILE:scripts/improve_description.py #!/usr/bin/env python3 """Improve a skill description based on eval results. Takes eval results (from run_eval.py) and generates an improved description by calling `claude -p` as a subprocess (same auth pattern as run_eval.py — uses the session's Claude Code auth, no separate ANTHROPIC_API_KEY needed). """ import argparse import json import os import re import subprocess import sys from pathlib import Path from scripts.utils import parse_skill_md def _call_claude(prompt: str, model: str | None, timeout: int = 300) -> str: """Run `claude -p` with the prompt on stdin and return the text response. Prompt goes over stdin (not argv) because it embeds the full SKILL.md body and can easily exceed comfortable argv length. """ cmd = ["claude", "-p", "--output-format", "text"] if model: cmd.extend(["--model", model]) # Remove CLAUDECODE env var to allow nesting claude -p inside a # Claude Code session. The guard is for interactive terminal conflicts; # programmatic subprocess usage is safe. Same pattern as run_eval.py. env = {k: v for k, v in os.environ.items() if k != "CLAUDECODE"} result = subprocess.run( cmd, input=prompt, capture_output=True, text=True, env=env, timeout=timeout, ) if result.returncode != 0: raise RuntimeError( f"claude -p exited {result.returncode}\nstderr: {result.stderr}" ) return result.stdout def improve_description( skill_name: str, skill_content: str, current_description: str, eval_results: dict, history: list[dict], model: str, test_results: dict | None = None, log_dir: Path | None = None, iteration: int | None = None, ) -> str: """Call Claude to improve the description based on eval results.""" failed_triggers = [ r for r in eval_results["results"] if r["should_trigger"] and not r["pass"] ] false_triggers = [ r for r in eval_results["results"] if not r["should_trigger"] and not r["pass"] ] # Build scores summary train_score = f"{eval_results['summary']['passed']}/{eval_results['summary']['total']}" if test_results: test_score = f"{test_results['summary']['passed']}/{test_results['summary']['total']}" scores_summary = f"Train: {train_score}, Test: {test_score}" else: scores_summary = f"Train: {train_score}" prompt = f"""You are optimizing a skill description for a Claude Code skill called "{skill_name}". A "skill" is sort of like a prompt, but with progressive disclosure -- there's a title and description that Claude sees when deciding whether to use the skill, and then if it does use the skill, it reads the .md file which has lots more details and potentially links to other resources in the skill folder like helper files and scripts and additional documentation or examples. The description appears in Claude's "available_skills" list. When a user sends a query, Claude decides whether to invoke the skill based solely on the title and on this description. Your goal is to write a description that triggers for relevant queries, and doesn't trigger for irrelevant ones. Here's the current description: "{current_description}" Current scores ({scores_summary}): """ if failed_triggers: prompt += "FAILED TO TRIGGER (should have triggered but didn't):\n" for r in failed_triggers: prompt += f' - "{r["query"]}" (triggered {r["triggers"]}/{r["runs"]} times)\n' prompt += "\n" if false_triggers: prompt += "FALSE TRIGGERS (triggered but shouldn't have):\n" for r in false_triggers: prompt += f' - "{r["query"]}" (triggered {r["triggers"]}/{r["runs"]} times)\n' prompt += "\n" if history: prompt += "PREVIOUS ATTEMPTS (do NOT repeat these — try something structurally different):\n\n" for h in history: train_s = f"{h.get('train_passed', h.get('passed', 0))}/{h.get('train_total', h.get('total', 0))}" test_s = f"{h.get('test_passed', '?')}/{h.get('test_total', '?')}" if h.get('test_passed') is not None else None score_str = f"train={train_s}" + (f", test={test_s}" if test_s else "") prompt += f'\n' prompt += f'Description: "{h["description"]}"\n' if "results" in h: prompt += "Train results:\n" for r in h["results"]: status = "PASS" if r["pass"] else "FAIL" prompt += f' [{status}] "{r["query"][:80]}" (triggered {r["triggers"]}/{r["runs"]})\n' if h.get("note"): prompt += f'Note: {h["note"]}\n' prompt += "\n\n" prompt += f""" Skill content (for context on what the skill does): {skill_content} Based on the failures, write a new and improved description that is more likely to trigger correctly. When I say "based on the failures", it's a bit of a tricky line to walk because we don't want to overfit to the specific cases you're seeing. So what I DON'T want you to do is produce an ever-expanding list of specific queries that this skill should or shouldn't trigger for. Instead, try to generalize from the failures to broader categories of user intent and situations where this skill would be useful or not useful. The reason for this is twofold: 1. Avoid overfitting 2. The list might get loooong and it's injected into ALL queries and there might be a lot of skills, so we don't want to blow too much space on any given description. Concretely, your description should not be more than about 100-200 words, even if that comes at the cost of accuracy. There is a hard limit of 1024 characters — descriptions over that will be truncated, so stay comfortably under it. Here are some tips that we've found to work well in writing these descriptions: - The skill should be phrased in the imperative -- "Use this skill for" rather than "this skill does" - The skill description should focus on the user's intent, what they are trying to achieve, vs. the implementation details of how the skill works. - The description competes with other skills for Claude's attention — make it distinctive and immediately recognizable. - If you're getting lots of failures after repeated attempts, change things up. Try different sentence structures or wordings. I'd encourage you to be creative and mix up the style in different iterations since you'll have multiple opportunities to try different approaches and we'll just grab the highest-scoring one at the end. Please respond with only the new description text in tags, nothing else.""" text = _call_claude(prompt, model) match = re.search(r"(.*?)", text, re.DOTALL) description = match.group(1).strip().strip('"') if match else text.strip().strip('"') transcript: dict = { "iteration": iteration, "prompt": prompt, "response": text, "parsed_description": description, "char_count": len(description), "over_limit": len(description) > 1024, } # Safety net: the prompt already states the 1024-char hard limit, but if # the model blew past it anyway, make one fresh single-turn call that # quotes the too-long version and asks for a shorter rewrite. (The old # SDK path did this as a true multi-turn; `claude -p` is one-shot, so we # inline the prior output into the new prompt instead.) if len(description) > 1024: shorten_prompt = ( f"{prompt}\n\n" f"---\n\n" f"A previous attempt produced this description, which at " f"{len(description)} characters is over the 1024-character hard limit:\n\n" f'"{description}"\n\n' f"Rewrite it to be under 1024 characters while keeping the most " f"important trigger words and intent coverage. Respond with only " f"the new description in tags." ) shorten_text = _call_claude(shorten_prompt, model) match = re.search(r"(.*?)", shorten_text, re.DOTALL) shortened = match.group(1).strip().strip('"') if match else shorten_text.strip().strip('"') transcript["rewrite_prompt"] = shorten_prompt transcript["rewrite_response"] = shorten_text transcript["rewrite_description"] = shortened transcript["rewrite_char_count"] = len(shortened) description = shortened transcript["final_description"] = description if log_dir: log_dir.mkdir(parents=True, exist_ok=True) log_file = log_dir / f"improve_iter_{iteration or 'unknown'}.json" log_file.write_text(json.dumps(transcript, indent=2)) return description def main(): parser = argparse.ArgumentParser(description="Improve a skill description based on eval results") parser.add_argument("--eval-results", required=True, help="Path to eval results JSON (from run_eval.py)") parser.add_argument("--skill-path", required=True, help="Path to skill directory") parser.add_argument("--history", default=None, help="Path to history JSON (previous attempts)") parser.add_argument("--model", required=True, help="Model for improvement") parser.add_argument("--verbose", action="store_true", help="Print thinking to stderr") args = parser.parse_args() skill_path = Path(args.skill_path) if not (skill_path / "SKILL.md").exists(): print(f"Error: No SKILL.md found at {skill_path}", file=sys.stderr) sys.exit(1) eval_results = json.loads(Path(args.eval_results).read_text()) history = [] if args.history: history = json.loads(Path(args.history).read_text()) name, _, content = parse_skill_md(skill_path) current_description = eval_results["description"] if args.verbose: print(f"Current: {current_description}", file=sys.stderr) print(f"Score: {eval_results['summary']['passed']}/{eval_results['summary']['total']}", file=sys.stderr) new_description = improve_description( skill_name=name, skill_content=content, current_description=current_description, eval_results=eval_results, history=history, model=args.model, ) if args.verbose: print(f"Improved: {new_description}", file=sys.stderr) # Output as JSON with both the new description and updated history output = { "description": new_description, "history": history + [{ "description": current_description, "passed": eval_results["summary"]["passed"], "failed": eval_results["summary"]["failed"], "total": eval_results["summary"]["total"], "results": eval_results["results"], }], } print(json.dumps(output, indent=2)) if __name__ == "__main__": main() FILE:scripts/package_skill.py #!/usr/bin/env python3 """ Skill Packager - Creates a distributable .skill file of a skill folder Usage: python utils/package_skill.py [output-directory] Example: python utils/package_skill.py skills/public/my-skill python utils/package_skill.py skills/public/my-skill ./dist """ import fnmatch import sys import zipfile from pathlib import Path from scripts.quick_validate import validate_skill # Patterns to exclude when packaging skills. EXCLUDE_DIRS = {"__pycache__", "node_modules"} EXCLUDE_GLOBS = {"*.pyc"} EXCLUDE_FILES = {".DS_Store"} # Directories excluded only at the skill root (not when nested deeper). ROOT_EXCLUDE_DIRS = {"evals"} def should_exclude(rel_path: Path) -> bool: """Check if a path should be excluded from packaging.""" parts = rel_path.parts if any(part in EXCLUDE_DIRS for part in parts): return True # rel_path is relative to skill_path.parent, so parts[0] is the skill # folder name and parts[1] (if present) is the first subdir. if len(parts) > 1 and parts[1] in ROOT_EXCLUDE_DIRS: return True name = rel_path.name if name in EXCLUDE_FILES: return True return any(fnmatch.fnmatch(name, pat) for pat in EXCLUDE_GLOBS) def package_skill(skill_path, output_dir=None): """ Package a skill folder into a .skill file. Args: skill_path: Path to the skill folder output_dir: Optional output directory for the .skill file (defaults to current directory) Returns: Path to the created .skill file, or None if error """ skill_path = Path(skill_path).resolve() # Validate skill folder exists if not skill_path.exists(): print(f"❌ Error: Skill folder not found: {skill_path}") return None if not skill_path.is_dir(): print(f"❌ Error: Path is not a directory: {skill_path}") return None # Validate SKILL.md exists skill_md = skill_path / "SKILL.md" if not skill_md.exists(): print(f"❌ Error: SKILL.md not found in {skill_path}") return None # Run validation before packaging print("🔍 Validating skill...") valid, message = validate_skill(skill_path) if not valid: print(f"❌ Validation failed: {message}") print(" Please fix the validation errors before packaging.") return None print(f"✅ {message}\n") # Determine output location skill_name = skill_path.name if output_dir: output_path = Path(output_dir).resolve() output_path.mkdir(parents=True, exist_ok=True) else: output_path = Path.cwd() skill_filename = output_path / f"{skill_name}.skill" # Create the .skill file (zip format) try: with zipfile.ZipFile(skill_filename, 'w', zipfile.ZIP_DEFLATED) as zipf: # Walk through the skill directory, excluding build artifacts for file_path in skill_path.rglob('*'): if not file_path.is_file(): continue arcname = file_path.relative_to(skill_path.parent) if should_exclude(arcname): print(f" Skipped: {arcname}") continue zipf.write(file_path, arcname) print(f" Added: {arcname}") print(f"\n✅ Successfully packaged skill to: {skill_filename}") return skill_filename except Exception as e: print(f"❌ Error creating .skill file: {e}") return None def main(): if len(sys.argv) < 2: print("Usage: python utils/package_skill.py [output-directory]") print("\nExample:") print(" python utils/package_skill.py skills/public/my-skill") print(" python utils/package_skill.py skills/public/my-skill ./dist") sys.exit(1) skill_path = sys.argv[1] output_dir = sys.argv[2] if len(sys.argv) > 2 else None print(f"📦 Packaging skill: {skill_path}") if output_dir: print(f" Output directory: {output_dir}") print() result = package_skill(skill_path, output_dir) if result: sys.exit(0) else: sys.exit(1) if __name__ == "__main__": main() FILE:scripts/quick_validate.py #!/usr/bin/env python3 """ Quick validation script for skills - minimal version """ import sys import os import re import yaml from pathlib import Path def validate_skill(skill_path): """Basic validation of a skill""" skill_path = Path(skill_path) # Check SKILL.md exists skill_md = skill_path / 'SKILL.md' if not skill_md.exists(): return False, "SKILL.md not found" # Read and validate frontmatter content = skill_md.read_text() if not content.startswith('---'): return False, "No YAML frontmatter found" # Extract frontmatter match = re.match(r'^---\n(.*?)\n---', content, re.DOTALL) if not match: return False, "Invalid frontmatter format" frontmatter_text = match.group(1) # Parse YAML frontmatter try: frontmatter = yaml.safe_load(frontmatter_text) if not isinstance(frontmatter, dict): return False, "Frontmatter must be a YAML dictionary" except yaml.YAMLError as e: return False, f"Invalid YAML in frontmatter: {e}" # Define allowed properties ALLOWED_PROPERTIES = {'name', 'description', 'license', 'allowed-tools', 'metadata', 'compatibility'} # Check for unexpected properties (excluding nested keys under metadata) unexpected_keys = set(frontmatter.keys()) - ALLOWED_PROPERTIES if unexpected_keys: return False, ( f"Unexpected key(s) in SKILL.md frontmatter: {', '.join(sorted(unexpected_keys))}. " f"Allowed properties are: {', '.join(sorted(ALLOWED_PROPERTIES))}" ) # Check required fields if 'name' not in frontmatter: return False, "Missing 'name' in frontmatter" if 'description' not in frontmatter: return False, "Missing 'description' in frontmatter" # Extract name for validation name = frontmatter.get('name', '') if not isinstance(name, str): return False, f"Name must be a string, got {type(name).__name__}" name = name.strip() if name: # Check naming convention (kebab-case: lowercase with hyphens) if not re.match(r'^[a-z0-9-]+$', name): return False, f"Name '{name}' should be kebab-case (lowercase letters, digits, and hyphens only)" if name.startswith('-') or name.endswith('-') or '--' in name: return False, f"Name '{name}' cannot start/end with hyphen or contain consecutive hyphens" # Check name length (max 64 characters per spec) if len(name) > 64: return False, f"Name is too long ({len(name)} characters). Maximum is 64 characters." # Extract and validate description description = frontmatter.get('description', '') if not isinstance(description, str): return False, f"Description must be a string, got {type(description).__name__}" description = description.strip() if description: # Check for angle brackets if '<' in description or '>' in description: return False, "Description cannot contain angle brackets (< or >)" # Check description length (max 1024 characters per spec) if len(description) > 1024: return False, f"Description is too long ({len(description)} characters). Maximum is 1024 characters." # Validate compatibility field if present (optional) compatibility = frontmatter.get('compatibility', '') if compatibility: if not isinstance(compatibility, str): return False, f"Compatibility must be a string, got {type(compatibility).__name__}" if len(compatibility) > 500: return False, f"Compatibility is too long ({len(compatibility)} characters). Maximum is 500 characters." return True, "Skill is valid!" if __name__ == "__main__": if len(sys.argv) != 2: print("Usage: python quick_validate.py ") sys.exit(1) valid, message = validate_skill(sys.argv[1]) print(message) sys.exit(0 if valid else 1) FILE:scripts/run_eval.py #!/usr/bin/env python3 """Run trigger evaluation for a skill description. Tests whether a skill's description causes Claude to trigger (read the skill) for a set of queries. Outputs results as JSON. """ import argparse import json import os import select import subprocess import sys import time import uuid from concurrent.futures import ProcessPoolExecutor, as_completed from pathlib import Path from scripts.utils import parse_skill_md def find_project_root() -> Path: """Find the project root by walking up from cwd looking for .claude/. Mimics how Claude Code discovers its project root, so the command file we create ends up where claude -p will look for it. """ current = Path.cwd() for parent in [current, *current.parents]: if (parent / ".claude").is_dir(): return parent return current def run_single_query( query: str, skill_name: str, skill_description: str, timeout: int, project_root: str, model: str | None = None, ) -> bool: """Run a single query and return whether the skill was triggered. Creates a command file in .claude/commands/ so it appears in Claude's available_skills list, then runs `claude -p` with the raw query. Uses --include-partial-messages to detect triggering early from stream events (content_block_start) rather than waiting for the full assistant message, which only arrives after tool execution. """ unique_id = uuid.uuid4().hex[:8] clean_name = f"{skill_name}-skill-{unique_id}" project_commands_dir = Path(project_root) / ".claude" / "commands" command_file = project_commands_dir / f"{clean_name}.md" try: project_commands_dir.mkdir(parents=True, exist_ok=True) # Use YAML block scalar to avoid breaking on quotes in description indented_desc = "\n ".join(skill_description.split("\n")) command_content = ( f"---\n" f"description: |\n" f" {indented_desc}\n" f"---\n\n" f"# {skill_name}\n\n" f"This skill handles: {skill_description}\n" ) command_file.write_text(command_content) cmd = [ "claude", "-p", query, "--output-format", "stream-json", "--verbose", "--include-partial-messages", ] if model: cmd.extend(["--model", model]) # Remove CLAUDECODE env var to allow nesting claude -p inside a # Claude Code session. The guard is for interactive terminal conflicts; # programmatic subprocess usage is safe. env = {k: v for k, v in os.environ.items() if k != "CLAUDECODE"} process = subprocess.Popen( cmd, stdout=subprocess.PIPE, stderr=subprocess.DEVNULL, cwd=project_root, env=env, ) triggered = False start_time = time.time() buffer = "" # Track state for stream event detection pending_tool_name = None accumulated_json = "" try: while time.time() - start_time < timeout: if process.poll() is not None: remaining = process.stdout.read() if remaining: buffer += remaining.decode("utf-8", errors="replace") break ready, _, _ = select.select([process.stdout], [], [], 1.0) if not ready: continue chunk = os.read(process.stdout.fileno(), 8192) if not chunk: break buffer += chunk.decode("utf-8", errors="replace") while "\n" in buffer: line, buffer = buffer.split("\n", 1) line = line.strip() if not line: continue try: event = json.loads(line) except json.JSONDecodeError: continue # Early detection via stream events if event.get("type") == "stream_event": se = event.get("event", {}) se_type = se.get("type", "") if se_type == "content_block_start": cb = se.get("content_block", {}) if cb.get("type") == "tool_use": tool_name = cb.get("name", "") if tool_name in ("Skill", "Read"): pending_tool_name = tool_name accumulated_json = "" else: return False elif se_type == "content_block_delta" and pending_tool_name: delta = se.get("delta", {}) if delta.get("type") == "input_json_delta": accumulated_json += delta.get("partial_json", "") if clean_name in accumulated_json: return True elif se_type in ("content_block_stop", "message_stop"): if pending_tool_name: return clean_name in accumulated_json if se_type == "message_stop": return False # Fallback: full assistant message elif event.get("type") == "assistant": message = event.get("message", {}) for content_item in message.get("content", []): if content_item.get("type") != "tool_use": continue tool_name = content_item.get("name", "") tool_input = content_item.get("input", {}) if tool_name == "Skill" and clean_name in tool_input.get("skill", ""): triggered = True elif tool_name == "Read" and clean_name in tool_input.get("file_path", ""): triggered = True return triggered elif event.get("type") == "result": return triggered finally: # Clean up process on any exit path (return, exception, timeout) if process.poll() is None: process.kill() process.wait() return triggered finally: if command_file.exists(): command_file.unlink() def run_eval( eval_set: list[dict], skill_name: str, description: str, num_workers: int, timeout: int, project_root: Path, runs_per_query: int = 1, trigger_threshold: float = 0.5, model: str | None = None, ) -> dict: """Run the full eval set and return results.""" results = [] with ProcessPoolExecutor(max_workers=num_workers) as executor: future_to_info = {} for item in eval_set: for run_idx in range(runs_per_query): future = executor.submit( run_single_query, item["query"], skill_name, description, timeout, str(project_root), model, ) future_to_info[future] = (item, run_idx) query_triggers: dict[str, list[bool]] = {} query_items: dict[str, dict] = {} for future in as_completed(future_to_info): item, _ = future_to_info[future] query = item["query"] query_items[query] = item if query not in query_triggers: query_triggers[query] = [] try: query_triggers[query].append(future.result()) except Exception as e: print(f"Warning: query failed: {e}", file=sys.stderr) query_triggers[query].append(False) for query, triggers in query_triggers.items(): item = query_items[query] trigger_rate = sum(triggers) / len(triggers) should_trigger = item["should_trigger"] if should_trigger: did_pass = trigger_rate >= trigger_threshold else: did_pass = trigger_rate < trigger_threshold results.append({ "query": query, "should_trigger": should_trigger, "trigger_rate": trigger_rate, "triggers": sum(triggers), "runs": len(triggers), "pass": did_pass, }) passed = sum(1 for r in results if r["pass"]) total = len(results) return { "skill_name": skill_name, "description": description, "results": results, "summary": { "total": total, "passed": passed, "failed": total - passed, }, } def main(): parser = argparse.ArgumentParser(description="Run trigger evaluation for a skill description") parser.add_argument("--eval-set", required=True, help="Path to eval set JSON file") parser.add_argument("--skill-path", required=True, help="Path to skill directory") parser.add_argument("--description", default=None, help="Override description to test") parser.add_argument("--num-workers", type=int, default=10, help="Number of parallel workers") parser.add_argument("--timeout", type=int, default=30, help="Timeout per query in seconds") parser.add_argument("--runs-per-query", type=int, default=3, help="Number of runs per query") parser.add_argument("--trigger-threshold", type=float, default=0.5, help="Trigger rate threshold") parser.add_argument("--model", default=None, help="Model to use for claude -p (default: user's configured model)") parser.add_argument("--verbose", action="store_true", help="Print progress to stderr") args = parser.parse_args() eval_set = json.loads(Path(args.eval_set).read_text()) skill_path = Path(args.skill_path) if not (skill_path / "SKILL.md").exists(): print(f"Error: No SKILL.md found at {skill_path}", file=sys.stderr) sys.exit(1) name, original_description, content = parse_skill_md(skill_path) description = args.description or original_description project_root = find_project_root() if args.verbose: print(f"Evaluating: {description}", file=sys.stderr) output = run_eval( eval_set=eval_set, skill_name=name, description=description, num_workers=args.num_workers, timeout=args.timeout, project_root=project_root, runs_per_query=args.runs_per_query, trigger_threshold=args.trigger_threshold, model=args.model, ) if args.verbose: summary = output["summary"] print(f"Results: {summary['passed']}/{summary['total']} passed", file=sys.stderr) for r in output["results"]: status = "PASS" if r["pass"] else "FAIL" rate_str = f"{r['triggers']}/{r['runs']}" print(f" [{status}] rate={rate_str} expected={r['should_trigger']}: {r['query'][:70]}", file=sys.stderr) print(json.dumps(output, indent=2)) if __name__ == "__main__": main() FILE:scripts/run_loop.py #!/usr/bin/env python3 """Run the eval + improve loop until all pass or max iterations reached. Combines run_eval.py and improve_description.py in a loop, tracking history and returning the best description found. Supports train/test split to prevent overfitting. """ import argparse import json import random import sys import tempfile import time import webbrowser from pathlib import Path from scripts.generate_report import generate_html from scripts.improve_description import improve_description from scripts.run_eval import find_project_root, run_eval from scripts.utils import parse_skill_md def split_eval_set(eval_set: list[dict], holdout: float, seed: int = 42) -> tuple[list[dict], list[dict]]: """Split eval set into train and test sets, stratified by should_trigger.""" random.seed(seed) # Separate by should_trigger trigger = [e for e in eval_set if e["should_trigger"]] no_trigger = [e for e in eval_set if not e["should_trigger"]] # Shuffle each group random.shuffle(trigger) random.shuffle(no_trigger) # Calculate split points n_trigger_test = max(1, int(len(trigger) * holdout)) n_no_trigger_test = max(1, int(len(no_trigger) * holdout)) # Split test_set = trigger[:n_trigger_test] + no_trigger[:n_no_trigger_test] train_set = trigger[n_trigger_test:] + no_trigger[n_no_trigger_test:] return train_set, test_set def run_loop( eval_set: list[dict], skill_path: Path, description_override: str | None, num_workers: int, timeout: int, max_iterations: int, runs_per_query: int, trigger_threshold: float, holdout: float, model: str, verbose: bool, live_report_path: Path | None = None, log_dir: Path | None = None, ) -> dict: """Run the eval + improvement loop.""" project_root = find_project_root() name, original_description, content = parse_skill_md(skill_path) current_description = description_override or original_description # Split into train/test if holdout > 0 if holdout > 0: train_set, test_set = split_eval_set(eval_set, holdout) if verbose: print(f"Split: {len(train_set)} train, {len(test_set)} test (holdout={holdout})", file=sys.stderr) else: train_set = eval_set test_set = [] history = [] exit_reason = "unknown" for iteration in range(1, max_iterations + 1): if verbose: print(f"\n{'='*60}", file=sys.stderr) print(f"Iteration {iteration}/{max_iterations}", file=sys.stderr) print(f"Description: {current_description}", file=sys.stderr) print(f"{'='*60}", file=sys.stderr) # Evaluate train + test together in one batch for parallelism all_queries = train_set + test_set t0 = time.time() all_results = run_eval( eval_set=all_queries, skill_name=name, description=current_description, num_workers=num_workers, timeout=timeout, project_root=project_root, runs_per_query=runs_per_query, trigger_threshold=trigger_threshold, model=model, ) eval_elapsed = time.time() - t0 # Split results back into train/test by matching queries train_queries_set = {q["query"] for q in train_set} train_result_list = [r for r in all_results["results"] if r["query"] in train_queries_set] test_result_list = [r for r in all_results["results"] if r["query"] not in train_queries_set] train_passed = sum(1 for r in train_result_list if r["pass"]) train_total = len(train_result_list) train_summary = {"passed": train_passed, "failed": train_total - train_passed, "total": train_total} train_results = {"results": train_result_list, "summary": train_summary} if test_set: test_passed = sum(1 for r in test_result_list if r["pass"]) test_total = len(test_result_list) test_summary = {"passed": test_passed, "failed": test_total - test_passed, "total": test_total} test_results = {"results": test_result_list, "summary": test_summary} else: test_results = None test_summary = None history.append({ "iteration": iteration, "description": current_description, "train_passed": train_summary["passed"], "train_failed": train_summary["failed"], "train_total": train_summary["total"], "train_results": train_results["results"], "test_passed": test_summary["passed"] if test_summary else None, "test_failed": test_summary["failed"] if test_summary else None, "test_total": test_summary["total"] if test_summary else None, "test_results": test_results["results"] if test_results else None, # For backward compat with report generator "passed": train_summary["passed"], "failed": train_summary["failed"], "total": train_summary["total"], "results": train_results["results"], }) # Write live report if path provided if live_report_path: partial_output = { "original_description": original_description, "best_description": current_description, "best_score": "in progress", "iterations_run": len(history), "holdout": holdout, "train_size": len(train_set), "test_size": len(test_set), "history": history, } live_report_path.write_text(generate_html(partial_output, auto_refresh=True, skill_name=name)) if verbose: def print_eval_stats(label, results, elapsed): pos = [r for r in results if r["should_trigger"]] neg = [r for r in results if not r["should_trigger"]] tp = sum(r["triggers"] for r in pos) pos_runs = sum(r["runs"] for r in pos) fn = pos_runs - tp fp = sum(r["triggers"] for r in neg) neg_runs = sum(r["runs"] for r in neg) tn = neg_runs - fp total = tp + tn + fp + fn precision = tp / (tp + fp) if (tp + fp) > 0 else 1.0 recall = tp / (tp + fn) if (tp + fn) > 0 else 1.0 accuracy = (tp + tn) / total if total > 0 else 0.0 print(f"{label}: {tp+tn}/{total} correct, precision={precision:.0%} recall={recall:.0%} accuracy={accuracy:.0%} ({elapsed:.1f}s)", file=sys.stderr) for r in results: status = "PASS" if r["pass"] else "FAIL" rate_str = f"{r['triggers']}/{r['runs']}" print(f" [{status}] rate={rate_str} expected={r['should_trigger']}: {r['query'][:60]}", file=sys.stderr) print_eval_stats("Train", train_results["results"], eval_elapsed) if test_summary: print_eval_stats("Test ", test_results["results"], 0) if train_summary["failed"] == 0: exit_reason = f"all_passed (iteration {iteration})" if verbose: print(f"\nAll train queries passed on iteration {iteration}!", file=sys.stderr) break if iteration == max_iterations: exit_reason = f"max_iterations ({max_iterations})" if verbose: print(f"\nMax iterations reached ({max_iterations}).", file=sys.stderr) break # Improve the description based on train results if verbose: print(f"\nImproving description...", file=sys.stderr) t0 = time.time() # Strip test scores from history so improvement model can't see them blinded_history = [ {k: v for k, v in h.items() if not k.startswith("test_")} for h in history ] new_description = improve_description( skill_name=name, skill_content=content, current_description=current_description, eval_results=train_results, history=blinded_history, model=model, log_dir=log_dir, iteration=iteration, ) improve_elapsed = time.time() - t0 if verbose: print(f"Proposed ({improve_elapsed:.1f}s): {new_description}", file=sys.stderr) current_description = new_description # Find the best iteration by TEST score (or train if no test set) if test_set: best = max(history, key=lambda h: h["test_passed"] or 0) best_score = f"{best['test_passed']}/{best['test_total']}" else: best = max(history, key=lambda h: h["train_passed"]) best_score = f"{best['train_passed']}/{best['train_total']}" if verbose: print(f"\nExit reason: {exit_reason}", file=sys.stderr) print(f"Best score: {best_score} (iteration {best['iteration']})", file=sys.stderr) return { "exit_reason": exit_reason, "original_description": original_description, "best_description": best["description"], "best_score": best_score, "best_train_score": f"{best['train_passed']}/{best['train_total']}", "best_test_score": f"{best['test_passed']}/{best['test_total']}" if test_set else None, "final_description": current_description, "iterations_run": len(history), "holdout": holdout, "train_size": len(train_set), "test_size": len(test_set), "history": history, } def main(): parser = argparse.ArgumentParser(description="Run eval + improve loop") parser.add_argument("--eval-set", required=True, help="Path to eval set JSON file") parser.add_argument("--skill-path", required=True, help="Path to skill directory") parser.add_argument("--description", default=None, help="Override starting description") parser.add_argument("--num-workers", type=int, default=10, help="Number of parallel workers") parser.add_argument("--timeout", type=int, default=30, help="Timeout per query in seconds") parser.add_argument("--max-iterations", type=int, default=5, help="Max improvement iterations") parser.add_argument("--runs-per-query", type=int, default=3, help="Number of runs per query") parser.add_argument("--trigger-threshold", type=float, default=0.5, help="Trigger rate threshold") parser.add_argument("--holdout", type=float, default=0.4, help="Fraction of eval set to hold out for testing (0 to disable)") parser.add_argument("--model", required=True, help="Model for improvement") parser.add_argument("--verbose", action="store_true", help="Print progress to stderr") parser.add_argument("--report", default="auto", help="Generate HTML report at this path (default: 'auto' for temp file, 'none' to disable)") parser.add_argument("--results-dir", default=None, help="Save all outputs (results.json, report.html, log.txt) to a timestamped subdirectory here") args = parser.parse_args() eval_set = json.loads(Path(args.eval_set).read_text()) skill_path = Path(args.skill_path) if not (skill_path / "SKILL.md").exists(): print(f"Error: No SKILL.md found at {skill_path}", file=sys.stderr) sys.exit(1) name, _, _ = parse_skill_md(skill_path) # Set up live report path if args.report != "none": if args.report == "auto": timestamp = time.strftime("%Y%m%d_%H%M%S") live_report_path = Path(tempfile.gettempdir()) / f"skill_description_report_{skill_path.name}_{timestamp}.html" else: live_report_path = Path(args.report) # Open the report immediately so the user can watch live_report_path.write_text("

Starting optimization loop...

") webbrowser.open(str(live_report_path)) else: live_report_path = None # Determine output directory (create before run_loop so logs can be written) if args.results_dir: timestamp = time.strftime("%Y-%m-%d_%H%M%S") results_dir = Path(args.results_dir) / timestamp results_dir.mkdir(parents=True, exist_ok=True) else: results_dir = None log_dir = results_dir / "logs" if results_dir else None output = run_loop( eval_set=eval_set, skill_path=skill_path, description_override=args.description, num_workers=args.num_workers, timeout=args.timeout, max_iterations=args.max_iterations, runs_per_query=args.runs_per_query, trigger_threshold=args.trigger_threshold, holdout=args.holdout, model=args.model, verbose=args.verbose, live_report_path=live_report_path, log_dir=log_dir, ) # Save JSON output json_output = json.dumps(output, indent=2) print(json_output) if results_dir: (results_dir / "results.json").write_text(json_output) # Write final HTML report (without auto-refresh) if live_report_path: live_report_path.write_text(generate_html(output, auto_refresh=False, skill_name=name)) print(f"\nReport: {live_report_path}", file=sys.stderr) if results_dir and live_report_path: (results_dir / "report.html").write_text(generate_html(output, auto_refresh=False, skill_name=name)) if results_dir: print(f"Results saved to: {results_dir}", file=sys.stderr) if __name__ == "__main__": main() FILE:scripts/utils.py """Shared utilities for skill-creator scripts.""" from pathlib import Path def parse_skill_md(skill_path: Path) -> tuple[str, str, str]: """Parse a SKILL.md file, returning (name, description, full_content).""" content = (skill_path / "SKILL.md").read_text() lines = content.split("\n") if lines[0].strip() != "---": raise ValueError("SKILL.md missing frontmatter (no opening ---)") end_idx = None for i, line in enumerate(lines[1:], start=1): if line.strip() == "---": end_idx = i break if end_idx is None: raise ValueError("SKILL.md missing frontmatter (no closing ---)") name = "" description = "" frontmatter_lines = lines[1:end_idx] i = 0 while i < len(frontmatter_lines): line = frontmatter_lines[i] if line.startswith("name:"): name = line[len("name:"):].strip().strip('"').strip("'") elif line.startswith("description:"): value = line[len("description:"):].strip() # Handle YAML multiline indicators (>, |, >-, |-) if value in (">", "|", ">-", "|-"): continuation_lines: list[str] = [] i += 1 while i < len(frontmatter_lines) and (frontmatter_lines[i].startswith(" ") or frontmatter_lines[i].startswith("\t")): continuation_lines.append(frontmatter_lines[i].strip()) i += 1 description = " ".join(continuation_lines) continue else: description = value.strip('"').strip("'") i += 1 return name, description, content FILE:scripts/__init__.py

Eval Set Review: __SKILL_NAME_PLACEHOLDER__

Eval Review:

Review Complete

""" + title_prefix + """Skill Description Optimization

Starting optimization loop...

Tạo và cải thiện skill — skill-creator

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