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Context Audit

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undefined-ui
context-audit

Audit an agent's context layout against the four places: system prompt, tools, history, tail. Use when the user asks to audit, review or fix their agent's context, prompt caching, token spend per turn, or CLAUDE.md / AGENTS.md layout. Read-only: reports findings and a fix list, changes nothing. Do NOT use for writing evals (evals-bootstrap) or for loop design (goal-test).

Overview

Publisherundefined-ui
Repositorysecond-brain-os
Skill namecontext-audit
Stars
1K
Forks
166
Bundled files
Instructions only
LicenseMIT
Links
  • 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 undefined-ui on GitHub. Read the source before you install it.

Installation

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

1

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.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/undefined-ui/second-brain-os/tree/main/plugins/agents-course/skills/context-audit
  4. 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.
2

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/undefined-ui/second-brain-os.git /tmp/second-brain-os
mkdir -p .claude/skills
cp -r /tmp/second-brain-os/plugins/agents-course/skills/context-audit .claude/skills/context-audit
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Context Audit 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 Audit 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 Audit 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.

Audit the context window

Theory: How models read context and The four places. Every piece of context belongs in exactly one of four places — a byte-stable system prompt, a frozen tool set, a compacting history, and a short live tail — and most agent problems trace back to something sitting in the wrong one.

Core rule

Report and rank; never edit. The output is an audit, and the user decides what to apply. If they ask you to apply fixes afterwards, that is a normal edit session, not this skill.

Workflow

  1. Find the context sources. Locate what actually reaches the model: system prompt (or CLAUDE.md / AGENTS.md for a Claude Code setup), tool definitions, and — if the project logs requests — one full mid-conversation payload. If nothing is logged, say so and audit the static files; ask for one captured request only if the user can produce it cheaply.
  2. Measure the five numbers. Approximate token counts for: system prompt, tool definitions, message history, retrieved content, current query. A rough count (chars / 4) is fine; write all five down.
  3. Hunt cache killers. Flag every dynamic value in the system prompt — timestamps, user names, injected memories, "current task" lines. Each one is a guaranteed cache miss on every call. Check whether the tool list can change mid-run; a changing list invalidates the whole prefix.
  4. Hunt window bloat. Find the largest single item in the history. A verbatim tool result over ~2,000 tokens should have been a file path plus a one-line receipt. Count tools: past twenty (or ~10K tokens of definitions), recommend deferred tools + tool search.
  5. Check the tail. Locate the current goal. If it appears only in the opening message, it lives in the weak middle of the window — recommend restating it in the tail every three to five steps.
  6. Check guides. If CLAUDE.md / AGENTS.md exists: it should be a few lines of constraints the code cannot show, not four pages of narrative. Flag anything the repo already records (structure, history).

Output format

Context audit — <project>
system prompt   <n> tok   <clean | N dynamic values: ...>
tools           <n> tok   <n> tools  <frozen | mutates mid-run>
history         <n> tok   largest item: <what, n tok>
tail            <present | goal only in opening message>

Top fixes, in order of saved tokens per turn:
1. ...
2. ...

Each fix names the file and line where possible, states what moves to which of the four places, and estimates the saving. Close with the one-line rule: stable prefix, frozen tools, compact history, live tail.

Frequently asked questions

What does the Context Audit AI skill do?

Audit an agent's context layout against the four places: system prompt, tools, history, tail. Use when the user asks to audit, review or fix their agent's context, prompt caching, token spend per turn, or CLAUDE.md / AGENTS.md layout. Read-only: reports findings and a fix list, changes nothing. Do NOT use for writing evals (evals-bootstrap) or for loop design (goal-test).

Why use Context Audit on TypingMind?

Because you install it once and use it with any model. Context Audit 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 Audit in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/undefined-ui/second-brain-os/tree/main/plugins/agents-course/skills/context-audit. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Context Audit?

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 Audit?

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

Is the Context Audit AI skill free?

Yes. It is published on GitHub by undefined-ui under the MIT license. 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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