feedback-learning
# Feedback Learning System v2
A complete, zero-LLM pipeline for agents to learn from user feedback. Track what works, catch what doesn't, promote durable rules.
## Architecture
```
User feedback / exec error
↓
detect-feedback.py ←── error-catcher.sh (PostToolUse hook)
↓
log-event.sh ──────────────────────────────────────────→ events.jsonl
↓
analyze-patterns.py (nightly)
↓
patterns.json
(positive + negative patterns)
↓ (≥3 hits, delta test)
genes.json
(structured rules: condition→action)
↓
weekly-report.py (Sundays)
↓
reports/WEEKLY_*.md
```
## Quick Reference
| Situation | Action |
|-----------|--------|
| User gives positive feedback | `log-event.sh <agent> positive user_nlp "<ctx>" "<signal>"` |
| User corrects/complains | `log-event.sh <agent> correction user_nlp "<ctx>" "<signal>" "<lesson>"` |
| Exec command failed | `log-event.sh <agent> error exec_fail "<ctx>" "<stderr>" "<lesson>"` |
| Detect feedback from text | `python3 detect-feedback.py "переделай это"` |
| Run pattern analysis now | `python3 analyze-patterns.py` |
| Generate report now | `python3 weekly-report.py` |
| Check active rules (genes) | `python3 check-genes.py` |
| Mark gene as resolved | `python3 check-genes.py --resolve <gene_id>` |
## Setup
### 1. Install files
```bash
DIR="${FEEDBACK_LEARNING_DIR:-$HOME/.openclaw/shared/learning}"
mkdir -p "$DIR/reports"
cp scripts/* "$DIR/"
chmod +x "$DIR/log-event.sh" "$DIR/error-catcher.sh"
touch "$DIR/events.jsonl"
```
### 2. Initialize data files
```bash
DIR="${FEEDBACK_LEARNING_DIR:-$HOME/.openclaw/shared/learning}"
[ -f "$DIR/patterns.json" ] || cat > "$DIR/patterns.json" << 'EOF'
{"version": "2.1", "updated": "", "patterns": {"negative": [], "positive": []}}
EOF
[ -f "$DIR/genes.json" ] || cat > "$DIR/genes.json" << 'EOF'
{"version": "2.1", "rules": []}
EOF
[ -f "$DIR/capsules.json" ] || cat > "$DIR/capsules.json" << 'EOF'
{"version": "2.1", "capsules": []}
EOF
```
### 3. Add to AGENTS.md boot sequence
```markdown
## Feedback Learning
Before tasks: check `$FEEDBACK_LEARNING_DIR/genes.json` for applicable rules.
Auto-detect and log signals:
- Positive words/emoji → `bash $DIR/log-event.sh <agent> positive user_nlp "<ctx>" "<signal>"`
- Negative/correction → `bash $DIR/log-event.sh <agent> correction user_nlp "<ctx>" "<signal>" "<lesson>"`
- Exec fail (exit≠0) → `bash $DIR/log-event.sh <agent> error exec_fail "<ctx>" "<stderr[:200]>" "<lesson>"`
```
### 4. Set up crons
```
# Pattern analysis (nightly 3:30 AM)
schedule: cron 30 3 * * * @ Europe/Moscow
payload: python3 ~/.openclaw/shared/learning/analyze-patterns.py
# Weekly report (Sundays 4:00 AM)
schedule: cron 0 4 * * 0 @ Europe/Moscow
payload: python3 ~/.openclaw/shared/learning/weekly-report.py
```
### 5. (Optional) Hook integration for auto-error capture
For Claude Code / Codex hooks:
```json
{
"hooks": {
"PostToolUse": [{
"matcher": "Bash",
"hooks": [{"type": "command", "command": "bash ~/.openclaw/shared/learning/error-catcher.sh"}]
}]
}
}
```
## Usage
### Log events manually
```bash
DIR="${FEEDBACK_LEARNING_DIR:-$HOME/.openclaw/shared/learning}"
# Error
bash "$DIR/log-event.sh" anton error exec_fail \
"updating openclaw.json" "SyntaxError: trailing comma" \
"Always validate JSON with python3 -c before writing"
# Positive
bash "$DIR/log-event.sh" anton positive user_nlp \
"generated weekly report" "🔥 огонь!"
# Correction
bash "$DIR/log-event.sh" anton correction user_nlp \
"sent message in wrong format" "не так, в маркдауне давай" \
"Confirm output format before sending to Telegram"
```
### 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, не тот формат"
# → {"type": "correction", "source": "user_nlp", "signal": "переделай", "confidence": 0.8}
# Pipe mode for hook usage
echo "$TOOL_OUTPUT" | python3 detect-feedback.py --pipe | bash log-event.sh auto
```
### Check active rules before a task
```bash
python3 check-genes.py
# Lists active rules, signals stale ones
python3 check-genes.py --filter exec_fail
# Filter by type
python3 check-genes.py --resolve gene_20260310_120000_0
# Mark a resolved rule as inactive
```
## Data Files
| File | Purpose |
|------|---------|
| `events.jsonl` | Append-only event log (all feedback), deduped by content hash |
| `patterns.json` | Grouped patterns: BOTH positive and negative, with counts |
| `genes.json` | Promoted structured rules (condition → action → context) |
| `capsules.json` | Successful reasoning paths to avoid re-computation |
| `reports/` | Weekly synthesis reports |
## Event Schema
```json
{
"ts": "2026-03-20T12:00:00Z",
"id": "sha256_first8",
"agent": "anton",
"type": "error|correction|positive|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
}
```
## Gene (Promoted Rule) Schema v2
```json
{
"id": "gene_20260310_120000_0",
"status": "active|stale|resolved|wont-fix",
"origin": "original signal/pattern text",
"type": "error|correction|positive",
"condition": "When doing X",
"action": "Do Y instead of Z",
"context": "Additional context",
"agents": ["anton"],
"occurrences": 3,
"last_seen": "2026-03-20T...",
"promoted_at": "2026-03-20T...",
"expires": null,
"active": true
}
```
## Promotion Flow (v2)
1. Events accumulate in `events.jsonl` (deduped by hash)
2. `analyze-patterns.py` groups similar events (both positive AND negative)
3. Pattern hits ≥3 in 30 days → **Behavioral Delta Test**: would this rule change a future decision? If yes → promote.
4. Promoted gene has structured fields: `condition`, `action`, `context`
5. **Stagnation check**: if gene exists but same pattern keeps recurring → mark gene as `stale` and escalate
6. Genes auto-expire after 90 days of inactivity (no new events matching)
7. `weekly-report.py` includes gene health: active / stale / resolved counts
## Supported Languages
- **Russian:** 20+ negative, 19+ positive triggers, correction patterns
- **English:** 10 negative, 8 positive triggers
- **Emoji:** Universal positive/negative reactions
## What's New in v2
| Feature | v1 | v2 |
|---------|----|----|
| Positive pattern tracking | ❌ skipped | ✅ tracked separately |
| Gene structure | `"AVOID: key_text"` | `condition → action → context` |
| Gene lifecycle | active only | active / stale / resolved / wont-fix |
| Behavioral Delta Test | ❌ | ✅ promotes only if rule changes future behavior |
| Stagnation detection | ❌ | ✅ re-occurring genes flagged as stale |
| Path configuration | hardcoded | `$FEEDBACK_LEARNING_DIR` env var |
| Event deduplication | ❌ | ✅ content hash |
| Hook integration | ❌ | ✅ error-catcher.sh for PostToolUse |
| Gene check utility | ❌ | ✅ check-genes.py |
| Gene expiry | ❌ | ✅ 90-day inactivity auto-expire |
标签
skill
ai
