Context Budget

Community

coco-research

context-budget

Audit token consumption across agents, skills, MCP servers, and rules. Identifies bloat, redundant components, and produces prioritized token-savings recommendations. Use when the context window is filling up too fast or before adding new components.

Overview

Publisher	coco-research
Repository	`coco`
Skill name	`context-budget`
Stars	550
Forks	12
Bundled files	Instructions only
Links	Skill source Repository Homepage

Markdown instructions
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
Works with any LLM
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
Self-contained
Everything the model needs lives in the instructions — no extra files to sync.
Open source
Published by coco-research on GitHub. Read the source before you install it.

Installation

Install the Context Budget AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/coco-research/coco/tree/main/skills/context-budget
Enable the skill in any chat where you want it available.

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/coco-research/coco.git /tmp/coco
mkdir -p .claude/skills
cp -r /tmp/coco/skills/context-budget .claude/skills/context-budget

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Context Budget in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Context Budget on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Context Budget is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

@agents/PROMPT-DEFENSE.md

Context Budget Audit

Analyze token overhead across every loaded component in a session and surface actionable optimizations to reclaim context space.

When to Use

Session performance feels sluggish or output quality is degrading
You've recently added many skills, agents, or MCP servers
Planning to add more components and need to know if there's room
Running /context-budget command (this skill backs it)

Audit Procedure

Phase 1: Inventory

Scan all component directories and estimate token consumption:

Agents (agents/*.md)

Count lines and tokens per file (words × 1.3)
Extract description frontmatter length
Flag: files >200 lines (heavy), description >30 words (bloated frontmatter)

Skills (skills/*/SKILL.md)

Count tokens per SKILL.md
Flag: files >400 lines
Check for duplicate copies — skip identical copies to avoid double-counting

Rules (rules/**/*.md, rules/**/*.mdc)

Count tokens per file
Flag: files >100 lines
Detect content overlap between rule files

MCP Servers (active MCP config)

Count configured servers and total tool count
Estimate schema overhead at ~500 tokens per tool
Flag: servers with >20 tools

CLAUDE.md / System Prompts

Count tokens in CLAUDE.md chain
Flag: combined total >300 lines

Phase 2: Classify

Sort every component into a bucket:

Bucket	Criteria	Action
Always needed	Referenced in CLAUDE.md, backs active command, matches project type	Keep
Sometimes needed	Domain-specific, not referenced in CLAUDE.md	Consider on-demand activation
Rarely needed	No command reference, overlapping content, no project match	Remove or lazy-load

Phase 3: Report

Generate a prioritized savings report:


CONTEXT BUDGET AUDIT
====================
Total estimated tokens: {total}
Context headroom: {headroom}%

TOP SAVINGS OPPORTUNITIES:
1. {component} — {tokens} tokens ({bucket}) → {recommendation}
2. {component} — {tokens} tokens ({bucket}) → {recommendation}
...

ALWAYS NEEDED (keep):
- {component} ({tokens} tokens)

SOMETIMES NEEDED (lazy-load candidates):
- {component} ({tokens} tokens)

RARELY NEEDED (removal candidates):
- {component} ({tokens} tokens)

Token Estimation Formula

English text: ~1.3 tokens per word
Code: ~1.5 tokens per word (more punctuation/symbols)
YAML frontmatter: ~1.2 tokens per word
MCP tool schema: ~500 tokens per tool definition

Integration Points

Run automatically before /team:ship to verify context headroom
Feed results to learning system as optimization instincts
Cross-reference with Brain DB decisions about past context issues

Frequently asked questions

What does the Context Budget AI skill do?

Why use Context Budget on TypingMind?

Because you install it once and use it with any model. Context Budget is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Context Budget in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/coco-research/coco/tree/main/skills/context-budget. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Context Budget?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Context Budget?

As many as you like. As long as a model supports skills, you can use Context Budget with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Context Budget AI skill free?

It is published on GitHub by coco-research. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Context Budget Audit

When to Use

Audit Procedure

Phase 1: Inventory

Phase 2: Classify

Phase 3: Report

Token Estimation Formula

Integration Points

Frequently asked questions

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