context-budgeting

Manage and optimize OpenClaw context window usage via partitioning, pre-compression checkpointing, and information lifecycle management. Use when the session context is near its limit (>80%), when the agent experiences "memory loss" after compaction, or when aiming to reduce token costs and latency for long-running tasks.

作者: admin | 来源: ClawHub

# Context Budgeting Skill This skill provides a systematic framework for managing the finite context window (RAM) of an OpenClaw agent. ## Core Concepts ### 1. Information Partitioning - **Objective/Goal (10%)**: Core task instructions and active constraints. - **Short-term History (40%)**: Recent 5-10 turns of raw dialogue. - **Decision Logs (20%)**: Summarized outcomes of past steps ("Tried X, failed because Y"). - **Background/Knowledge (20%)**: High-relevance snippets from MEMORY.md. ### 2. Pre-compression Checkpointing (Mandatory) Before any compaction (manual or automatic), the agent MUST: 1. **Generate Checkpoint**: Update `memory/hot/HOT_MEMORY.md` with: - **Status**: Current task progress. - **Key Decision**: Significant choices made. - **Next Step**: Immediate action required. 2. **Run Automation**: Execute `scripts/gc_and_checkpoint.sh` to trigger the physical cleanup. ## Automation Tool: `gc_and_checkpoint.sh` Located at: `skills/context-budgeting/scripts/gc_and_checkpoint.sh` **Usage**: - Run this script after updating `HOT_MEMORY.md` to finalize the compaction process without restarting the session. ## Integration with Heartbeat Heartbeat (every 30m) acts as the Garbage Collector (GC): 1. Check `/status`. If Context > 80%, trigger the **Checkpointing** procedure. 2. Clear raw data (e.g., multi-megabyte JSON outputs) once the summary is extracted.

context-budgeting