Auto-optimize — Agent Skill

Double-layer auto-optimization Agent Skill — host-agnostic: use with any IDE, CLI, or Skill-capable tool (OpenClaw, editor integrations, etc.), not tied to a single vendor.

One-liner

An Agent Skill workflow (entry: root SKILL.md + docs/): lock requirements first, iterate multiple Prompt phrasings in parallel, write each round to disk under tracks/prompt-*/, use an acceptance checklist (not self-score alone) as the delivery bar, and optionally hash-compare delivery vs the chosen final track file.

Why use it?

• Vague requirements → churn without a baseline
• Only one phrasing → no A/B on “how to ask”
• “Five iterations” with no files → not auditable
• Self-score 8/10 while features missing
• No proof delivery matches the “final” track file

This Skill addresses that with four phases and five verifiability rules (R1–R5).

Quick start

1. Keep this repo at your project root (or merge SKILL.md, program.md, docs/, scripts/ into an existing repo).
2. Optional: add host-specific rules (e.g. .cursor/rules/, OpenClaw config) to enforce the workflow.
3. Ask for features in your host; the agent should follow the phases when this Skill is loaded.
4. Verify from repository root:

FINAL_TRACK_FILE=tracks/prompt-a/r05.html DELIVERY_FILE=index.html bash scripts/skill-verify.sh

Layout

License

MIT — see LICENSE.