extract-skills

Extract Skills

When to use this skill

Reads classified decisions from _analysis/full_report.md and transforms them into generic, framework-agnostic skill files ready for the skill registry.

Zero skills is a valid outcome. Not every codebase contains extractable patterns. Do not force extraction — it is better to report nothing than to produce generic noise.

Prerequisites

{CODEBASE_ROOT}/_analysis/full_report.md must exist. If it doesn't, stop immediately and tell the user to run /analyze-agent-codebase (Claude Code) or @analyze-agent-codebase (Cursor) first.

All output goes to {CODEBASE_ROOT}/_analysis/extracted_skills_codebase/.

Instructions

Phase 1: Locate and read Section 14

Do not read full_report.md in full — it is a concatenation of all cluster files and is large. Section 14 is the only input needed for extraction.

First check whether _analysis/synthesis.md exists:

test -f _analysis/synthesis.md && echo "EXISTS" || echo "NOT_FOUND"

If synthesis.md exists: read _analysis/synthesis.md directly. It is the source of Section 14 and is much smaller than the assembled report. Skip the grep step.

If synthesis.md does not exist: locate Section 14 in full_report.md without loading the whole file:

grep -n "SECTION 14" _analysis/full_report.md | head -1

Read full_report.md starting from the line number returned by grep to EOF. Do not read any content before that line.

Section 14 contains:

• 14A: Classified decisions (tagged UNIVERSAL, ENGINEERING, or FRAMEWORK-SPECIFIC)
• 14B: Anti-patterns (missing, disabled, or broken implementations)
• 14C: Design trade-offs (decision frameworks with context-dependent options)

Drop all FRAMEWORK-SPECIFIC decisions.

For remaining UNIVERSAL and ENGINEERING decisions, apply the "would you say this unprompted" test at the decision level, not the concept level:

• Drop if you would give the specific decision with its specific failure mode unprompted (e.g., "use environment variables for secrets," "add retry logic").
• Keep if you know the general concept but not the specific production decisions within it. Example: You know "LLM-as-judge" exists, but do not know that you need model diversity between judge and production model to avoid shared blind spots, or that evaluation scores should be written to tracing spans for regression detection.