rsaryev/tokendoctor/audit

Claude Code and agent skill by rsaryev/tokendoctor

Trust tier 2
  • community
LLM review pending
Source check pending

Checking that the SKILL.md file is still in the repository.

Audit this Claude Code setup for settings that waste tokens or silently do nothing.

Install

npx vskill@latest install rsaryev/tokendoctor/audit

SKILL.md

Audit the Claude Code configuration for the current project (or $ARGUMENTS if given).

  1. Run tokendoctor-audit --json (add --project <dir> when an argument was given). It is read-only

and reports the Claude Code version, what it found, and a list of findings, each with a severity, an explanation and a documentation link.

  1. Present the findings grouped by severity: errors first (settings that are wrong or ignored),

then warnings, then info. For each, give one or two sentences in plain words and the doc link. Do not restate the whole detail text.

  1. Be honest about limits: the audit checks configuration against documented behaviour. It does

not measure savings, so state no percentages. Point to /usage (usage breakdown and recommendations) and /context (what is loaded now) for measuring.

  1. Say which findings have an automatic fix (those with a fixes list) and offer

/tokendoctor fix <finding-id> for them. Never edit files from this skill.

  1. Review what Claude is told to do, not only how it is configured: read this project's memory

(~/.claude/projects/<project path with / as ->/memory/, its MEMORY.md and the files it lists) and the instruction files (~/.claude/CLAUDE.md, ~/.claude/rules/, the project's CLAUDE.md and .claude/rules/). Use your own judgment: point out anything there that makes Claude spend more than the work needs, contradicts the settings or another instruction, is stale, or is unsafe to keep. Quote file:line and say how to rewrite it. Where an instruction is plainly the person's own choice, show what it costs and leave the choice to them.

  1. Read the docs as they are today. The audit checked every settings key and env variable

against Claude Code's current settings reference and variable list (facts.docs says when they were read; null means offline with no copy, so those checks were skipped: say so). To explain a finding beyond its detail, or to answer a question about a setting, read its doc link: every page is served as Markdown at that URL (…/docs/en/<page>.md), and https://code.claude.com/docs/llms.txt indexes them all. Prefer this to memory or to a mirror such as context7: it is the text of the current release, while mirrors re-index on their own schedule.

  1. See what really loaded. Findings from a debug log (facts.debugLog gives its time) come from a

claude --debug run of the last day. To check now, offer tokendoctor-selfcheck: it starts claude -p once in this project on Haiku, with no saved session and no MCP servers (one small request, a few cents at most), and reports which copy of tokendoctor loaded and in which tier, whether the built-in guard was seated, mods hooking compaction ahead of it, skipped hooks, the window Claude Code really compacts at, and a skill listing over its budget. Run it only after the person says yes; tokendoctor-selfcheck --log <file> reads a log they already have, at no cost.

  1. If facts.version is null, say the Claude Code version could not be read and that rules which

depend on a minimum version were still applied.

  1. Build the usage dashboard: run tokendoctor-dashboard --open. It reads every transcript on this

machine (usage figures only, never message text), writes one HTML file with no network use, and opens it: spend by day, by kind of token, model, reasoning effort, main thread and each subagent type, context size, project, hour of the week, and the largest sessions, with filters. Give the path it prints, and tie one or two of its numbers to the findings above (for example the share of spend that was the conversation re-read from the cache, or above 200K tokens of context).

Security

  • Security scanPassed, 100 of 100Pattern checks on the skill's files.
  • Model reviewNot runA model review is scheduled for the next rescan.

Quality evaluation

  • Task evaluationNot runNo evaluation yet. Run one in Skill Studio.

Source

Popularity

Repository stars
20
Repository forks
4
7-day trend score
0

Stars and forks are for the whole GitHub repository, not this skill alone. Not refreshed yet.

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