Setup & Installation
Or with the ClawHub CLI, for registry-managed skill folders outside a full OpenClaw workspace:
What This Skill Does
Monitors AI agent context window consumption and enforces policies for sub-agent spawning, compaction preparation, and post-compaction recovery. Tracks usage thresholds, estimates remaining runway, and profiles session patterns to recommend config adjustments.
Unlike general memory tools, this skill targets context window mechanics specifically — spawn thresholds, compaction checkpoints, and config tuning — preventing the silent quality degradation that happens before hard limits are hit.
When to use it
- Checking how much context runway remains mid-session
- Deciding whether to spawn a sub-agent for a tool-heavy task
- Writing a structured checkpoint before triggering compaction
- Recovering working state after compaction or starting a new session
- Tuning context compression and pruning settings based on work style
Example Workflow
Here's how your AI assistant might use this skill in practice.
User asks: What's eating my context and how much runway do I have?
- 1Run session_status to get current window size and usage percentage
- 2Estimate breakdown across baseline, messages, tool call results, and compaction summaries
- 3Classify session pattern as tool-heavy, conversational, mixed, or bursty
- 4Report usage as a range with threshold-based recommendations
Context usage breakdown with runway estimate and a recommendation to spawn sub-agents or compact based on current percentage