Audit the context window

SkillAI & models

Lets your agent audit its own context layout and report token waste and fixes without changing anything.

Then ask your AI: use the Audit the context window skill

Details

Instructions available. Your AI can read the instructions. Execution depends on the setup they require.

Add it as a step in a pipeline you build on Ahel, or connect Ahel to Claude, ChatGPT, Cursor, Claude Code or Codex and ask your AI to use it.

About this skill

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 N

What this skill tells your AI

The instructions your AI receives, as published by undefined-ui/second-brain-os in plugins/agents-course/skills/context-audit/SKILL.md and read by Ahel’s review.

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.

Signals

GitHub stars
1k
Forks
163
Last commit
Oct 2026
Advanced
Item type
skill
Key
context-audit-undefined-ui
Source
github.com/undefined-ui/second-brain-os