Setup & Installation
Or with the ClawHub CLI, for registry-managed skill folders outside a full OpenClaw workspace:
What This Skill Does
Covers strategies for compressing agent conversation history when sessions exceed context window limits. Three production-ready approaches are compared: anchored iterative summarization, opaque compression, and regenerative full summaries. The core metric is tokens-per-task rather than tokens-per-request.
Optimizing for tokens-per-task rather than tokens-per-request avoids the hidden cost of re-fetching lost context, which naive compression strategies miss entirely.
When to use it
- Preventing coding agents from forgetting modified files mid-session
- Handling codebases larger than the model context window
- Designing summarization pipelines for long-running agent workflows
- Evaluating compression quality with probe-based testing
- Tuning compression trigger thresholds for multi-phase tasks
Example Workflow
Here's how your AI assistant might use this skill in practice.
User asks: compress the current 90,000-token debugging session before continuing
- 1Identify the structured summary sections: Session Intent, Files Modified, Decisions Made, Current State, Next Steps
- 2Summarize only the newly-truncated span of conversation history into those sections
- 3Merge the new section content into the existing persistent summary rather than regenerating from scratch
- 4Verify artifact trail completeness by checking that all read and modified files are listed
- 5Run probe-based checks asking factual questions to confirm critical details were retained
Compressed structured summary under 2,000 tokens, with full file tracking and decision history preserved, ready to continue the session