Auto-Summarization Skill
Proactive context management to prevent overflow and improve session quality.
Problem
- OpenClaw compaction triggers on context overflow (reactive)
- Compaction timeout (60s internal) causes fallback to pre-compaction state
- Tool loops (research with web_fetch) generate massive context without stopping
- No control over WHAT is preserved during compaction
Solution: Rolling Summarization
How It Works
Every N turns, create a concise summary of recent work and update SESSION-STATE.md. This keeps the session lean while preserving important context.
Trigger Thresholds
| Condition | Action |
|---|---|
| Every 15 turns | Create rolling summary |
| After 10 consecutive tool calls | Force summary (tool loop guard) |
| Context >70% estimated | Proactive summary + flush to memory |
Rolling Summary Format
Update SESSION-STATE.md with a condensed view:
## Rolling Summary (as of HH:MM)
- Completed: [what was done]
- In progress: [what's being worked on]
- Decisions: [key decisions made]
- Blockers: [anything blocking]
- Next: [what to do next]Integration with Existing Stack
- Hindsight auto-retain captures important facts before summary
- SESSION-STATE.md stores the rolling summary (always in context via bootstrap)
- Daily notes get the detailed version at end of session
- Working buffer becomes unnecessary if rolling summary works well
Anti-Patterns
- ❌ Don't summarize every turn (adds latency, wastes LLM calls)
- ❌ Don't duplicate Hindsight content (it already retains facts)
- ❌ Don't include routine operations (heartbeat checks, status pings)
- ✅ DO summarize: decisions, blockers, task progress, user preferences
- ✅ DO keep it under 200 chars per section
Prompt Addition (add to agents that need it)
After completing a task or every 15 turns:
1. Read SESSION-STATE.md
2. Update the Rolling Summary section with current state
3. Keep it concise (under 500 chars total)
4. This prevents context overflow and preserves continuityMetrics
| Metric | Before | After (target) |
|---|---|---|
| Context overflow/week | 1-2 | ~0 |
| Compaction timeout rate | ~50% | <20% |
| Context lost per session | High | Low |
| Additional LLM cost | $0 | ~$0.02/week |
Version
1.0.0 — Initial implementation