Localized reading guide
skill-creatorPurpose, usage and source
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.
Source retrieved:
- Source repository
- anthropics/skills
- SKILL.md
skills/skill-creator/SKILL.md- Repository stars
- 185,373
- Repository updated
- 2026-10-29
- Categories
- Agent workflows
- Last collected
License: License file needs review
Workflow excerpts are reading aids, not executable instructions from Codez. No third-party Skill was run to produce this page.
The purpose and workflow passages below are upstream material, organized and translated for reading. They are distinct from the separately labeled Codez editorial analysis.
What this Skill does
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.
How the upstream workflow works
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.
Upstream passage · L296 ↗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. 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-snapshot/), then point the baseline subagent at the snapshot. Save to old_skill/outputs/.
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.
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)Requirements and setup
No short requirements passage was selected from this file. This does not mean the Skill has no dependencies. Check the repository setup instructions and referenced files.
Limits and quality checks
No separate limits passage was selected from this file. This does not mean the Skill is safe for every input or environment. Review the complete source and validate your own result.
Start with a safe trial
- Check the supported host and the repository installation instructions. Keep the Skill directory and its referenced scripts, assets and documentation together; SKILL.md alone may omit dependencies.
- Give the agent your real input, desired output and acceptance criteria. Invocation syntax and available tools depend on the host; this site does not assume a universal install command.
- Try the source workflow in a disposable project or on a copy. Review operations that change files, remote services or billable resources before allowing them.
- Validate the output against the source quality checks and your own requirements. A successful trigger or a high repository Star count does not establish a correct result.
Read further in the source
- Skill Creator
- Communicating with the user
- Capture Intent
- Interview and Research
- Write the SKILL.md
- Progressive Disclosure
- Principle of Lack of Surprise
- Writing Patterns
- Writing Style
- Test Cases
- Running and evaluating test cases
- Step 1: Spawn all runs (with-skill AND baseline) in the same turn
- Step 2: While runs are in progress, draft assertions
- Step 3: As runs complete, capture timing data
- Step 4: Grade, aggregate, and launch the viewer
- What the user sees in the viewer
- Step 5: Read the feedback
- Improving the skill
- How to think about improvements
- The iteration loop
- Advanced: Blind comparison
- Description Optimization
- Step 1: Generate trigger eval queries
- Step 2: Review with user
- Step 3: Run the optimization loop
- How skill triggering works
- Step 4: Apply the result
- Package and Present (only if
present_filestool is available) - Claude.ai-specific instructions
- Cowork-Specific Instructions
Related Skills
Translation and scope
This reading guide selects passages from the pinned upstream SKILL.md. Translation is assisted. It is not a complete manual or a claim of tested compatibility. Read the original and its referenced files before use.
This introduction refers to the pinned version below; current repository activity and collection time are shown separately.
- Pinned source
- Read the original ↗
- Source retrieved
- Source commit
ca45fc496a3436251f191354091d557da31c15b5- Source SHA-256
dcd4803e61e913e6fc27294184cd3a71f09f5e924ff20c8a9a20173e7b3c2bcf