Context Budgeting: AI & Machine Learning for Claude Code | AI Skill Market | AI Skill Market

Context Budgeting: AI & Machine Learning for Claude Code | AI Skill Market | AI Skill Market

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:

Generate Checkpoint: Update
```
memory/hot/HOT_MEMORY.md
```
with:
- Status: Current task progress.
- Key Decision: Significant choices made.
- Next Step: Immediate action required.
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):

Check
```
/status
```
. If Context > 80%, trigger the Checkpointing procedure.
Clear raw data (e.g., multi-megabyte JSON outputs) once the summary is extracted.