docs-cleaner▌
daymade/claude-code-skills · updated Apr 22, 2026
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Consolidate redundant documentation while preserving 100% of valuable content.
Documentation Cleaner
Consolidate redundant documentation while preserving 100% of valuable content.
Core Principle
Critical evaluation before deletion. Never blindly delete. Analyze each section's unique value before proposing removal. The goal is reduction without information loss.
Workflow
Phase 1: Discovery
- Identify all documentation files covering the topic
- Count total lines across files
- Map content overlap between documents
Phase 2: Value Analysis
For each document, create a section-by-section analysis table:
| Section | Lines | Value | Reason |
|---|---|---|---|
| API Reference | 25 | Keep | Unique endpoint documentation |
| Setup Steps | 40 | Condense | Verbose but essential |
| Test Results | 30 | Delete | One-time record, not reference |
Value categories:
- Keep: Unique, essential, frequently referenced
- Condense: Valuable but verbose
- Delete: Duplicate, one-time, self-evident, outdated
See references/value_analysis_template.md for detailed criteria.
Phase 3: Consolidation Plan
Propose target structure:
Before: 726 lines (3 files, high redundancy)
After: ~100 lines (1 file + reference in CLAUDE.md)
Reduction: 86%
Value preserved: 100%
Phase 4: Execution
- Create consolidated document with all valuable content
- Delete redundant source files
- Update references (CLAUDE.md, README, imports)
- Verify no broken links
Value Preservation Checklist
Before finalizing, confirm preservation of:
- Essential procedures (setup, configuration)
- Key constraints and gotchas
- Troubleshooting guides
- Technical debt / roadmap items
- External links and references
- Debug tips and code snippets
Anti-Patterns
| Pattern | Problem | Solution |
|---|---|---|
| Blind deletion | Loses valuable information | Section-by-section analysis first |
| Keeping everything | No reduction achieved | Apply value criteria strictly |
| Multiple sources of truth | Future divergence | Single authoritative location |
| Orphaned references | Broken links | Update all references after consolidation |
Output Artifacts
A successful cleanup produces:
- Consolidated document - Single source of truth
- Value analysis - Section-by-section justification
- Before/after metrics - Lines reduced, value preserved
- Updated references - CLAUDE.md or README with pointer to new location
How to use docs-cleaner on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add docs-cleaner
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches docs-cleaner from GitHub repository daymade/claude-code-skills and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate docs-cleaner. Access the skill through slash commands (e.g., /docs-cleaner) or your agent's skill management interface.
Security & Verification 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 development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
Task Automation & Efficiency
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Knowledge Enhancement
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Quality Improvement
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
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
Installation Steps
- 1.Install skill using provided installation command
- 2.Test with simple use case relevant to your work
- 3.Evaluate output quality and relevance
- 4.Iterate on prompts to improve results
- 5.Integrate 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
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.6★★★★★67 reviews- ★★★★★Anaya Lopez· Dec 24, 2024
docs-cleaner reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Anika Khan· Dec 24, 2024
docs-cleaner fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Ganesh Mohane· Dec 20, 2024
Solid pick for teams standardizing on skills: docs-cleaner is focused, and the summary matches what you get after install.
- ★★★★★Anika Reddy· Dec 20, 2024
docs-cleaner has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Shikha Mishra· Dec 12, 2024
docs-cleaner reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Meera Gonzalez· Dec 12, 2024
docs-cleaner has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Anika Nasser· Dec 8, 2024
I recommend docs-cleaner for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Soo Li· Dec 8, 2024
Useful defaults in docs-cleaner — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Mia Ndlovu· Dec 4, 2024
Solid pick for teams standardizing on skills: docs-cleaner is focused, and the summary matches what you get after install.
- ★★★★★Anika Shah· Nov 27, 2024
docs-cleaner reduced setup friction for our internal harness; good balance of opinion and flexibility.
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