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anthropics / skill-creator

Skill Creator

This runbook guides an AI agent through the complete lifecycle of creating, testing, iteratively improving, and packaging AI agent skills. Starting from intent capture and research, the agent drafts a SKILL.md, writes test cases, runs parallel evaluations…

agent claude-codemodel anthropic/claude-sonnet-4.6snapshot python312-uveval programmatic11 stepsv1.0.0

Deploy Skill Creator to your jetty.io

One-click installs this runbook into a collection on your Jetty account. You can run it from the Spot dashboard, schedule it, or pipe inputs in via the API.

Deploy on jetty.io →View source
The shape of the run

11 steps · start to finish.

  1. 1
    Step 1

    Environment Setup

    ▶

    Verify the environment has all required tools and the skill path is valid.

    # Check Python is available
python3 --version || { echo "ERROR: python3 not found"; exit 1; }

# Check claude CLI (needed for description optimization)
command -v claude >/dev/null 2>&1 && echo "claude CLI present" || echo "WARN: claude CLI not found (description optimization will be unavailable)"

# Verify skill path exists or create it
SKILL_NAME="${SKILL_NAME:-my-skill}"
SKILL_PATH="${SKILL_PATH:-./$SKILL_NAME}"
mkdir -p "$SKILL_PATH"
mkdir -p "$SKILL_PATH/evals"
mkdir -p "${SKILL_NAME}-workspace"
mkdir -p /app/results

echo "Setup complete. Skill path: $SKILL_PATH"
  2. 2
    Step 2

    Capture Intent

    ▶

    Understand the user's intent before writing anything. If the current conversation already contains a workflow the user wants to capture (e.g., they say "turn this into a skill"), extract answers from…

  3. 3
    Step 3

    Interview and Research

    ▶

    Before writing the SKILL.md, proactively gather:

  4. 4
    Step 4

    Write the SKILL.md

    ▶

    Based on the user interview, produce <skill-path>/SKILL.md with this structure:

  5. 5
    Step 5

    Write Test Cases

    ▶

    After drafting the skill, create 2–3 realistic test prompts — the kind of message a real user would actually send. Share them with the user for confirmation before running.

  6. 6
    Step 6

    Run & Evaluate (max 5 iterations)

    ▶

    This is one continuous sequence — do not stop partway through.

  7. 7
    Step 7

    Iterate on Errors (max 3 rounds)

    ▶

    After reading feedback, improve the skill and re-run:

  8. 8
    Step 8

    Advanced — Blind Comparison (Optional)

    ▶

    For rigorous comparison between two skill versions, use the blind comparison system in agents/comparator.md and agents/analyzer.md. An independent agent evaluates both outputs without knowing which…

  9. 9
    Step 9

    Description Optimization

    ▶

    After finalizing the skill body, offer to optimize the description for better triggering accuracy.

  10. 10
    Step 10

    Package and Present

    ▶

    If the present_files tool is available:

  11. 11
    Step 11

    Environment-Specific Adaptations

    ▶

    No subagents: run test cases sequentially, executing SKILL.md instructions inline. Skip baseline runs. - No browser: skip generate_review.py. Present results directly in conversation; ask for…