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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).

Quellinformationen

Repository
undefined-ui/second-brain-os
Letzte Quellaktivität
7. Oktober 2026 um 19:16
Erkannte Sprache von SKILL.md
Englisch
Sterne
1.124
Forks
187

Installationsoptionen

Standardmäßig ist der Prompt ausgewählt, der zuerst die Quelle prüft. Sie können zu einem direkten Befehl wechseln oder eine lokale Kopie herunterladen.

Quelldateien prüfen

Lesen Sie SKILL.md und alle von SkillsMP angezeigten Begleitdateien, bevor Sie sich für eine Installation entscheiden.

SKILL.md wird angezeigt

SKILL.md
Quellanweisungen · Schreibgeschützte Vorschau
name
context-audit
description
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).
# Audit the context window Theory: [How models read context](https://secondbrainos.dev/#/read/course-1-context/how-models-read) and [The four places](https://secondbrainos.dev/#/read/course-1-context/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.
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