Setup & Installation
Or with the ClawHub CLI, for registry-managed skill folders outside a full OpenClaw workspace:
What This Skill Does
Techniques for compressing agent conversation history without losing critical information. Covers three production-ready approaches: anchored iterative summarization, opaque compression, and regenerative full summaries. The core insight is optimizing tokens-per-task rather than tokens-per-request, since overly aggressive compression forces costly re-fetching.
Targeting tokens-per-task instead of tokens-per-request avoids the hidden cost where compression that saves 0.5% more tokens causes 20% more re-fetching, resulting in higher total token spend.
When to use it
- Compressing a 100+ message debugging session before hitting context limits
- Tracking which files were modified across a multi-hour coding agent session
- Designing summarization triggers for codebases over 5M tokens
- Evaluating compression quality with probe-based recall tests
- Reducing total token spend on long-running agentic workflows
Example Workflow
Here's how your AI assistant might use this skill in practice.
User asks: Compress this 89,000-token debugging session so the agent can continue without losing context
- 1Identify the compression trigger point at 70-80% context utilization
- 2Define structured summary sections: Session Intent, Files Modified, Decisions Made, Current State, Next Steps
- 3Summarize the truncated history span into each explicit section
- 4Merge the new summary into any existing structured summary rather than regenerating from scratch
- 5Validate retention by running probe questions against the compressed output
A structured markdown summary preserving the original error, all modified file paths, root cause, test status, and next steps — compressing 89,000 tokens to roughly 300 words