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Knip

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brianlovin
knip

Run knip to find and remove unused files, dependencies, and exports. Use for cleaning up dead code and unused dependencies.

Overview

Publisherbrianlovin
Repositoryagent-config
Skill nameknip
Stars
370
Forks
31
Bundled files
Instructions only
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 brianlovin on GitHub. Read the source before you install it.

Installation

Install the Knip 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.
  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/brianlovin/agent-config.git /tmp/agent-config
mkdir -p .claude/skills
cp -r /tmp/agent-config/skills/knip .claude/skills/knip
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Knip 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 Knip 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 Knip 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.

Knip Code Cleanup

Run knip to find and remove unused files, dependencies, and exports from this codebase.

Setup

  1. Check if knip is available:

    • Run npx knip --version to test
    • If it fails or is very slow, check if knip is in package.json devDependencies
    • If not installed locally, install with npm install -D knip (or pnpm/yarn/bun equivalent based on lockfile present)
  2. Knip does NOT remove unused imports/variables inside files — that's a linter's job. Knip finds unused files, dependencies, and exports across the project.

Workflow

Always follow this configuration-first workflow. Even for simple "run knip" or "clean up codebase" prompts, configure knip properly before acting on reported issues.

Step 1: Understand the project

  • Check what frameworks and tools the project uses (look at package.json)
  • Check if a knip config exists (knip.json, knip.jsonc, or knip key in package.json)
  • If a config exists, review it for improvements (see Configuration Best Practices below)

Step 2: Run knip and read configuration hints first

bash
npx knip

Focus on configuration hints before anything else. These appear at the top of the output and suggest config adjustments to reduce false positives.

Step 3: Address hints by adjusting knip.json

Fix configuration hints before addressing reported issues. Common adjustments:

  • Enable/disable plugins for detected frameworks
  • Add entry patterns for non-standard entry points
  • Configure workspace settings for monorepos

Step 4: Repeat steps 2-3

Re-run knip after each config change. Repeat until configuration hints are resolved and false positives are minimized.

Step 5: Address actual issues

Once the configuration is settled, work through reported issues. Prioritize in this order:

  1. Unused files — address these first ("inbox zero" approach removes the most noise)
  2. Unused dependencies — remove from package.json
  3. Unused devDependencies — remove from package.json
  4. Unused exports — remove or mark as internal
  5. Unused types — remove, or configure ignoreExportsUsedInFile (see below)

Step 6: Re-run and repeat

Re-run knip after each batch of fixes. Removing unused files often exposes newly-unused exports and dependencies.

Configuration Best Practices

When reviewing or creating a knip config, follow these rules:

  • Never use ignore patternsignore hides real issues and should almost never be used. Always prefer specific solutions. Other ignore* options (like ignoreDependencies, ignoreExportsUsedInFile) are fine because they target specific issue types.
  • Many unused exported types? Add ignoreExportsUsedInFile: { interface: true, type: true } — this handles the common case of types only used in the same file. Prefer this over broader ignore options.
  • Remove redundant patterns — Knip already respects .gitignore, so ignoring node_modules, dist, build, .git is redundant.
  • Remove entry patterns covered by defaults — Auto-detected plugins already add standard entry points. Don't duplicate them.
  • Config files showing as unused (e.g. vite.config.ts) — Enable or disable the corresponding plugin explicitly rather than ignoring the file.
  • Dependencies matching Node.js builtins (e.g. buffer, process) — Add to ignoreDependencies.
  • Unresolved imports from path aliases — Add paths to knip config (uses tsconfig.json semantics).

Production Mode

Use --production to focus on production code only:

bash
npx knip --production

This excludes test files, config files, and other non-production entry points. Do NOT use project or ignore patterns to exclude test files — use --production instead.

Cleanup Confidence Levels

Auto-delete (high confidence):

  • Unused exports that are clearly internal (not part of public API)
  • Unused type exports
  • Unused dependencies (remove from package.json)
  • Unused files that are clearly orphaned (not entry points, not config files)

Ask first (needs clarification):

  • Files that might be entry points or dynamically imported
  • Exports that might be part of a public API (index.ts, lib exports)
  • Dependencies that might be used via CLI or peer dependencies
  • Anything in paths like src/index, lib/, or files with "public" or "api" in the name

Use the AskUserQuestion tool to clarify before deleting these.

Auto-fix

Once configuration is settled and you're confident in the results:

bash
# Auto-fix safe changes (removes unused exports and dependencies)
npx knip --fix

# Auto-fix including file deletion
npx knip --fix --allow-remove-files

Only use --fix after the configuration-first workflow is complete.

Error Handling

If knip exits with code 2 (unexpected error like "error loading file"):

  • Check if a config file exists — if not, create knip.json in the project root
  • Check for known issues at knip.dev
  • Review the configuration reference for syntax/option errors
  • Run knip again after fixes

Common Commands

bash
# Basic run
npx knip

# Production only (excludes test/config entry points)
npx knip --production

# Auto-fix what's safe
npx knip --fix

# Auto-fix including file deletion
npx knip --fix --allow-remove-files

# JSON output for parsing
npx knip --reporter json

Notes

  • Watch for monorepo setups — may need --workspace flag
  • Some frameworks need plugins enabled in config
  • Knip does not handle unused imports/variables inside files — use ESLint or Biome for that

Frequently asked questions

What does the Knip AI skill do?

Run knip to find and remove unused files, dependencies, and exports. Use for cleaning up dead code and unused dependencies.

Why use Knip on TypingMind?

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

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

Which AI models can use Knip?

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 Knip?

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

Is the Knip AI skill free?

It is published on GitHub by brianlovin. Check the repository for licensing terms. 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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