prompt-library
Skill0 starsChanged 10 months agoMITView on GitHub
Curated collection of high-quality prompts for various tasks—role-based templates, analysis frameworks, code review prompts, and brainstorming structures. Use when building prompts for specific roles, needing analysis or brainstorming frameworks, creating code review prompts, or searching for proven prompt patterns and structures.
7 other projects use a version of this, 4 of them edited.
Adopted by
Most recently taken up by
- awaku7/agentclitheir own version, first seen Nov 2, 2026
- xfstudio/skillsedited, first seen Oct 13, 2026
- poorvith-mp/skills-agentstheir own version, first seen Sep 6, 2026
- karim-bhalwani/agentic-harnessedited, first seen Jul 12, 2026
- broomva/skillstheir own version, first seen Jun 13, 2026
- jornalistainclusivo/jinc-skillsedited, first seen Apr 22, 2026
Other versions7 projects use a version of this.
Word for word · 0
None found.
Edited · 4
- xfstudio/skills★ 964% same
- ai4brands-design/claude-skills★ 264% same
- jornalistainclusivo/jinc-skills★ 162% same
- karim-bhalwani/agentic-harness★ 011% same
Wrote their own version · 3
- awaku7/agentcli★ 12same name
- broomva/skills★ 4same name
- poorvith-mp/skills-agents★ 0same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository33 other files it gives its agents.
- architectSkill
- brainstormingSkill
- concise-planningSkill
- context-engineerSkill
- data-deprecation-analysisSkill
- data-modelerSkill
- data-pipeline-engineerSkill
- data-quality-frameworksSkill
- dbt-transformation-patternsSkill
- github-workflow-automationSkill
- guardianSkill
- implementerSkill
- json-to-pydanticSkill
- llm-app-patternsSkill
- navigatorSkill
- ops-managerSkill
- orchestratorSkill
- principal-data-engineerSkill
- product-manager-toolkitSkill
- proj-memorySkill
- qa-testerSkill
- researcherSkill
- senior-data-engineerSkill
- software-architectureSkill
- spark-optimizationSkill
- sql-optimization-patternsSkill
- state-managerSkill
- subagent-driven-developmentSkill
- thinkerSkill
- top-web-vulnerabilitiesSkill
- verification-before-completionSkill
- web-design-guidelinesSkill
- workflow-managerSkill
The file
Files
Prompt Library
Curated collection of high-quality prompts for various use cases, organized by task type.
When to Use This Reference
Use when:
- Needing a role-based prompt template
- Building analysis or brainstorming prompts
- Creating prompts for code review or testing
- Looking for prompt patterns and structures
Role-Based Prompts
Expert Developer
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.Code Reviewer
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)Technical Writer
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 levelSystem Architect
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.Task-Specific Prompts
Debug This Code
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 Like I'm 5 (ELI5)
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 toneCode Refactoring
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 Tests
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 appropriateAPI Documentation
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.Analysis Prompts
Code Complexity Analysis
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.Performance Analysis
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.Security Review
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).Creative & Transformation Prompts
Brainstorm Features
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.Name Generator
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.Migrate Code
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 Format
Convert [INPUT_FORMAT] to [OUTPUT_FORMAT]:
1. Parse source format
2. Map equivalent structures
3. Handle format-specific features
4. Validate output correctness
5. Add necessary formatting
Show conversion examples.Best Practices
- ✅ Be specific: More specific prompts = better results
- ✅ Show format: Include example output structure
- ✅ Use roles: "Act as X" improves consistency
- ✅ Set constraints: Specify what matters most
- ✅ Request reasoning: Ask to explain thinking
- ✅ Include context: Share relevant background
- ✅ Iterate: Refine prompts based on results
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

