How do I install the Context Budgeting skill?

Run npx skillmds@latest add dvcrn/context-budgeting in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Context Budgeting safe to use?

This skill has not completed SkillMD's automated safety review yet. Independent scanners report: SkillSpector: PASS, Skill Scanner: PASS. Capability flags: executes scripts. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Context Budgeting?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Context Budgeting free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Context Budgeting?

dvcrn (@dvcrn) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Context Budgeting by dvcrn — Agent Skill

Name: Context Budgeting
Rating: 4.5 (4 reviews)
Author: dvcrn

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.

dvcrn Updated Sep 8, 2026 32 repo stars

File contents

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.

Frequently asked questions