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Improve Claude Md

OrganizationPopular
humanlayer
improve-claude-md

improve a CLAUDE.md file using <important if> blocks to improve instruction adherence

Overview

Publisherhumanlayer
Repositoryskills
Skill nameimprove-claude-md
Stars
4.7K
Forks
151
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 humanlayer on GitHub. Read the source before you install it.

Installation

Install the Improve Claude Md 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/humanlayer/skills/tree/main/plugins/improve-claude-md/skills/improve-claude-md
  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/humanlayer/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/plugins/improve-claude-md/skills/improve-claude-md .claude/skills/improve-claude-md
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Improve Claude Md 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 Improve Claude Md 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 Improve Claude Md 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.

When the user provides a CLAUDE.md file (or asks you to improve one), rewrite it following the principles and structure below.

Core Problem

Claude Code injects a system reminder with every CLAUDE.md that says:

"this context may or may not be relevant to your tasks. You should not respond to this context unless it is highly relevant to your task."

This means Claude will ignore parts of your CLAUDE.md it deems irrelevant. The more content that isn't applicable to the current task, the more likely Claude is to ignore everything — including the parts that matter.

Solution: <important if="condition"> Blocks

Wrap conditionally-relevant sections of the CLAUDE.md in <important if="condition"> XML tags. This exploits the same XML tag pattern used in Claude Code's own system prompt, giving the model an explicit relevance signal that cuts through the "may or may not be relevant" framing.

Principles

1. Foundational context stays bare, domain guidance gets wrapped

Not everything should be in an <important if> block. Context that is relevant to virtually every task — project identity, project map, tech stack — should be left as plain markdown at the top of the file. This is onboarding context the agent always needs.

Domain-specific guidance that only matters for certain tasks — testing patterns, API conventions, state management, i18n — gets wrapped in <important if> blocks with targeted conditions.

The rule of thumb: if it's relevant to 90%+ of tasks, leave it bare. If it's relevant to a specific kind of work, wrap it.

2. Conditions must be specific and targeted

Bad — overly broad conditions that match everything:

<important if="you are writing or modifying any code">
- Use absolute imports
- Use functional components
- Use camelCase filenames
</important>

Good — each rule has its own narrow trigger:

<important if="you are adding or modifying imports">
- Use `@/` absolute imports (see tsconfig.json for path aliases)
- Avoid default exports except in route files
</important>

<important if="you are creating new components">
- Use functional components with explicit prop interfaces
</important>

<important if="you are creating new files or directories">
- Use camelCase for file and directory names
</important>

3. Keep it short, use progressive disclosure sparingly

Do not shard into separate files that require the agent to make tool calls to discover, unless the extra context is incredibly verbose or complex.

The whole point of <important if> blocks is that everything is inline but conditionally weighted — the agent sees it all but only attends to what matches.

Prefer to keep the file concise.

4. Less is more

  • Frontier models can reliably follow a few hundred. Claude Code's system prompt and tools already use ~50 of those. Your CLAUDE.md should be as lean as possible.
  • Cut any instruction that a linter, formatter, or pre-commit hook can enforce
  • Cut any instruction the agent can discover from existing code patterns. LLMs are in-context learners — if your codebase consistently uses a pattern, the agent will follow it after a few searches.
  • Cut code snippets. They go stale and bloat the file. Use file path references instead (e.g., "see src/utils/example.ts for the pattern").

5. Keep all commands

Do not drop commands from the original file. The commands table is foundational reference — the agent needs to know what's available even if some commands are used less frequently.

Output Structure

When rewriting a CLAUDE.md, produce this structure:

# CLAUDE.md

[one-line project identity — what it is, what it's built with]

## Project map
[directory listing with brief descriptions]

<important if="you need to run commands to build, test, lint, or generate code">
[commands table — all commands from the original]
</important>

<important if="<specific trigger for rule 1>">
[rule 1]
</important>

<important if="<specific trigger for rule 2>">
[rule 2]
</important>

... more rules, each with their own block ...

<important if="<specific trigger for domain area 1>">

[guidance]

</important>

... more domain sections ...

How to Apply

When given an existing CLAUDE.md to improve:

  1. Identify the project identity — extract a single sentence describing what this is. Leave it bare at the top.
  2. Extract the directory map — keep it bare (no <important if> wrapper). This is foundational context.
  3. Extract the tech stack — if present, keep it bare near the top. Condense to one or two lines.
  4. Extract commands — keep ALL commands from the original. Wrap in a single <important if> block.
  5. Break apart rules — split any list of rules into individual <important if> blocks with specific conditions. You can group rules, but never group unrelated rules under one broad condition.
  6. Wrap domain sections — testing, API patterns, state management, i18n, etc. each get their own block with a condition describing when that knowledge matters.
  7. Delete linter territory — remove style guidelines, formatting rules, and anything enforceable by tooling. Suggest replacing with pre-push or pre-commit hooks.
  8. Delete code snippets — replace with file path references.
  9. Delete vague instructions — remove anything like "leverage the X agent" or "follow best practices" that isn't concrete and actionable.

Example

Input:

markdown
# CLAUDE.md

This is an Express API with a React frontend in a Turborepo monorepo.

## Commands

| Command | Description |
|---|---|
| `turbo build` | Build all packages |
| `turbo test` | Run all tests |
| `turbo lint` | Lint all packages |
| `turbo dev` | Start dev server |
| `turbo storybook` | Start Storybook |
| `turbo db:generate` | Generate Prisma client |
| `turbo db:migrate` | Run database migrations |
| `turbo analyze` | Bundle analyzer |

## Project Structure

- `apps/api/` - Express REST API
- `apps/web/` - React SPA
- `packages/db/` - Prisma schema and client
- `packages/ui/` - Shared component library
- `packages/config/` - Shared configuration

## Coding Standards

- Use named exports
- Use functional components with TypeScript interfaces for props
- Use camelCase for variables, PascalCase for components
- Prefer `const` over `let`
- Always use strict equality (`===`)
- Use template literals instead of string concatenation
- Write JSDoc comments for all public functions
- Use barrel exports in index.ts files

## API Development

- All routes go in `apps/api/src/routes/`
- Use Zod for request validation
- Use Prisma for database access
- Error responses follow RFC 7807 format
- Authentication via JWT middleware

## Testing

- Jest + Supertest for API tests
- Vitest + Testing Library for frontend
- Test fixtures in `__fixtures__/` directories
- Use `createTestClient()` helper for API integration tests
- Mock database with `prismaMock` from `packages/db/test`

## State Management

- Zustand for global client state
- React Query for server state
- URL state via `nuqs`

Output:

markdown
# CLAUDE.md

Express API + React frontend in a Turborepo monorepo.

## Project map

- `apps/api/` - Express REST API
- `apps/web/` - React SPA
- `packages/db/` - Prisma schema and client
- `packages/ui/` - Shared component library
- `packages/config/` - Shared configuration

<important if="you need to run commands to build, test, lint, or generate code">

Run with `turbo` from the repo root.

| Command | What it does |
|---|---|
| `turbo build` | Build all packages |
| `turbo test` | Run all tests |
| `turbo lint` | Lint all packages |
| `turbo dev` | Start dev server |
| `turbo storybook` | Start Storybook |
| `turbo db:generate` | Regenerate Prisma client after schema changes |
| `turbo db:migrate` | Run database migrations |
| `turbo analyze` | Bundle analyzer |
</important>

<important if="you are adding or modifying imports or exports">
- Use named exports (no default exports)
</important>

<important if="you are creating new components">
- Use functional components with TypeScript interfaces for props
</important>

<important if="you are adding or modifying API routes">

- All routes go in `apps/api/src/routes/`
- Use Zod for request validation
- Use Prisma for database access
- Error responses follow RFC 7807 format
- Authentication via JWT middleware
</important>

<important if="you are writing or modifying tests">

- API: Jest + Supertest
- Frontend: Vitest + Testing Library
- Test fixtures in `__fixtures__/` directories
- Use `createTestClient()` helper for API integration tests
- Mock database with `prismaMock` from `packages/db/test`
</important>

<important if="you are working with state management, stores, or URL parameters">

- Zustand for global client state
- React Query for server state
- URL state via `nuqs`
</important>

What was removed and why:

  • camelCase/PascalCase, const vs let, strict equality, template literals, JSDoc, barrel exports — linter and formatter territory, or discoverable from existing code patterns
  • Coding Standards as a grouped section — split into targeted blocks by trigger condition

What was NOT removed:

  • All commands kept (including dev, storybook, analyze)
  • Project map left bare (foundational context, relevant to every task)

Frequently asked questions

What does the Improve Claude Md AI skill do?

improve a CLAUDE.md file using <important if> blocks to improve instruction adherence

Why use Improve Claude Md on TypingMind?

Because you install it once and use it with any model. Improve Claude Md 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 Improve Claude Md in TypingMind?

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

Which AI models can use Improve Claude Md?

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 Improve Claude Md?

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

Is the Improve Claude Md AI skill free?

Yes. It is published on GitHub by humanlayer 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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