Setup & Installation
Or with the ClawHub CLI, for registry-managed skill folders outside a full OpenClaw workspace:
What This Skill Does
A structured methodology for improving code through repeated analyze-plan-mutate-verify-score-archive cycles, adapted from the ALMA research framework. Each cycle is logged in `.evolution/log.json`, so lessons from past iterations inform the next change. Designed for situations where ad-hoc fixes keep failing.
Unlike ad-hoc iteration, it enforces a maximum of 3 changes per cycle and requires every change to link to a specific observation, making it possible to attribute improvements or regressions accurately.
When to use it
- Debugging a web scraper that fails on 40% of target pages
- Optimizing a slow database query through multiple profiling rounds
- Improving an LLM prompt pipeline with structured variant tracking
- Breaking out of a debugging loop after two or more failed fix attempts
- Evolving an agent's memory design through principled experimentation
Example Workflow
Here's how your AI assistant might use this skill in practice.
User asks: improve a web scraper that fails on 40% of target pages
- 1Assess each component: marks parse_html() as Broken (crashes on pages without <article>), fetch_page() as Working, extract_links() as Fragile
- 2Plan one targeted change: add fallback selectors in parse_html() for pages missing <article>
- 3Mutate the code: add cascading selector logic trying <article>, then <main>, then <body>
- 4Verify: run the scraper against the same test pages, confirm no crashes
- 5Score and archive: pass rate rises from 40% to 72%, log the change and record the learned principle
Pass rate reaches 88% after a second cycle fixing relative URL resolution in extract_links(), with both cycles logged in .evolution/log.json