skill-creator

name	skill-creator
description	Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.

Skill Creator

Create and iteratively improve skills. Core loop: Draft → Test → Evaluate → Improve → Repeat.

Skill Structure

skill-name/
├── SKILL.md          (required: YAML frontmatter + markdown instructions)
└── Bundled Resources (optional)
    ├── scripts/      Executable code for deterministic/repetitive tasks
    ├── references/   Docs loaded into context as needed
    └── assets/       Templates, icons, fonts

SKILL.md targets: Under 500 lines (ideal). Put detail in references/. YAML requires name + description.

Description field: Primary trigger mechanism. Make it "pushy" — list specific contexts and phrases that should trigger the skill. Agents tend to under-trigger; compensate with explicit use-case coverage.

Workflow

1. Capture Intent

Extract from conversation: tools used, steps, corrections, input/output formats. Ask only what's missing:

• What should this skill enable?
• When should it trigger? (phrases, contexts)
• What's the expected output format?
• Are test cases needed? (yes for verifiable outputs; often no for subjective tasks)

2. Interview & Research

Ask about edge cases, input/output formats, success criteria, dependencies. Check available MCPs. Research in parallel via subagents when useful.

3. Write SKILL.md

Write draft, then review with fresh eyes. Prefer imperative form. Explain the why — smart agents follow reasoning over mandates. Avoid ALL-CAPS MUST whenever possible. Keep lean.

4. Create Test Cases

2–3 realistic prompts a real user would type. Save to evals/evals.json:

{"skill_name": "example", "evals": [{"id": 1, "prompt": "...", "expected_output": "..."}]}

5. Run & Evaluate

See agents/ for spawning test runs, grading, benchmarking, and launching the viewer:

• Spawn runs — one with-skill + one baseline per test case, all in same turn (with-skill AND baseline, never sequential)
• Draft assertions while runs are in progress
• Grade via agents/grader.md; aggregate via scripts/aggregate_benchmark
• Launch viewer: nohup python eval-viewer/generate_review.py <workspace>/iteration-N --skill-name "name" --benchmark <workspace>/iteration-N/benchmark.json > /dev/null 2>&1 &
• Read feedback from feedback.json after user reviews. Kill server after: kill $VIEWER_PID

6. Improve

Generalize from feedback → lean prompt → explain why → bundle repeated work (if all 3 test cases wrote create_chart.py, put it in scripts/). Iterate: improve → rerun in iteration-N+1/ → viewer with --previous-workspace → repeat.

7. Optimize Description (Optional)

After skill is done:

python -m scripts.run_loop --eval-set <path> --skill-path <path> --model <model-id> --max-iterations 5 --verbose

Generates 20 eval queries (10 should-trigger, 10 should-not-trigger). Review via assets/eval_review.html. Apply best_description to SKILL.md frontmatter.

Writing Principles

1. Generalize — rules preventing a class of problem, not just this example
2. Keep lean — remove instructions not pulling their weight
3. Explain why — "Read X before Y — prevents overwriting state" beats "ALWAYS read X"
4. Bundle repeated work — if multiple test runs independently wrote the same script, bundle it

Environment Notes

• Claude Code: Full workflow — subagents, viewer server, description optimization
• Claude.ai: Run tests inline, present results in conversation, skip benchmarks and description optimization
• Cowork: Use --static <output_path> for viewer (no display), feedback via downloaded feedback.json
• Updating existing skill: Preserve original directory name + name frontmatter. Copy to writable location before editing if installed path is read-only.

Reference Files

• agents/grader.md — grading assertions against outputs
• agents/comparator.md — blind A/B comparison
• agents/analyzer.md — analyzing why one version won + benchmark patterns
• references/schemas.md — JSON schemas for evals.json, grading.json, benchmark.json