skill-creator-pro: создание, улучшение и анализ скиллов

--- name: skill-creator-pro description: Create new skills, modify and improve existing skills, and measure skill performance. Enhanced version with quick commands. Use when users want to create a skill from scratch, update 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. Triggers on phrases like "make a skill", "create a new skill", "build a skill for", "improve this skill", "optimize my skill", "test my skill", "turn this into a skill", "skill description optimization", or "help me create a skill". --- # Skill Creator Pro Creates, improves, and tests Agent Skills for any domain — engineering, content creation, research, personal productivity, and beyond. ## Workflow Overview ``` Phase 1: Understand → Phase 2: Design → Phase 3: Write Phase 4: Test → Phase 5: Improve → Phase 6: Optimize ``` Jump in at the right phase based on where the user is: - "I want to make a skill for X" → Start at Phase 1 - "Here's my skill draft, help me improve it" → Start at Phase 4 - "My skill isn't triggering correctly" → Start at Phase 6 - "Just vibe with me" → Skip phases as needed, stay flexible 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. --- ## Phase 1: Understand This phase uses the Inversion pattern — ask first, build later. If the current conversation already contains a workflow the user wants to capture (e.g., "turn this into a skill"), extract answers from the conversation history first before asking. Ask these questions **one at a time**, wait for each answer. DO NOT proceed to Phase 2 until all required questions are answered. **Q1 (Required)**: What should this skill enable Claude to do? **Q2 (Required)**: When should it trigger? What would a user say to invoke it? **Q3 (Required)**: Which content pattern fits best? Read `references/content-patterns.md` and recommend 1-2 patterns with brief reasoning. Let the user confirm before continuing. **Q4**: What's the expected output format? **Q5**: Should we set up test cases? Skills with objectively verifiable outputs (file transforms, data extraction, fixed workflows) benefit from test cases. Skills with subjective outputs (writing style, art direction) often don't need them. Suggest the appropriate default, but let the user decide. **Gate**: All required questions answered + content pattern confirmed → proceed to Phase 2. ### Interview and Research After the 5 questions, proactively ask 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. --- ## Phase 2: Design Before writing, read: - `references/content-patterns.md` — apply the confirmed pattern's structure - `references/design_principles.md` — 5 principles to follow - `references/patterns.md` — implementation patterns (config.json, gotchas, script reuse, etc.) Decide: - File structure needed (`scripts/` / `references/` / `assets/`) - Whether `config.json` setup is needed (user needs to provide personal config) - Whether on-demand hooks are needed **Gate**: Design decisions clear → proceed to Phase 3. --- ## Phase 3: Write Based on the interview and design decisions, write the SKILL.md. ### Components - **name**: Skill identifier (kebab-case, no "claude" or "anthropic" — see `references/constraints_and_rules.md`) - **description**: The primary triggering mechanism. Include what the skill does AND when to use it. Follow the formula: `[What it does] + [When to use] + [Trigger phrases]`. Under 1024 characters, no XML angle brackets. Make it slightly "pushy" to combat undertriggering — see `references/constraints_and_rules.md` for guidance. - **compatibility**: Required tools/dependencies (optional, rarely needed) - **the rest of the skill :)** ### Skill Writing Guide **Before writing**, read: - `references/content-patterns.md` — apply the confirmed pattern's structure to the SKILL.md body - `references/design_principles.md` — 5 design principles - `references/constraints_and_rules.md` — technical constraints, naming conventions - Keep `references/quick_checklist.md` handy for pre-publication verification #### 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 ``` **Gotchas section** - Every skill should have one. Add it as you discover real failures: ```markdown ## Gotchas - **[Problem]**: [What goes wrong] → [What to do instead] ``` **config.json setup** - If the skill needs user configuration, check for `config.json` at startup and use `AskUserQuestion` to collect missing values. See `references/patterns.md` for the standard flow. ### 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. If you find yourself stacking ALWAYS/NEVER, stop and ask: can I explain the reasoning instead? A skill that explains *why* is more robust than one that just issues commands. **Gate**: Draft complete, checklist reviewed → proceed to Phase 4. ### 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). ### Plugin Integration Check **IMPORTANT**: After writing the skill draft, check if this skill is part of a Claude Code plugin. If the skill path contains `.claude-plugins/` or `plugins/`, automatically perform a plugin integration check. #### When to Check Check plugin integration if: - Skill path contains `.claude-plugins/` or `plugins/` - User mentions "plugin", "command", or "agent" in context - You notice related commands or agents in the same directory structure #### What to Check 1. **Detect Plugin Context** ```bash # Look for plugin.json in parent directories SKILL_DIR="path/to/skill" CURRENT_DIR=$(dirname "$SKILL_DIR") while [ "$CURRENT_DIR" != "/" ]; do if [ -f "$CURRENT_DIR/.claude-plugin/plugin.json" ]; then echo "Found plugin at: $CURRENT_DIR" break fi CURRENT_DIR=$(dirname "$CURRENT_DIR") done ``` 2. **Check for Related Components** - Look for `commands/` directory - are there commands that should use this skill? - Look for `agents/` directory - are there agents that should reference this skill? - Search for skill name in existing commands and agents 3. **Verify Three-Layer Architecture** The plugin should follow this pattern: ``` Command (Orchestration) → Agent (Execution) → Skill (Knowledge) ``` **Command Layer** should: - Check prerequisites (is service running?) - Gather user requirements (use AskUserQuestion) - Delegate complex work to agent - Verify final results **Agent Layer** should: - Define clear capabilities - Reference skill for API/implementation details - Outline execution workflow - Handle errors and iteration **Skill Layer** should: - Document API endpoints and usage - Provide best practices - Include examples - Add troubleshooting guide - NOT contain workflow logic (that's in commands) 4. **Generate Integration Report** If this skill is part of a plugin, generate a brief report: ```markdown ## Plugin Integration Status Plugin: {name} v{version} Skill: {skill-name} ### Related Components - Commands: {list or "none found"} - Agents: {list or "none found"} ### Architecture Check - [ ] Command orchestrates workflow - [ ] Agent executes autonomously - [ ] Skill documents knowledge - [ ] Clear separation of concerns ### Recommendations {specific suggestions if integration is incomplete} ``` 5. **Offer to Fix Integration Issues** If you find issues: - Missing command that should orchestrate this skill - Agent that doesn't reference the skill - Command that tries to do everything (monolithic) - Skill that contains workflow logic Offer to create/fix these components following the three-layer pattern. #### Example Integration Check ```bash # After creating skill at: plugins/my-plugin/skills/api-helper/ # 1. Detect plugin Found plugin: my-plugin v1.0.0 # 2. Check for related components Commands found: - commands/api-call.md (references api-helper ✅) Agents found: - agents/api-executor.md (references api-helper ✅) # 3. Verify architecture ✅ Command delegates to agent ✅ Agent references skill ✅ Skill documents API only ✅ Clear separation of concerns Integration Score: 0.9 (Excellent) ``` #### Reference Documentation For detailed architecture guidance, see: - `PLUGIN_ARCHITECTURE.md` in project root - `tldraw-helper/ARCHITECTURE.md` for reference implementation - `tldraw-helper/commands/draw.md` for example command **After integration check**, proceed with test cases as normal. ## Phase 4: Test ### 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 `<skill-name>-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: <path-to-skill> - Task: <eval prompt> - Input files: <eval files if any, or "none"> - Save outputs to: <workspace>/iteration-<N>/eval-<ID>/with_skill/outputs/ - Outputs to save: <what the user cares about — e.g., "the .docx file", "the final CSV"> ``` **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-path> <workspace>/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 <workspace>/iteration-N --skill-name <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 <skill-creator-path>/eval-viewer/generate_review.py <workspace>/iteration-N --skill-name "my-skill" --benchmark <workspace>/iteration-N/benchmark.json > /dev/null 2>&1 & VIEWER_PID=$! ``` For iteration 2+, also pass `--previous-workspace <workspace>/iteration-<N-1>`. **Cowork / headless environments:** If `webbrowser.open()` is not available or the environment has no display, use `--static <output_path>` 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 ``` --- ## Phase 5: Improve ### 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-<N+1>/` 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. --- ## Phase 6: Optimize Description ### 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_<skill-name>.html`) and open it: `open /tmp/eval_review_<skill-name>.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 <path-to-trigger-eval.json> --skill-path <path-to-skill> --model <model-id-powering-this-session> --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 with extended thinking 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. --- ### Final Quality Check Before packaging, run through `references/quick_checklist.md` to verify: - All technical constraints met (naming, character limits, forbidden terms) - Description follows the formula: `[What it does] + [When to use] + [Trigger phrases]` - File structure correct (SKILL.md capitalization, kebab-case folders) - Security requirements satisfied (no malware, no misleading functionality) - Quantitative success criteria achieved (90%+ trigger rate, efficient tool usage) - Design principles applied (Progressive Disclosure, Composability, Portability) This checklist helps catch common issues before publication. --- ### 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 <path/to/skill-folder> ``` 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. --- ## 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 <output_path>` 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. --- ## 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/design_principles.md` — Core design principles (Progressive Disclosure, Composability, Portability) and three common use case patterns (Document Creation, Workflow Automation, MCP Enhancement) - `references/constraints_and_rules.md` — Technical constraints, naming conventions, security requirements, and quantitative success criteria - `references/quick_checklist.md` — Comprehensive pre-publication checklist covering file structure, frontmatter, testing, and quality tiers - `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!