Feedback Learning System#

A complete pipeline for agents to learn from user feedback without spending tokens on analysis.

Architecture#

text

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):

text

schedule: cron 30 3 * * * @ <timezone>
payload: python3 ~/.openclaw/shared/learning/analyze-patterns.py

Weekly report (Sundays):

text

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#

FilePurposeevents.jsonlAppend-only event log (all feedback)patterns.jsonGrouped recurring patterns with countsgenes.jsonPromoted rules (≥3 occurrences → active rule)capsules.jsonSuccessful 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