Metadata-Version: 2.4
Name: better-skills
Version: 0.4.1
Summary: CLI for authoring, evaluating, and iteratively improving Claude Agent Skills.
Author: Richard Wang
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---
name: better-skills
description: Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.
---

# Better Skills

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 `iterate` 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

This skill is liable to be used by people across a wide range of familiarity with coding jargon. If you haven't heard (and how could you, it's only very recently that it started), there's a trend now where the power of Claude is inspiring plumbers to open up their terminals, parents and grandparents to google "how to install npm". On the other hand, the bulk of users are probably fairly computer-literate.

So please pay attention to context cues to understand how to phrase your communication! In the default case, just to give you some idea:

- "evaluation" and "benchmark" are borderline, but OK
- for "JSON" and "assertion" you want to see serious cues from the user that they know what those things are before using them without explaining them

It's OK to briefly explain terms if you're in doubt, and feel free to clarify terms with a short definition if you're unsure if the user will get it.

---

## Creating a skill

### Capture Intent

Start by understanding the user's intent. The current conversation might already contain a workflow the user wants to capture (e.g., they say "turn this into a skill"). If so, extract answers from the conversation history first — the tools used, the sequence of steps, corrections the user made, input/output formats observed. The user may need to fill the gaps, and should confirm before proceeding to the next step.

1. What should this skill enable Claude to do?
2. When should this skill trigger? (what user phrases/contexts)
3. What's the expected output format?
4. Should we set up test cases to verify the skill works? Skills with objectively verifiable outputs (file transforms, data extraction, code generation, fixed workflow steps) benefit from test cases. Skills with subjective outputs (writing style, art) often don't need them. Suggest the appropriate default based on the skill type, but let the user decide.

### Interview and Research

Proactively ask questions about edge cases, input/output formats, example files, success criteria, and dependencies. Wait to write test prompts until you've got this part ironed out.

Check available MCPs - if useful for research (searching docs, finding similar skills, looking up best practices), research in parallel via subagents if available, otherwise inline. Come prepared with context to reduce burden on the user.

### Write the SKILL.md

Based on the user interview, fill in these components:

- **name**: Skill identifier
- **description**: When to trigger, what it does. This is the primary triggering mechanism - include both what the skill does AND specific contexts for when to use it. All "when to use" info goes here, not in the body. Note: currently Claude has a tendency to "undertrigger" skills -- to not use them when they'd be useful. To combat this, please make the skill descriptions a little bit "pushy". So for instance, instead of "How to build a simple fast dashboard to display internal Anthropic data.", you might write "How to build a simple fast dashboard to display internal Anthropic data. Make sure to use this skill whenever the user mentions dashboards, data visualization, internal metrics, or wants to display any kind of company data, even if they don't explicitly ask for a 'dashboard.'"
- **compatibility**: Required tools, dependencies (optional, rarely needed)
- **the rest of the skill :)**

### Skill Writing Guide

#### Anatomy of a Skill

```
skill-name/
├── SKILL.md (required)
│   ├── YAML frontmatter (name, description required)
│   └── Markdown instructions
└── Bundled Resources (optional)
    ├── scripts/    - Executable code for deterministic/repetitive tasks
    ├── references/ - Docs loaded into context as needed
    └── assets/     - Files used in output (templates, icons, fonts)
```

#### Progressive Disclosure

Skills use a three-level loading system:
1. **Metadata** (name + description) - Always in context (~100 words)
2. **SKILL.md body** - In context whenever skill triggers (<500 lines ideal)
3. **Bundled resources** - As needed (unlimited, scripts can execute without loading)

These word counts are approximate and you can feel free to go longer if needed.

**Key patterns:**
- Keep SKILL.md under 500 lines; if you're approaching this limit, add an additional layer of hierarchy along with clear pointers about where the model using the skill should go next to follow up.
- Reference files clearly from SKILL.md with guidance on when to read them
- For large reference files (>300 lines), include a table of contents

**Domain organization**: When a skill supports multiple domains/frameworks, organize by variant:
```
cloud-deploy/
├── SKILL.md (workflow + selection)
└── references/
    ├── aws.md
    ├── gcp.md
    └── azure.md
```
Claude reads only the relevant reference file.

#### Principle of Lack of Surprise

This goes without saying, but skills must not contain malware, exploit code, or any content that could compromise system security. A skill's contents should not surprise the user in their intent if described. Don't go along with requests to create misleading skills or skills designed to facilitate unauthorized access, data exfiltration, or other malicious activities. Things like a "roleplay as an XYZ" are OK though.

#### Writing Patterns

Prefer using the imperative form in instructions.

**Defining output formats** - You can do it like this:
```markdown
## Report structure
ALWAYS use this exact template:
# [Title]
## Executive summary
## Key findings
## Recommendations
```

**Examples pattern** - It's useful to include examples. You can format them like this (but if "Input" and "Output" are in the examples you might want to deviate a little):
```markdown
## Commit message format
**Example 1:**
Input: Added user authentication with JWT tokens
Output: feat(auth): implement JWT-based authentication
```

### Writing Style

Try to explain to the model why things are important in lieu of heavy-handed musty MUSTs. Use theory of mind and try to make the skill general and not super-narrow to specific examples. Start by writing a draft and then look at it with fresh eyes and improve it.

### Test Cases

After writing the skill draft, come up with 2-3 realistic test prompts — the kind of thing a real user would actually say. Share them with the user: [you don't have to use this exact language] "Here are a few test cases I'd like to try. Do these look right, or do you want to add more?" Then run them.

Save test cases to `evals.json`. Don't write assertions yet — just the prompts. You'll draft assertions in the next step while the runs are in progress.

```json
{
  "skill_name": "example-skill",
  "evals": [
    {
      "id": 1,
      "prompt": "User's task prompt",
      "expected_output": "Description of expected result",
      "files": []
    }
  ]
}
```

See `references/schemas.md` for the full schema (including the `assertions` field, which you'll add later).

## Running and evaluating test cases

This section is one continuous sequence — don't stop partway through. Do NOT use `/skill-test` or any other testing skill.

Put results in `<skill-name>-workspace/` as a sibling to the skill directory. The default flow uses **one orchestrator command** that handles snapshot creation, executor + grader runs, aggregation, and viewer launch. Underneath it sits the same set of scripts you can drop down to for advanced cases (single-phase reruns, just re-aggregating, etc.).

The on-disk layout the orchestrator builds:

```
<skill-name>-workspace/
└── iteration-<N>/
    ├── manifest.json                   (index of all runs, see references/manifest-schema.md)
    ├── benchmark.json                  (after aggregation)
    ├── benchmark.md
    ├── viewer.log                      (when viewer launched in background)
    └── eval-<id>/
        ├── eval_metadata.json          (regenerated from evals.json each run)
        └── current/ or baseline/      (always these two directory names)
            └── run-<k>/
                ├── transcript.jsonl
                ├── stderr.log
                ├── timing.json
                ├── grading.json
                ├── grader_transcript.jsonl
                ├── run_status.json     (status checkpoint for --resume)
                └── outputs/
    └── skill-state/                    (auto-snapshot of the live skill,
                                         dumped at iterate-end so future
                                         iterations can compare against it)
```

Every iteration runs each case under exactly two configurations: `current` (the live skill at `--skill-path`) and `baseline` (resolved from `default_baseline` in evals.json or the `--baseline` CLI flag). The directory names are fixed.

### Step 1: Run one full iteration with `better-skills iterate`

`scripts/cli.py` exposes a single `better-skills` CLI with one subcommand per pipeline stage. `iterate` is the default — it runs all executors + graders in parallel, writes a per-iteration manifest, dumps the live skill into `iteration-N/skill-state/` for future-iteration comparisons, aggregates into `benchmark.json`/`benchmark.md`, and launches the viewer in the background — all from one command.

Before launching, ensure `<skill>/evals.json` exists with `defaults` + `cases` (see `references/evals-schema.md` for the schema, or `better-skills init <skill-path>` to scaffold a starting template).

**Invocation** — launch as a **background Bash tool call** (`run_in_background: true`) so you can draft assertions while it runs:

```bash
python -m scripts.cli iterate \
  --skill-path <path-to-skill> \
  --workspace <skill-name>-workspace \
  --iteration <N>
```

Run it **from the better-skills directory** (so `python -m scripts.*` resolves).

**Baseline modes** are declared via `default_baseline` in evals.json (overridable per-invocation with `--baseline`). Grammar: `none | previous | iteration-N | path:/abs/path`.
- **Creating a new skill / first iteration**: `default_baseline: "previous"` is the right default — iteration 1 has no previous, so it auto-degrades to `none` (bare model, no skill). Iteration 2+ then automatically compares against iteration N-1's `skill-state/` snapshot.
- **Long-term progress against a fixed reference**: `default_baseline: "iteration-1"` (or any earlier iteration). Useful for "how much have I improved since the start" rather than "did this iteration help".
- **Compare against an external skill version**: `--baseline path:/abs/path/to/other-skill` — pair with `git worktree add /tmp/skill-prev <commit>` to compare against any past commit.

The runner auto-snapshots the live skill into `iteration-N/skill-state/` at iterate-end, so `previous` and `iteration-N` resolve naturally on subsequent runs. No manual snapshot management.

**Executor and grader runtimes** are configured in `evals.json` (`executor`, `grader_executor`, `default_model`, `grader_model`). Default is Claude for both; switch to `opencode` when the user only has OpenCode access. See `references/evals-schema.md` for the schema.

**Common CLI flags** (all override `evals.json` defaults):
- `--evals-json <path>` — defaults to `<skill-path>/evals.json`
- `--num-workers N` — parallelism
- `--default-timeout SEC` — per-run ceiling; per-case override via a `timeout_s` field in evals.json
- `--baseline <spec>` — override `default_baseline` from evals.json (`none | previous | iteration-N | path:/abs`)
- `--runs-per-config N` — replicate each case × config N times for variance analysis
- `--resume` — skip runs whose transcript already shows executor success (and `grading.json` exists for the grader phase). Use after a crash, network blip, or to add new runs to a partial iteration.
- `--phase executor` / `--phase grader` — run only one phase. Useful when interleaving manual work between phases.
- `--no-view` / `--no-aggregate` — skip the trailing steps when you only want raw runs.
- `--previous-iteration <N>` — pass `<workspace>/iteration-<N>/` to the viewer as `--previous-workspace` for diff display.

**Output:** `iterate` prints a structured JSON summary to stdout (status, manifest_path, benchmark_path, viewer_pid). The viewer runs in the background — `kill <viewer_pid>` to stop it when you're done.

**Crucial: do NOT read the run `transcript.jsonl` files into the main agent's context.** Each transcript is a stream of tool calls and reasoning events — tens of thousands of tokens per run. The grader (also a subprocess) reads each transcript; the main agent should only look at the script's summary JSON, the manifest, and per-run `grading.json`.

**Environment inheritance.** Env vars exported before launching the CLI (e.g. `ANTHROPIC_API_KEY=...`, `ANTHROPIC_BASE_URL=...`) propagate to all eval runs.

For case-specific environment variables (e.g., one case tests `FEATURE=A`, another tests `FEATURE=B`), set `env: {KEY: VALUE}` on the case in `evals.json`. For parallel-test isolation when the skill touches external mutable state (database, browser profile, webhook receiver, port, ...), see the advanced "Per-run setup" section in `references/evals-schema.md` — it's symptom-led with copy-paste recipes.

**Dashboard upload (silent).** When `SKILL_DASHBOARD_URL` and `SKILL_DASHBOARD_TOKEN` are set in the env, the aggregation step also POSTs the iteration to the dashboard. This is fail-soft — a network error never blocks the workflow but will print `[dashboard] upload failed: …` on stderr. Set `SKILL_DASHBOARD_DISABLED=1` to opt out.

### 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.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 `evals.json` with the assertions once drafted (do NOT hand-edit the per-run `eval_metadata.json` files — they're regenerated from `evals.json` on every script invocation). Also explain to the user what they'll see in the viewer — both the qualitative outputs and the quantitative benchmark.

If you launched `iterate` with `--phase executor` to interleave assertion-drafting, kick off grading once assertions are ready:

```bash
python -m scripts.cli iterate \
  --skill-path <path-to-skill> \
  --workspace <skill-name>-workspace \
  --iteration <N> \
  --phase grader --resume
```

The grader writes `grading.json` per run; it must use the fields `text`, `passed`, and `evidence` for each expectation — the viewer depends on these exact field names. For assertions that can be checked programmatically, the grader is instructed to write and run a script rather than eyeballing it — scripts are faster, more reliable, and can be reused across iterations.

### Step 3: Timing data — captured automatically

The runner writes `timing.json` to each run directory as soon as its executor exits, and backfills grader timings when the grader phase finishes. Fields (see `references/schemas.md` for the full schema):

```json
{
  "total_tokens": 84852,
  "duration_ms": 23332,
  "total_duration_seconds": 23.3,
  "executor_start": "...",
  "executor_end": "...",
  "executor_duration_seconds": 25.4,
  "grader_start": "...",
  "grader_end": "...",
  "grader_duration_seconds": 14.1
}
```

The top-level `total_tokens` / `duration_ms` / `total_duration_seconds` come from the subprocess's final `result` event; the `executor_*` / `grader_*` fields are wall-clock measurements bracketing each subprocess. The same data is also surfaced (per run) in `manifest.json`'s `runs[]` entries — handy if you want a quick scan of the iteration without walking every run dir.

### Step 4: Analyst pass and viewer

After `iterate` finishes:

1. **Read the manifest** — `cat <workspace>/iteration-N/manifest.json` gives a quick per-run scan (status, pass_rate, tokens, duration). Use this instead of crawling individual run dirs.

2. **Do an analyst pass** — read `benchmark.json` 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.

3. **The viewer is already running** (unless you used `--no-view`). Tell the user something like: "I've opened the results in your browser at http://localhost:3117. 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." For iteration 2+, pass `--previous-iteration <N-1>` to `iterate` so the viewer shows a diff against the prior run.

   **Cowork / headless environments:** Use `--no-view` and instead invoke the viewer manually with `--static <output_path>` to write a standalone HTML file. 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.

### 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-current", "feedback": "the chart is missing axis labels", "timestamp": "..."},
    {"run_id": "eval-1-current", "feedback": "", "timestamp": "..."},
    {"run_id": "eval-2-current", "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. The pid was printed in `iterate`'s stdout JSON as `viewer_pid`:

```bash
kill <viewer_pid> 2>/dev/null
```

### Advanced: dropping below `iterate`

`iterate` is a thin orchestrator over `better-skills`'s other subcommands. They stay supported for finer control:

- **Scaffold a skill's eval configs**: `python -m scripts.cli init <skill-path>` — writes a starter `evals.json` + `triggers.json`.
- **Just re-run the executor**: `python -m scripts.cli run --skill-path … --workspace … --iteration N --phase executor`. Use `--resume` to skip runs whose transcript already shows success.
- **Just re-grade** without re-executing (e.g., after editing assertions): `python -m scripts.cli run … --phase grader --resume`. Already-graded runs are skipped.
- **Re-aggregate** an existing iteration: `python -m scripts.cli aggregate <workspace>/iteration-N`. The iteration's `manifest.json` is required.
- **Manual dashboard upload**: `python -m scripts.upload_dashboard <workspace>/iteration-N --skill-name <name> --iteration N --skill-path <path>`. Requires `SKILL_DASHBOARD_URL` + `SKILL_DASHBOARD_TOKEN` env or `--dashboard-url`/`--token` flags.
- **Standalone viewer**: `python -m scripts.cli view <workspace>/iteration-N` — same as what `iterate` runs in the background.
- **Trigger pipeline** (test description triggering): `python -m scripts.cli trigger-loop --skill-path <path>` runs eval+improve loop. Underlying subcommands `trigger-eval` and `trigger-improve` are also exposed.

The `manifest.json` schema and `run_status.json` lifecycle are documented in `references/manifest-schema.md`. The new `evals.json` schema is in `references/evals-schema.md`.

---

## 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 each run's `transcript.jsonl` and notice if the subprocesses all independently wrote similar helper scripts or took the same multi-step approach. 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: `python -m scripts.cli iterate --skill-path … --workspace … --iteration <N+1> --previous-iteration <N>`. With `default_baseline: "previous"` (the recommended default), iteration `<N+1>` automatically compares against iteration `<N>`'s `skill-state/` snapshot — no manual baseline management. To compare against a different prior iteration for one run, pass `--baseline iteration-<K>`.
3. The viewer launches automatically with the previous iteration as the diff target
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 and most users won't need it — the human review loop is usually sufficient. The comparator/analyzer aren't wrapped by `better-skills` yet, so running them means spawning an executor CLI directly with the role specs from `agents/`.

---

## 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 to propose improvements based on what failed. It re-evaluates each new description on both train and test, iterating up to 5 times. When it's done, it opens an HTML report in the browser showing the results per iteration and returns JSON with `best_description` — selected by test score rather than train score to avoid overfitting.

### How skill triggering works

Understanding the triggering mechanism helps design better eval queries. Skills appear in Claude's `available_skills` list with their name + description, and Claude decides whether to consult a skill based on that description. The important thing to know is that Claude only consults skills for tasks it can't easily handle on its own — simple, one-step queries like "read this PDF" may not trigger a skill even if the description matches perfectly, because Claude can handle them directly with basic tools. Complex, multi-step, or specialized queries reliably trigger skills when the description matches.

This means your eval queries should be substantive enough that Claude would actually benefit from consulting a skill. Simple queries like "read file X" are poor test cases — they won't trigger skills regardless of description quality.

### Step 4: Apply the result

Take `best_description` from the JSON output and update the skill's SKILL.md frontmatter. Show the user before/after and report the scores.

---

### Package and Present (only if `present_files` tool is available)

Check whether you have access to the `present_files` tool. If you don't, skip this step. If you do, package the skill and present the .skill file to the user:

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

After packaging, direct the user to the resulting `.skill` file path so they can install it.

---

## Environments without subprocess capability

In Claude.ai (and any environment where you can't spawn an executor CLI), the core workflow is the same (draft → test → review → improve → repeat), but `iterate` and the optimization loops can't run. Here's what to adapt:

**Running test cases**: No subprocesses 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 subprocesses (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 independent subprocesses. 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 and blind comparison**: Both require subprocess execution. Skip them in this environment.

**Packaging**: The `package_skill.py` script works anywhere with Python and a filesystem. On Claude.ai, you can run it and the user can download the resulting `.skill` file.

**Updating an existing skill**: The user might be asking you to update an existing skill, not create a new one. In this case:
- **Preserve the original name.** Note the skill's directory name and `name` frontmatter field -- use them unchanged. E.g., if the installed skill is `research-helper`, output `research-helper.skill` (not `research-helper-v2`).
- **Copy to a writeable location before editing.** The installed skill path may be read-only. Copy to `/tmp/skill-name/`, edit there, and package from the copy.
- **If packaging manually, stage in `/tmp/` first**, then copy to the output directory -- direct writes may fail due to permissions.

---

## Cowork-Specific Instructions

If you're in Cowork, the main things to know are:

- You have an executor CLI (`claude` or `opencode`), so the main workflow (launch 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 runs subprocesses, not a browser, but please save it until you've fully finished making the skill and the user agrees it's in good shape.
- **Updating an existing skill**: The user might be asking you to update an existing skill, not create a new one. Follow the update guidance in the claude.ai section above.

---

## Reference files

The agents/ directory contains role specs the runner uses for the grader, comparator, and analyzer subprocesses.

- `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/evals-schema.md` — The `evals.json` (defaults + baseline grammar + cases) and `triggers.json` schema.
- `references/schemas.md` — Other JSON structures (grading.json, etc.).
- `references/manifest-schema.md` — The per-iteration `manifest.json` + `run_status.json` format that `iterate` writes and downstream scripts read.

---

Repeating one more time the core loop here for emphasis:

- Figure out what the skill is about
- Draft or edit the skill
- Run `iterate` on the 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!
