# Meta Context Budgeting

> Techniques for managing the context window limit in long-running AI agents.

- Skill: `jcorpac/meta-context-budgeting` (Agent Skill)
- Install (CLI): `npx skillmds@latest add jcorpac/meta-context-budgeting`
- Raw SKILL.md: https://api.skillmd.com/api/skills/jcorpac/meta-context-budgeting/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: jcorpac (https://skillmd.com/u/jcorpac)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/jcorpac/meta-context-budgeting

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# Meta-Context Budgeting

AI models have a "budget" (the context window). Every token you use leaves less room for reasoning.

## Budgeting Strategies
- **Purging**: Identifying and removing outdated logs, repetitive instructions, or completed task summaries once they are no longer needed.
- **Summarization**: Compressing long technical discussions into concise action points before proceeding to the next phase.
- **Priority Filtering**: Keeping "System Instructions" and "Foundational Constraints" at the highest priority, while ephemeral chat history is lower priority.

## Tools
- **Token Counters**: Using tools to monitor current usage.
- **Selective Retrieval**: Only fetching the most relevant "chunks" of a codebase or skill library.

## Best Practices
- **Clean State**: Occasionally starting a fresh session and only carrying over the "Essential State" (ADRs, current plan, critical constraints).