feedback-learning

# Feedback Learning System A complete pipeline for agents to learn from user feedback without spending tokens on analysis. ## Architecture ``` User feedback → detect-feedback.py → log-event.sh → events.jsonl ↓ weekly-report.py ← analyze-patterns.py ↓ patterns.json ↓ (≥3 occurrences) genes.json (promoted rules) ``` ## Setup ### 1. Install files Copy the skill contents to your shared learning directory: ```bash DEST="$HOME/.openclaw/shared/learning" mkdir -p "$DEST/reports" cp scripts/* "$DEST/" chmod +x "$DEST/log-event.sh" touch "$DEST/events.jsonl" ``` ### 2. Initialize data files If they don't exist, create empty JSON stores: ```bash cat > "$DEST/patterns.json" << 'EOF' {"version": "2.0", "updated": "", "patterns": []} EOF cat > "$DEST/genes.json" << 'EOF' {"version": "2.0", "rules": []} EOF cat > "$DEST/capsules.json" << 'EOF' {"version": "2.0", "capsules": []} EOF ``` ### 3. Create LEARNINGS.md for each agent Add to each agent's workspace: ```markdown # LEARNINGS.md **Last Updated:** YYYY-MM-DD **Total:** 0 ## 🟢 Что работает (положительный фидбек) (пока пусто) ## 🔴 Что НЕ работает (отрицательный фидбек) (пока пусто) ## 🧠 Извлечённые правила (пока пусто) ## 🔁 Повторяющиеся паттерны (пока пусто) ## 💡 Feature Requests (пока пусто) ``` ### 4. Add to AGENTS.md Add this block to each agent's AGENTS.md boot sequence: ```markdown ## Feedback Learning - On positive feedback (👍❤️🔥👏💯 or words like "круто","топ","зашло"): Run: `bash ~/.openclaw/shared/learning/log-event.sh <agent> positive user_emoji "<context>" "<signal>"` - On negative feedback (👎🤦😤 or words like "фигня","переделай"): Run: `bash ~/.openclaw/shared/learning/log-event.sh <agent> correction user_nlp "<context>" "<signal>" "<hint>"` - On exec errors: Run: `bash ~/.openclaw/shared/learning/log-event.sh <agent> error exec_fail "<context>" "<signal>" "<hint>"` ``` ### 5. Set up crons Pattern analysis (daily): ``` schedule: cron 30 3 * * * @ <timezone> payload: python3 ~/.openclaw/shared/learning/analyze-patterns.py ``` Weekly report (Sundays): ``` schedule: cron 30 4 * * 0 @ <timezone> payload: python3 ~/.openclaw/shared/learning/weekly-report.py ``` ## Usage ### Log an event manually ```bash bash log-event.sh anton error exec_fail "config update" "trailing comma in JSON" "Validate JSON before writing" bash log-event.sh anton positive user_emoji "sent report" "🔥" bash log-event.sh anton correction user_nlp "sent message" "переделай, не тот формат" "Confirm format before sending" ``` ### Detect feedback from text (no LLM) ```bash echo "круто, зашло!" | python3 detect-feedback.py # → {"type": "positive", "source": "user_nlp", "signal": "круто", "confidence": 0.8} python3 detect-feedback.py "переделай это" # → {"type": "correction", "source": "user_nlp", "signal": "переделай", "confidence": 0.8} ``` ### Run pattern analysis ```bash python3 analyze-patterns.py ``` Outputs: pattern count, promotion status. Updates `patterns.json`. Auto-promotes to `genes.json` when a pattern hits ≥3 occurrences in 30 days. ### Generate weekly report ```bash python3 weekly-report.py ``` Saves to `reports/WEEKLY_REPORT_YYYY_WNN.md` with stats by agent, source, top patterns, and newly promoted rules. ## Data Files | File | Purpose | |------|---------| | `events.jsonl` | Append-only event log (all feedback) | | `patterns.json` | Grouped recurring patterns with counts | | `genes.json` | Promoted rules (≥3 occurrences → active rule) | | `capsules.json` | Successful reasoning paths (avoid re-computation) | | `reports/` | Weekly synthesis reports | ## Event Schema ```json { "ts": "2026-03-20T12:00:00Z", "agent": "anton", "type": "error|correction|positive|pattern|requery", "source": "exec_fail|user_nlp|user_emoji|requery|auto", "context": "what agent was doing", "signal": "the trigger text or emoji", "hint": "suggested fix or rule", "heat": 1 } ``` ## Promotion Flow 1. Events accumulate in `events.jsonl` 2. `analyze-patterns.py` groups similar events by signal text (≥60% similarity) 3. Patterns with ≥3 occurrences in 30 days are promoted to `genes.json` 4. Agents read `genes.json` at boot to apply learned rules 5. `weekly-report.py` synthesizes progress for human review ## Supported Languages Feedback detection supports: - **Russian**: 20+ negative triggers, 19+ positive triggers, correction patterns - **English**: 10 negative, 8 positive triggers - **Emoji**: Universal positive/negative reactions