Setup & Installation
Or with the ClawHub CLI, for registry-managed skill folders outside a full OpenClaw workspace:
Version History
- Updated the description in SKILL.md from Portuguese to English for broader accessibility. - No functional changes to code or workflow; only documentation was modified.
What This Skill Does
Manages conversation token usage by summarizing history, extracting pending actions, and writing a compact briefing file before each model call. A Python script reads a plain-text exchange log and outputs a replacement context document. Run it before each prompt instead of resending the full thread.
Rather than trimming the raw conversation ad hoc, it produces a structured, size-bounded briefing that separates decisions, pending tasks, and recent turns into predictable sections.
When to use it
- Pruning a bloated multi-turn agent thread before the next API call
- Tracking open TODOs and follow-ups across a long session
- Reducing token costs on repetitive agentic workflows
- Injecting a one-page decision summary into a fresh prompt
- Auditing what the model knew at each turn via versioned summary files
Example Workflow
Here's how your AI assistant might use this skill in practice.
User asks: Summarize our Q2 planning session and keep the next prompt lean
- 1Append the latest user and assistant messages to context/history.txt in ROLE: text format
- 2Run context_gatekeeper.py with --history and --summary flags
- 3Script produces context/current-summary.md with up to 6 summary sentences, open action items, and the last 4 turns
- 4Inject the summary file content at the top of the next prompt
- 5Proceed with the API call using only the briefing plus 2-3 recent messages if needed
A context/current-summary.md file containing a condensed briefing, pending task list, and recent exchange snippet ready for injection