forge

name	forge
description	Mine transcripts for knowledge - decisions, learnings, failures, patterns. Triggers: "forge insights", "mine transcripts", "extract knowledge".

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