forge

---

name: forge description: 'Mine transcripts into learnings. Triggers: "forge", "mine transcripts into learnings.", "forge skill".' practices:

• wiki-knowledge-surface
• lean-startup hexagonal_role: domain consumes: [] produces:
• .agents/research/*.md context_rel:
• kind: shared-kernel with: standards skill_api_version: 1 user-invocable: false context: window: fork intent: mode: task sections: exclude:
  • TASK intel_scope: full metadata: tier: background dependencies: [] internal: true output_contract: .agents/learnings/.md, .agents/patterns/.md

---

Forge Skill

Cross-vendor analog: the capture half of Anthropic Managed Agents' memory + dreaming pair (May 2026). Forge mines transcripts; $curate --mode=dream curates between sessions. Off the API, local, cross-vendor.

Typically runs automatically via SessionEnd hook.

Loop position: capture sub-step of move 7 in the operating loop. Extracts candidate learnings from transcripts; the promotion ratchet decides which ones survive (one-offs die at handoff; repeats promote to .agents/learnings/). Forge is the funnel, not the filter.

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.

Flags

Flag	Default	Description
--promote	off	Process pending extractions from .agents/knowledge/pending/ and promote to .agents/learnings/. Absorbs the former extract skill.

Promote Mode

Given /forge --promote:

Promote Step 1: Find Pending Files

ls -lt .agents/knowledge/pending/*.md 2>/dev/null
ls -lt .agents/ao/pending.jsonl 2>/dev/null

If no pending files found, report "No pending extractions" and exit.

Promote Step 2: Process Each Pending File

For each file in .agents/knowledge/pending/:

1. Read the file content
2. Validate it has required fields (# Learning:, **Category**:, **Confidence**:)
3. Copy to .agents/learnings/ (preserving filename)
4. Remove the source file from .agents/knowledge/pending/

Promote Step 3: Process Pending Queue

if [ -f .agents/ao/pending.jsonl ] && [ -s .agents/ao/pending.jsonl ]; then
  # Process each queued session
  cat .agents/ao/pending.jsonl
  # After processing, clear the queue
  > .agents/ao/pending.jsonl
fi

Promote Step 4: Report

Promoted N learnings from pending → .agents/learnings/
Queue cleared.

Done. Return immediately after reporting.

---

Manual Execution

Given /forge [path]:

Step 1: Identify Transcript

With ao CLI:

# Mine recent sessions
ao forge transcript --last-session

# Mine specific transcript
ao forge transcript <path>

Without ao CLI: Look at recent conversation history and extract learnings manually.

Step 2: Extract Knowledge Types

Read skills/forge/references/uncaptured-lesson-patterns.md for signal patterns and the 26 known uncaptured lesson categories.

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

Uncaptured Lesson Matching: During transcript scanning, match events against the 26 known uncaptured lesson patterns (see references/uncaptured-lesson-patterns.md). Pre-fill learning templates with matched pattern metadata (category, base confidence, pattern number tag).

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

if command -v ao &>/dev/null; then
  ao forge markdown .agents/forge/YYYY-MM-DD-forge.md 2>/dev/null
else
  # Without ao CLI: auto-promote high-confidence candidates to learnings
  mkdir -p .agents/learnings .agents/ao
  for f in .agents/forge/YYYY-MM-DD-*.md; do
    [ -f "$f" ] || continue
    # Extract confidence (numeric or categorical)
    CONF=$(grep -i "confidence:" "$f" | head -1 | awk '{print $NF}')
    # Normalize categorical to numeric: high=0.9, medium=0.6, low=0.3
    case "$CONF" in
      high) CONF_NUM=0.9 ;; medium) CONF_NUM=0.6 ;; low) CONF_NUM=0.3 ;; *) CONF_NUM=$CONF ;;
    esac
    # Auto-promote if confidence >= 0.7, prepending required frontmatter
    if (( $(echo "$CONF_NUM >= 0.7" | bc -l) )); then
      { printf -- '---\ntype: learning\nsource: forge\ndate: %s\nmaturity: provisional\nutility: 0.5\n---\n' "$(date +%Y-%m-%d)"; cat "$f"; } > .agents/learnings/"$(basename "$f")"
      TITLE=$(head -1 "$f" | sed 's/^# //')
      echo "{\"file\": \".agents/learnings/$(basename $f)\", \"title\": \"$TITLE\", \"keywords\": [], \"timestamp\": \"$(date -Iseconds)\"}" >> .agents/ao/search-index.jsonl
      echo "Auto-promoted (confidence $CONF): $(basename $f)"
    fi
  done
  echo "Forge indexing complete (ao CLI not available — high-confidence candidates auto-promoted)"
fi

Step 5: Update Capture Tracking

After extracting learnings that match uncaptured lesson patterns (Step 2), record which patterns were captured. This state lives in .agents/forge/capture-tracking.json (a runtime artifact, never in skills/).

mkdir -p .agents/forge

1. Read .agents/forge/capture-tracking.json if it exists, otherwise start with {}
2. For each matched pattern, add or update an entry keyed by pattern number:

   {
     "3": {"captured": true, "date": "2026-03-30", "learning_path": ".agents/learnings/tooling/use-bin-cp.md"},
     "7": {"captured": true, "date": "2026-03-29", "learning_path": ".agents/learnings/operations/worktree-commit.md"}
   }
   

3. Write the updated JSON back to .agents/forge/capture-tracking.json

Pattern numbers correspond to the numbered headings in references/uncaptured-lesson-patterns.md (1-30, 26 total patterns).

Step 6: Report Results

Tell the user:

• Number of items extracted by type
• Location of forge output
• Candidates ready for promotion to learnings
• Capture progress: "X/26 uncaptured lesson patterns captured" (read from .agents/forge/capture-tracking.json)

The Quality Pool

Forged candidates enter at Tier 0 (.agents/forge/), then promote to Tier 1 (.agents/learnings/) via human review, 2+ citations, or auto-promote when confidence >= 0.7 (ao-free fallback).

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

Examples

See references/examples.md for the SessionEnd hook invocation walkthrough and manual transcript-mining walkthrough.

Troubleshooting

Problem	Cause	Solution
No extractions found	Transcript lacks knowledge signals or ao CLI unavailable	Check transcript contains decisions/learnings; verify ao CLI installed
Low confidence scores	Weak signals or vague conversation	Focus sessions on concrete decisions and explicit learnings
forge --queue fails	CLI not available or permission error	Manually append to .agents/ao/pending.jsonl with session metadata
Duplicate forge outputs	Same session forged multiple times	Check forge filenames before writing; ao CLI handles dedup automatically

Reference Documents

• references/forge.feature — Executable spec: mine transcripts → queued candidates, funnel-not-filter, --promote, consumes-transcripts-not-skills (soc-qk4b)
• references/uncaptured-lesson-patterns.md — signal patterns and 26 known uncaptured lesson categories for transcript mining
• references/feedback-compiler-drafts.md — auto-drafted learning workflow (F5.4 fail->pass ledger transitions, cli/internal/feedbackcompiler)