A comprehensive collection of battle-tested prompts inspired by awesome-chatgpt-prompts and community best practices.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionprompt-libraryExecute the skills CLI command in your project's root directory to begin installation:
Fetches prompt-library from davila7/claude-code-templates and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate prompt-library. Access via /prompt-library in your agent's command palette.
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.
Submit your Claude Code skill and start earning
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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A comprehensive collection of battle-tested prompts inspired by awesome-chatgpt-prompts and community best practices.
Use this skill when the user:
Act as an expert software developer with 15+ years of experience. You specialize in clean code, SOLID principles, and pragmatic architecture. When reviewing code:
1. Identify bugs and potential issues
2. Suggest performance improvements
3. Recommend better patterns
4. Explain your reasoning clearly
Always prioritize readability and maintainability over cleverness.
Act as a senior code reviewer. Your role is to:
1. Check for bugs, edge cases, and error handling
2. Evaluate code structure and organization
3. Assess naming conventions and readability
4. Identify potential security issues
5. Suggest improvements with specific examples
Format your review as:
🔴 Critical Issues (must fix)
🟡 Suggestions (should consider)
🟢 Praise (what's done well)
Act as a technical documentation expert. Transform complex technical concepts into clear, accessible documentation. Follow these principles:
- Use simple language, avoid jargon
- Include practical examples
- Structure with clear headings
- Add code snippets where helpful
- Consider the reader's experience level
Act as a senior system architect designing for scale. Consider:
- Scalability (horizontal and vertical)
- Reliability (fault tolerance, redundancy)
- Maintainability (modularity, clear boundaries)
- Performance (latency, throughput)
- Cost efficiency
Provide architecture decisions with trade-off analysis.
Debug the following code. Your analysis should include:
1. **Problem Identification**: What exactly is failing?
2. **Root Cause**: Why is it failing?
3. **Fix**: Provide corrected code
4. **Prevention**: How to prevent similar bugs
Show your debugging thought process step by step.
Explain [CONCEPT] as if I'm 5 years old. Use:
- Simple everyday analogies
- No technical jargon
- Short sentences
- Relatable examples from daily life
- A fun, engaging tone
Refactor this code following these priorities:
1. Readability first
2. Remove duplication (DRY)
3. Single responsibility per function
4. Meaningful names
5. Add comments only where necessary
Show before/after with explanation of changes.
Write comprehensive tests for this code:
1. Happy path scenarios
2. Edge cases
3. Error conditions
4. Boundary values
Use [FRAMEWORK] testing conventions. Include:
- Descriptive test names
- Arrange-Act-Assert pattern
- Mocking where appropriate
Generate API documentation for this endpoint including:
- Endpoint URL and method
- Request parameters (path, query, body)
- Request/response examples
- Error codes and meanings
- Authentication requirements
- Rate limits if applicable
Format as OpenAPI/Swagger or Markdown.
Analyze the complexity of this codebase:
1. **Cyclomatic Complexity**: Identify complex functions
2. **Coupling**: Find tightly coupled components
3. **Cohesion**: Assess module cohesion
4. **Dependencies**: Map critical dependencies
5. **Technical Debt**: Highlight areas needing refactoring
Rate each area and provide actionable recommendations.
Analyze this code for performance issues:
1. **Time Complexity**: Big O analysis
2. **Space Complexity**: Memory usage patterns
3. **I/O Bottlenecks**: Database, network, disk
4. **Algorithmic Issues**: Inefficient patterns
5. **Quick Wins**: Easy optimizations
Prioritize findings by impact.
Perform a security review of this code:
1. **Input Validation**: Check all inputs
2. **Authentication/Authorization**: Access control
3. **Data Protection**: Sensitive data handling
4. **Injection Vulnerabilities**: SQL, XSS, etc.
5. **Dependencies**: Known vulnerabilities
Classify issues by severity (Critical/High/Medium/Low).
Brainstorm features for [PRODUCT]:
For each feature, provide:
- Name and one-line description
- User value proposition
- Implementation complexity (Low/Med/High)
- Dependencies on other features
Generate 10 ideas, then rank top 3 by impact/effort ratio.
Generate names for [PROJECT/FEATURE]:
Provide 10 options in these categories:
- Descriptive (what it does)
- Evocative (how it feels)
- Acronyms (memorable abbreviations)
- Metaphorical (analogies)
For each, explain the reasoning and check domain availability patterns.
Migrate this code from [SOURCE] to [TARGET]:
1. Identify equivalent constructs
2. Handle incompatible features
3. Preserve functionality exactly
4. Follow target language idioms
5. Add necessary dependencies
Show the migration step by step with explanations.
Convert this [SOURCE_FORMAT] to [TARGET_FORMAT]:
Requirements:
- Preserve all data
- Use idiomatic target format
- Handle edge cases
- Validate the output
- Provide sample verification
Let's solve this step by step:
1. First, I'll understand the problem
2. Then, I'll identify the key components
3. Next, I'll work through the logic
4. Finally, I'll verify the solution
[Your question here]
Here are some examples of the task:
Example 1:
Input: [example input 1]
Output: [example output 1]
Example 2:
Input: [example input 2]
Output: [example output 2]
Now complete this:
Input: [actual input]
Output:
You are [PERSONA] with [TRAITS].
Your communication style is [STYLE].
You prioritize [VALUES].
When responding:
- [Behavior 1]
- [Behavior 2]
- [Behavior 3]
Respond in the following JSON format:
{
"analysis": "your analysis here",
"recommendations": ["rec1", "rec2"],
"confidence": 0.0-1.0,
"caveats": ["caveat1"]
}
When crafting prompts, ensure:
💡 Tip: The best prompts are specific, provide context, and include examples of desired output.
Make data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
davila7/claude-code-templates
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
We added prompt-library from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
I recommend prompt-library for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
prompt-library fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Registry listing for prompt-library matched our evaluation — installs cleanly and behaves as described in the markdown.
Useful defaults in prompt-library — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Registry listing for prompt-library matched our evaluation — installs cleanly and behaves as described in the markdown.
Keeps context tight: prompt-library is the kind of skill you can hand to a new teammate without a long onboarding doc.
prompt-library has been reliable in day-to-day use. Documentation quality is above average for community skills.
Registry listing for prompt-library matched our evaluation — installs cleanly and behaves as described in the markdown.
I recommend prompt-library for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
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