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Chisle Audit

Community
JayPokale
chisle-audit

One-shot efficiency audit of a file, diff, or whole repo across BOTH axes at once: over-engineered code (reinvented stdlib, needless abstractions, speculative config) AND bloated prose (verbose comments, padded docstrings, redundant doc sections). Neither a pure code-minimizer nor a pure prose compressor does both in one pass. That's the point. Ranked report, biggest saving first; changes nothing. Use when the user says "chisle audit", "/chisle-audit", "audit this for bloat", "what can I cut", "review this PR for over-engineering and verbosity".

Overview

PublisherJayPokale
RepositoryChisle
Skill namechisle-audit
Stars
648
Forks
46
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 JayPokale on GitHub. Read the source before you install it.

Installation

Install the Chisle 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/JayPokale/Chisle/tree/main/skills/chisle-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/JayPokale/Chisle.git /tmp/Chisle
mkdir -p .claude/skills
cp -r /tmp/Chisle/skills/chisle-audit .claude/skills/chisle-audit
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Chisle 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 Chisle 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 Chisle 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.

Chisle Audit

Scan the target (a diff, a file, or the repo tree) and report what to cut, on both axes. One-shot. Read-only: never edit, never write a flag, never apply fixes.

Scope

  • No argument → audit the current git diff (staged + unstaged). Empty diff → audit HEAD~1..HEAD.
  • A path → audit that file or directory.
  • "repo" / "whole repo" → walk the tree (skip vendored/generated/node_modules/dist/lockfiles).

What to flag

Code (the YAGNI axis):

  • Reinvented stdlib (hand-rolled debounce, deep-clone, groupBy, retry loop, date math)
  • Abstraction with one implementation (interface/factory/wrapper for a single case)
  • New dependency for what a few lines or an installed dep already covers
  • Config/option/flag that never varies
  • Speculative "for later" scaffolding with no current caller
  • Verbose code where a native platform feature (CSS, DB constraint, <input type>) does it

Prose (the compression axis), the half a code-only auditor misses:

  • Comments that restate the code (i += 1 // increment i)
  • Docstrings/READMEs padded with filler, hedging, ceremony, or duplicated content
  • Multi-paragraph explanations where one tight sentence carries the meaning
  • Decorative tables/emoji/headings that add tokens, not information
  • Dead prose: TODO graveyards, stale "see also" links, obsolete sections

What NOT to flag

Input validation at trust boundaries, error handling that prevents data loss, security, accessibility, deliberate // chisle: / // ponytail: shortcuts already documented, or domain comments that explain why (not what).

Output

One ranked list, biggest cut first. One finding per line. No preamble, no praise.

path:line  [code|prose]  <what's bloated> → <the lean replacement>. (~N lines/tokens)

End with a two-line summary:

N findings: X code, Y prose. Est. removable: ~A lines code, ~B lines prose.
Biggest win: <the single highest-impact cut>.

Be honest about uncertainty: mark a finding (check) if cutting it might lose behavior you can't verify from the snippet. Lean toward fewer, high-confidence findings over a long speculative list.

Frequently asked questions

What does the Chisle Audit AI skill do?

One-shot efficiency audit of a file, diff, or whole repo across BOTH axes at once: over-engineered code (reinvented stdlib, needless abstractions, speculative config) AND bloated prose (verbose comments, padded docstrings, redundant doc sections). Neither a pure code-minimizer nor a pure prose compressor does both in one pass. That's the point. Ranked report, biggest saving first; changes nothing. Use when the user says "chisle audit", "/chisle-audit", "audit this for bloat", "what can I cut", "review this PR for over-engineering and verbosity".

Why use Chisle Audit on TypingMind?

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

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

Which AI models can use Chisle 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 Chisle Audit?

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

Is the Chisle Audit AI skill free?

Yes. It is published on GitHub by JayPokale 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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