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.