Claude-skill-registry extract

Extract decisions and learnings from Claude session transcripts. Triggers: "extract learnings", "process pending", SessionStart hook.

install
source · Clone the upstream repo
git clone https://github.com/majiayu000/claude-skill-registry
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/majiayu000/claude-skill-registry "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/data/extract" ~/.claude/skills/majiayu000-claude-skill-registry-extract && rm -rf "$T"
manifest: skills/data/extract/SKILL.md
source content

Extract Skill

Typically runs automatically via SessionStart hook.

Process pending learning extractions from previous sessions.

How It Works

The SessionStart hook runs:

ao extract

This checks for queued extractions and outputs prompts for Claude to process.

Manual Execution

Given

/extract
:

Step 1: Check for Pending Extractions

ao extract 2>/dev/null

Or check the pending queue:

cat .agents/ao/pending.jsonl 2>/dev/null | head -5

Step 2: Process Each Pending Item

For each queued session:

  1. Read the session summary
  2. Extract actionable learnings
  3. Write to
    .agents/learnings/

Step 3: Write Learnings

Write to:

.agents/learnings/YYYY-MM-DD-<session-id>.md

# Learning: <Short Title>

**ID**: L1
**Category**: <debugging|architecture|process|testing|security>
**Confidence**: <high|medium|low>

## What We Learned

<1-2 sentences describing the insight>

## Why It Matters

<1 sentence on impact/value>

## Source

Session: <session-id>

Step 4: Clear the Queue

ao extract --clear 2>/dev/null

Step 5: Report Completion

Tell the user:

  • Number of learnings extracted
  • Key insights
  • Location of learning files

The Knowledge Loop

Session N ends:
  → ao forge --last-session --queue
  → Session queued in pending.jsonl

Session N+1 starts:
  → ao extract (this skill)
  → Claude processes the queue
  → Writes to .agents/learnings/
  → Loop closed

Key Rules

  • Runs automatically - usually via hook
  • Process the queue - don't leave extractions pending
  • Be specific - actionable learnings, not vague observations
  • Close the loop - extraction completes the knowledge cycle