Agent skill
forge
Mine transcripts for knowledge - decisions, learnings, failures, patterns. Triggers: "forge insights", "mine transcripts", "extract knowledge".
Install this agent skill to your Project
npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/data/forge
SKILL.md
Forge Skill
Typically runs automatically via SessionEnd hook.
Extract knowledge from session transcripts.
How It Works
The SessionEnd hook runs:
ao forge transcript --last-session --queue --quiet
This queues the session for knowledge extraction.
Manual Execution
Given /forge [path]:
Step 1: Identify Transcript
With ao CLI:
# Mine recent sessions
ao forge --recent
# Mine specific transcript
ao forge transcript <path>
Without ao CLI: Look at recent conversation history and extract learnings manually.
Step 2: Extract Knowledge Types
Look for these patterns in the transcript:
| Type | Signals | Weight |
|---|---|---|
| Decision | "decided to", "chose", "went with" | 0.8 |
| Learning | "learned that", "discovered", "realized" | 0.9 |
| Failure | "failed because", "broke when", "didn't work" | 1.0 |
| Pattern | "always do X", "the trick is", "pattern:" | 0.7 |
Step 3: Write Candidates
Write to: .agents/forge/YYYY-MM-DD-forge.md
# Forged: YYYY-MM-DD
## Decisions
- [D1] <decision made>
- Source: <where in conversation>
- Confidence: <0.0-1.0>
## Learnings
- [L1] <what was learned>
- Source: <where in conversation>
- Confidence: <0.0-1.0>
## Failures
- [F1] <what failed and why>
- Source: <where in conversation>
- Confidence: <0.0-1.0>
## Patterns
- [P1] <reusable pattern>
- Source: <where in conversation>
- Confidence: <0.0-1.0>
Step 4: Index for Search
ao forge index .agents/forge/YYYY-MM-DD-forge.md 2>/dev/null
Step 5: Report Results
Tell the user:
- Number of items extracted by type
- Location of forge output
- Candidates ready for promotion to learnings
The Quality Pool
Forged candidates enter at Tier 0:
Transcript → /forge → .agents/forge/ (Tier 0)
↓
Human review or 2+ citations
↓
.agents/learnings/ (Tier 1)
Key Rules
- Runs automatically - usually via hook
- Extract, don't interpret - capture what was said
- Score by confidence - not all extractions are equal
- Queue for review - candidates need validation
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