Fetches improve-claude-md from dexhorthy/slopfiles and configures it for Cursor.
3
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│ ── Universal (.agents/skills) ────────────────
│ · Cline · Codex · Goose · Windsurf
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4
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Confirm successful installation by checking the skill directory location:
.cursor/skills/improve-claude-md
Restart Cursor to activate improve-claude-md. Access via /improve-claude-md in your agent's command palette.
⚠
Security Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
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.
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:
Identify the project identity — extract a single sentence describing what this is. Leave it bare at the top.
Extract the directory map — keep it bare (no <important if> wrapper). This is foundational context.
Extract the tech stack — if present, keep it bare near the top. Condense to one or two lines.
Extract commands — keep ALL commands from the original. Wrap in a single <important if> block.
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.
Wrap domain sections — testing, API patterns, state management, i18n, etc. each get their own block with a condition describing when that knowledge matters.
Delete linter territory — remove style guidelines, formatting rules, and anything enforceable by tooling. Suggest replacing with pre-push or pre-commit hooks.
Delete code snippets — replace with file path references.
Delete vague instructions — remove anything like "leverage the X agent" or "follow best practices" that isn't concrete and actionable.
Example
Input:
# CLAUDE.mdThis 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:
# CLAUDE.mdExpress 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
<importantif="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><importantif="you are adding or modifying imports or exports">- Use named exports (no default exports)
</important>
Implementation Guide
Prerequisites
›Claude Desktop or compatible AI client with skill support
›Clear understanding of task or problem to solve
›Willingness to iterate and refine outputs
Time Estimate
15-45 minutes depending on use case complexity
Steps
1Install skill using provided installation command
2Test with simple use case relevant to your work
3Evaluate output quality and relevance
4Iterate on prompts to improve results
5Integrate into regular workflow if valuable
Common Pitfalls
⚠Expecting perfect results without iteration
⚠Not providing enough context in prompts
⚠Using skill for tasks outside its intended scope
⚠Accepting outputs without review and validation
Best Practices
✓ Do
+Start with clear, specific prompts
+Provide relevant context and constraints
+Review and refine all outputs before using
+Iterate to improve output quality
+Document successful prompt patterns
✗ Don't
−Don't use without understanding skill limitations
−Don't skip validation of outputs
−Don't share sensitive information in prompts
−Don't expect skill to replace human judgment
💡 Pro Tips
★Be specific about desired format and style
★Ask for multiple options to choose from
★Request explanations to understand reasoning
★Combine AI efficiency with human expertise
When to Use This
✓ Use when
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid when
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
Learning Path
1Familiarize yourself with skill capabilities and limitations
2Start with low-risk, non-critical tasks
3Progress to more complex and valuable use cases
4Build expertise through regular use and experimentation
