opencode-coder-skill-creator

name	opencode-coder-skill-creator
description	Create or adapt skills for OpenCode, improve existing skills through iterative test-and-review loops, and package finished skills. Use when users want to author a skill from scratch, revise skill behavior, run qualitative/quantitative evaluations, or prepare a distributable .skill bundle.

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 the skill on them via OpenCode's skill-loading mechanism
• 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 a description-optimization pass to improve triggering behavior via the CLI scripts in scripts/run_eval.py, scripts/improve_description.py, and scripts/run_loop.py.

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 AI assistants 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.

---

OpenCode Environment Assumptions

• Assume an OpenCode host environment with shell/file tools, but not necessarily browser access.
• Prefer compatibility with both interactive and headless runs; always provide a no-browser fallback path.
• Do not rely on Claude Code-only runtime semantics (for example task notification fields that are unavailable in OpenCode).
• Do not assume fixed download directories (for example ~/Downloads); use explicit user-confirmed paths.

Agent Mapping (OpenCode)

When the workflow uses role-specialized agents, map work like this:

• orchestrator: drive the end-to-end loop, sequence phases, and communicate with user
• tasker: execute concrete run/setup tasks and produce artifacts
• reviewer: grade outputs and perform comparative quality review
• verifier: synthesize findings, validate conclusions, and flag residual risk

Bundled instruction-file mapping:

• agents/grader.md → reviewer (default)
• agents/comparator.md → reviewer for blind A/B comparisons
• agents/analyzer.md → verifier for benchmark pattern analysis

Model Selection Guidance

• Use stronger reasoning models for drafting complex skills, refining nuanced instructions, and analyzing conflicting evidence.
• Use faster/cheaper models for mechanical path updates, schema plumbing, and repetitive artifact prep.
• If quality conclusions depend on subtle tradeoffs (generalization vs overfitting, benchmark variance interpretation), bias toward stronger reasoning.

---

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 the AI 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 if the runtime supports it, 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: AI assistants often have 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

The AI 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.

Runtime-value sentence review

Before finalizing runtime SKILL.md content, review each sentence and ask: does this sentence change runtime routing, decisions, constraints, or outputs?

Keep a sentence in runtime skill text only if it does at least one of the following:

1. Routing: changes which workflow/reference/tool path should be used
2. Decisions: changes branching, ask-first behavior, or prioritization logic
3. Constraints: changes scope boundaries, safety limits, or disallowed actions
4. Outputs: changes required response structure, required artifacts, or quality bars

If a sentence does not affect one of those runtime outcomes, move it out of runtime-loaded skill content and place it in contributor-facing docs or authoring sections (for example this skill, docs/, or design guidance), rather than leaving it in the invoked skill text.

Use docs/user-guide/how-to-write-skills.md as the canonical contributor reference for runtime-vs-authoring placement.

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 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).

Files vs hooks in functional evals

Use files when the input is static and can be declared upfront (fixtures, sample docs, templates). The runner hydrates these files into the eval workspace before execution.

Use hooks when setup/teardown is procedural (for example generating dynamic fixtures, initializing local state, or collecting artifacts).

• hooks.before_run: setup scripts, run sequentially before model execution
• hooks.after_run: teardown/collection scripts, run sequentially after model execution attempt

Hook entries support:

• script (required): path relative to the skill root
• args (optional): argument array
• timeout_seconds (optional): defaults to 30
• name (optional): defaults to script basename

Execution contract for functional hooks:

• Each run starts in a fresh disposable workspace
• Starting content is only runner-managed runtime skill injection + hydrated eval-declared files
• No git, beads, or project-specific state is present unless hooks create it
• Hook cwd is the workspace root used for skill execution
• Per-hook env overrides are not supported in v1
• Standard env vars are injected: EVAL_ID, EVAL_NAME, EVAL_PHASE, EVAL_WORKSPACE, EVAL_ARTIFACTS_DIR, EVAL_SKILL_ROOT

Failure semantics:

• before_run executes in listed order; first failure/timeout stops remaining before_run, skips model execution, records setup failure, and still runs after_run
• after_run always runs in listed order even after setup/model failure; any after_run failure fails the eval

The runner cleans up the disposable workspace after after_run finishes and artifacts are captured.

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: Run all eval variants for each test case

For each test case, run both variants: one with the skill, one without (or old-skill baseline). Prefer parallel execution if the runtime supports it; otherwise run serially and label runs clearly.

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 run 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.

{
  "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

As each run completes, write timing.json in the run directory. Use this fallback hierarchy:

1. Preferred: If completion metadata exposes total_tokens and elapsed time (or equivalent), record those values directly.
2. Fallback A: If per-run timing metadata is unavailable, capture wall-clock start/end timestamps and compute elapsed duration.
3. Fallback B: If token usage is unavailable, set total_tokens to null and include a short note about the source.

Never block the workflow waiting for telemetry that the runtime does not expose.

{
  "run_started_at": "2026-03-24T12:01:02Z",
  "run_finished_at": "2026-03-24T12:01:31Z",
  "duration_ms": 29000,
  "total_duration_seconds": 29.0,
  "total_tokens": null,
  "token_source": "unavailable"
}

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:

   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:

   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>.

   Headless / no-browser 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 may be downloaded as a feedback.json file when the user clicks "Submit All Reviews". After download, copy or move 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." If no browser is available, instead give the user the static HTML path and explain how to open it on their machine.

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:

{
  "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

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 runs all independently wrote similar helper scripts or took the same multi-step approach to something. If all 3 test cases resulted in 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.

---

Description Optimization

This section is for trigger evals only.

• Trigger eval schema: trigger-evals.json
• Trigger eval runner: scripts/run_eval.py (and scripts/run_loop.py)
• Functional eval schema: evals/evals.json
• Functional eval execution uses a dedicated functional runner
• Do not mix trigger and functional schemas in one command; no auto-detection in v1

The description field in SKILL.md frontmatter is the primary mechanism that determines whether the AI assistant 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:

[
  {"query": "the user prompt", "should_trigger": true},
  {"query": "another prompt", "should_trigger": false}
]

The queries must be realistic and something a user in the host AI environment 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__ → encodeURIComponent(JSON.stringify(eval_items)) (insert as the raw encoded string with no extra quotes; template decodes/parses it)
   • __SKILL_NAME_PLACEHOLDER__ → the skill's name
   • __SKILL_DESCRIPTION_PLACEHOLDER__ → the skill's current description
3. Write to a temp file (for example, /tmp/eval_review_<skill-name>.html) and open it using local OS tooling when available (for example open, xdg-open, or equivalent)
4. The user can edit queries, toggle should-trigger, add/remove entries, then click "Export Eval Set"
5. Save or move the exported eval_set.json to an explicit project path. Do not assume ~/Downloads; ask the user for the exact location if needed.

If browser access is unavailable, review and edit the eval JSON inline with the user instead.

This step matters — bad eval queries lead to bad descriptions.

Step 3: Automated OpenCode CLI optimization loop

Use the bundled scripts for a CLI-only loop. They rely on:

• opencode run --format json --print-logs --log-level DEBUG
• skill availability signals in debug logs (for example service=skill count=..., permission=skill pattern=<name>)
• actual skill-use proof in JSON events ("type":"tool_use", "tool":"skill", "input":{"name":"<runtime-skill-name>"})

Run a single eval pass:

python -m scripts.run_eval \
  --eval-set <path/to/eval_set.json> \
  --skill-path <path/to/skill> \
  --runs-per-query 1 \
  --verbose

Run iterative optimize/eval loop:

python -m scripts.run_loop \
  --eval-set <path/to/eval_set.json> \
  --skill-path <path/to/skill> \
  --timeout 240 \
  --max-iterations 3 \
  --runs-per-query 1 \
  --results-dir <path/to/results-root> \
  --report none

What gets recorded:

1. Per-run raw stdout.ndjson and stderr.log
2. Parsed trigger decision and rationale in result.json
3. Aggregate eval and loop outputs in results.json
4. Description rewrite transcripts in logs/improve_iter_*.json

Bounded-run interpretation:

• A run is considered decidable when trigger intent is provable from observed signal, even if the process times out. Examples:
  • tool_use (skill clearly used)
  • available_but_not_used (skill available but not used)
  • load_then_reject (skill loaded then explicitly rejected as out-of-scope)
• Timeout records still preserve this evidence (timeout_after_trigger, timeout_without_trigger, etc.) under detection.partial_signal.
• run_eval.py aggregates pass/fail using decidable signal correctness (success_rate) and reports undecidable_runs separately in summary for true measurement gaps.

Use these artifacts to identify false negatives/false positives, improve descriptions, and verify progress over iterations.

How skill triggering works

Understanding the triggering mechanism helps design better eval queries. Skills appear in the AI assistant's available_skills list with their name + description, and the assistant decides whether to consult a skill based on that description. The important thing to know is that the assistant 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 the assistant 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 the host environment 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 the best validated description from Step 3 and update the skill's SKILL.md frontmatter. You can do this automatically during loop runs with --apply-best, or manually after reviewing artifacts. Show the user before/after and summarize what improved (for example, reduced false triggers or better intent coverage).

---

Package and Present

Package the skill and direct the user to the resulting .skill file path so they can install it:

python -m scripts.package_skill <path/to/skill-folder>

If direct file presentation is unavailable, provide the absolute path and brief install instructions.

---

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 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.

Good luck!