Mine Transcripts

name: mine-transcripts description: > Mine real human CLI conversation transcripts into labeled training data for bridge/classifier improvement. triggers:

• mine transcripts
• extract training data
• mine conversations allowed-tools:
• Bash
• Read metadata: short-description: Mine CLI transcripts for classifier training

provides: - mine-transcripts composes: [, task-monitor]

mine-transcripts

Mine real human conversations from CLI agents for bridge classifier training.

Purpose

Train the bridge classifier on REAL human communication patterns, not synthetic templates. This enables personas like Embry to find the RIGHT experts when using /ask.

Two-tier training approach:

1. Developer (Graham) - baseline attunement to real communication patterns
2. Client - specific adaptation to individual users

Integration

┌─────────────────────────────────────────────────────────────────┐
│                    DATA SOURCES                                 │
├─────────────────────────────────────────────────────────────────┤
│  ~/.claude/projects/     Claude CLI conversations              │
│  ~/.codex/history.jsonl  Codex CLI (pure human input!)         │
│  ~/.codex/sessions/      Codex session transcripts             │
│  ~/.gemini/              Gemini CLI (if exists)                │
│  ~/.pi/                  Pi CLI (if exists)                    │
└─────────────────────────────────────────────────────────────────┘
                              │
                              ▼
┌─────────────────────────────────────────────────────────────────┐
│                    /mine-transcripts                            │
├─────────────────────────────────────────────────────────────────┤
│  1. Extract real human messages (filter system prompts)        │
│  2. Label with /taxonomy bridge extraction                     │
│  3. Detect emotional state (satisfied/frustrated)              │
│  4. Deduplicate against /memory                                │
│  5. Store unique examples for classifier training              │
└─────────────────────────────────────────────────────────────────┘
                              │
                              ▼
┌─────────────────────────────────────────────────────────────────┐
│              /create-classifier (bridge classifier)            │
│                              │                                  │
│                              ▼                                  │
│                      persona_router.py                          │
│                              │                                  │
│                              ▼                                  │
│                    Embry /asks the RIGHT personas               │
└─────────────────────────────────────────────────────────────────┘

Dependencies

• /taxonomy - Bridge label extraction (Precision, Resilience, Fragility, Corruption, Loyalty, Stealth)
• /memory - Deduplication against existing lessons, optional storage
• /episodic-archiver - Emotional context from archived sessions
• /scheduler - Nightly runs

Usage

# Mine from all CLI agents
./run.sh mine --all-agents

# Mine with deduplication against existing training data
./run.sh mine --all-agents --dedupe

# Mine and store to memory (creates lessons)
./run.sh mine --all-agents --store-memory

# Analyze coverage of existing training data
./run.sh analyze data/mined.jsonl

# Export for human review
./run.sh export --sample 500 --output for_review.jsonl

Output

Training data in JSONL format:

{"text": "the font size is too small for 10ft viewing", "labels": ["Precision", "Fragility"]}
{"text": "perfect, that fixed the issue!", "labels": ["Resilience", "Loyalty"]}

Emotional Context

Messages are enriched with emotional detection:

• Satisfied signals → Resilience, Loyalty bridges
• Frustrated signals → Fragility bridge
• High satisfaction → Both Resilience AND Loyalty

This helps the classifier understand that "works great!" indicates system resilience and good collaboration (Loyalty).

Scheduler Integration

Registered as transcript-mining-nightly:

• Runs at 4:30am daily
• Deduplicates against existing training data
• Feeds into bridge-classifier-retrain at 5am

Triggers

• mine transcripts
• extract training data
• mine conversations
• Nightly via /scheduler