cpf-skill-creator

name

cpf-skill-creator

description

Create and improve Claude Code skills using a structured checkpointflow workflow with test-driven iteration. Use this skill whenever the user wants to create a skill, make a skill, build a skill, improve an existing skill, test a skill with evals, benchmark skill quality, optimize a skill description, or turn a conversation into a reusable skill. Also triggers on "skill for X", "automate this as a skill", "package this as a skill", and similar phrases. Always use this instead of the generic skill-creator when in the checkpointflow repo.

cpf-skill-creator

Create and improve Claude Code skills through a structured, workflow-driven process powered by checkpointflow.

How it works

This skill wraps the skill creation process in a cpf workflow (workflows/skill-creator.yaml) that orchestrates the full lifecycle: capture intent, draft, test, grade, iterate, optimize, and package. The workflow uses audience-driven dispatch — agent steps do the work automatically, user steps pause for human decisions.

Run the workflow via the cpf-workflow-runner skill or directly:

cpf run -f .claude/skills/cpf-skill-creator/skill-creator.yaml --input '{"skill_name": "my-skill", "intent": "..."}'

The workflow handles:

1. Capture intent (user) — Refine what the skill does and when it triggers
2. Research & draft (agent) — Create the SKILL.md and test cases
3. Review draft (user) — Approve, revise, or cancel
4. Run tests & grade (agent) — Execute evals, grade, benchmark, launch viewer
5. Review results (user) — Examine outputs, decide to iterate/optimize/ship
6. Improve skill (agent) — Apply feedback, rewrite (loops back to step 4)
7. Optimize description (agent) — Tune triggering accuracy
8. Package (agent) — Create .skill file for distribution

Reference material: Anthropic skill-creator

This skill delegates to utilities from the official Anthropic skill-creator plugin rather than duplicating them. The reference path is:

~/.claude/plugins/cache/claude-plugins-official/skill-creator/unknown/skills/skill-creator/

Available utilities at that path:

Path	Purpose
scripts/aggregate_benchmark.py	Aggregate grading into benchmark.json
scripts/package_skill.py	Package skill as .skill file
scripts/run_loop.py	Description optimization loop
scripts/run_eval.py	Run trigger evaluation
eval-viewer/generate_review.py	Generate HTML review viewer
agents/grader.md	Grading instructions for subagents
agents/analyzer.md	Benchmark analysis instructions
agents/comparator.md	Blind A/B comparison instructions
assets/eval_review.html	Template for trigger eval review UI
references/schemas.md	JSON schemas for evals, grading, benchmark

When running Python scripts from the reference path, set the working directory to the skill-creator root so module imports resolve correctly:

cd ~/.claude/plugins/cache/claude-plugins-official/skill-creator/unknown/skills/skill-creator
python -m scripts.aggregate_benchmark <workspace>/iteration-1 --skill-name my-skill

If the reference path doesn't exist (plugin not installed), fall back to manual operations — grade inline, skip the viewer, and package by hand.

Skill writing principles

These principles apply when drafting or improving skills in the agent steps:

Description field is king. It's the primary triggering mechanism. Include both what the skill does AND specific contexts/phrases. Claude tends to under-trigger, so make descriptions slightly "pushy" — enumerate trigger phrases generously.

Progressive disclosure. Keep SKILL.md under 500 lines. Put detailed reference material in references/ subdirectories. Put scripts in scripts/. The model reads SKILL.md on trigger and only loads bundled resources when needed.

Explain the why. Today's LLMs respond better to reasoning than to rigid ALWAYS/NEVER rules. Explain why each instruction matters so the model can generalize to edge cases.

Examples over abstractions. Include concrete input/output examples. They're worth more than paragraphs of description.

Look for repeated patterns. If test runs independently produce similar helper scripts, bundle that script into the skill. Write it once in scripts/ and reference it.

Iteration philosophy

When improving a skill after user feedback:

• Generalize from the specific feedback. The skill will be used across many prompts, not just the test cases. Don't overfit.
• Keep it lean. Remove instructions that aren't pulling their weight. Read the test transcripts — if the skill is making the model waste time, cut those parts.
• Explain the why. If you find yourself writing ALL-CAPS MUST/NEVER, reframe as reasoning the model can internalize.
• Draft, then revise. Write the improvement, then read it fresh and tighten it.

Workspace layout

Test results and artifacts are organized in <skill-name>-workspace/ alongside the skill:

<skill-name>-workspace/
  evals/
    evals.json                    # Test prompts and assertions
  iteration-1/
    eval-0-<descriptive-name>/
      with_skill/
        outputs/                  # Files produced with the skill
        grading.json              # Assertion pass/fail results
        timing.json               # Tokens and duration
      without_skill/
        outputs/                  # Baseline outputs
        grading.json
        timing.json
      eval_metadata.json          # Prompt, assertions for this eval
    benchmark.json                # Aggregated metrics
    benchmark.md                  # Human-readable summary
    feedback.json                 # User feedback from the viewer
  iteration-2/
    ...

Quick start for improving an existing skill

If the user has an existing skill they want to improve, pass it via the existing_skill_path input:

cpf run -f workflows/skill-creator.yaml --input '{
  "skill_name": "my-skill",
  "intent": "Improve test coverage and fix edge case handling",
  "existing_skill_path": ".claude/skills/my-skill"
}'

The workflow will snapshot the existing skill for baseline comparison, then iterate.

Tips

• The workflow handles orchestration; each agent step does the actual work (reading code, writing files, running tools).
• Always use generate_review.py from the reference path — never write custom HTML for the viewer.
• For headless environments, pass --static <output.html> to generate_review.py.
• The --previous-workspace flag on generate_review.py enables iteration-over-iteration comparison.
• If subagents aren't available, run test cases inline (less rigorous but still useful with human review).
• Description optimization requires the claude CLI (claude -p). Skip it if not available.