Create new skills, modify and improve existing skills, and measure skill performance. 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.
Create new skills, modify and improve existing skills, and measure skill performance. 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.
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.
What should this skill enable Claude to do?
When should this skill trigger? (what user phrases/contexts)
What's the expected output format?
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.'"
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:
Metadata (name + description) - Always in context (~100 words)
SKILL.md body - In context whenever skill triggers (<500 lines ideal)
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:
The layout above keeps a skill's scripts/, references/, and assets/ inside its own folder, which is the right default: a self-contained skill is easy to reason about and to move. But when several skills in one plugin lean on the same facts or the same helper — say a data-format reference that an export skill and an import skill have to agree on — copying it into each skill invites drift, where you fix one copy and miss the other.
For that case, put the shared file once at the plugin root and point each skill at it with ${CLAUDE_PLUGIN_ROOT}, which Claude Code substitutes inline anywhere it appears in skill content with the absolute path to the plugin's install directory:
my-plugin/
├── .claude-plugin/plugin.json
├── shared/
│ └── data-format.md ← single source of truth
└── skills/
├── export/SKILL.md → "read ${CLAUDE_PLUGIN_ROOT}/shared/data-format.md"
└── import/SKILL.md → "read ${CLAUDE_PLUGIN_ROOT}/shared/data-format.md"
Write it as a plain instruction in the body ("read ${CLAUDE_PLUGIN_ROOT}/shared/data-format.md before starting"). Keep a skill's own bundled files relative (references/foo.md); reach for the variable only when the target lives outside the skill's own directory. The rule that bites if you skip it: never hardcode the resolved path, because the install directory is version-stamped and moves on every plugin update — the variable is what stays valid across the bump. Two siblings cover adjacent needs: ${CLAUDE_PLUGIN_DATA} is a directory that persists across updates (use it for installed dependencies like a venv or node_modules you don't want to rebuild each version), and a top-level bin/ is added to the Bash tool's PATH while the plugin is enabled, the tidiest way to share a runnable helper.
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:
## 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):
## 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 tmp/<skill-name>-workspace/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.
{"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 tmp/<skill-name>-workspace/ relative to the project root — eval artifacts are ephemeral and should not live inside the skill directory tree. If tmp/ does not exist, create it and append tmp/ to the project's .gitignore (only if a .gitignore file already exists). 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.
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 tmp/<skill-name>-workspace/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 tmp/<skill-name>-workspace/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:
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:
Grade each run — agents/grader.md defines the canonical grading.json schema. Spawn a grader subagent that reads it, OR (recommended for assertions that can be checked programmatically — faster, more reliable, reusable across iterations) write a script that produces output conforming to that schema. Two top-level keys are required: summary (with pass_rate, passed, failed, total) and expectations (each item with text, passed, evidence). The viewer and aggregate_benchmark.py both depend on these exact names. Common breakage: skipping summary makes the per-eval Formal Grades footer in the viewer show ? — 0 passed, 0 failed of 0; off-spec expectation field names (e.g. name/met/details) make assertions disappear from the list.
Aggregate into benchmark — run the aggregation script from the aeo-skill-creator directory:
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.
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.
Launch the viewer with both qualitative outputs and quantitative data:
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. If running on Windows or WSL, --static will work and should not need any manual file copying. 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.
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
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:
{"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:
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
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.
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.
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.
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:
Apply your improvements to the skill
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.
Launch the reviewer with --previous-workspace pointing at the previous iteration
Wait for the user to review and tell you they're done
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 to tmp/<skill-name>-workspace/trigger-evals/<eval-name>.json. If tmp/ does not exist, create it and append tmp/ to .gitignore if one exists. Format:
[{"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:
Read the template from assets/eval_review.html
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
Write to tmp/<skill-name>-workspace/eval_review.html and open it with python3 -m webbrowser "$(pwd)/tmp/<skill-name>-workspace/eval_review.html" (works on macOS, Linux, and WSL — absolute path required for WSL browser interop)
The user can edit queries, toggle should-trigger, add/remove entries, then click "Export Eval Set"
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 exported eval set to tmp/<skill-name>-workspace/trigger-evals/, then run in the background:
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.
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:
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/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.
