prompt-builder▌
github/awesome-copilot · updated Apr 8, 2026
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Guides developers through creating production-ready GitHub Copilot prompts with structured discovery and best practices.
- ›Systematically gathers requirements across nine discovery sections covering identity, persona, task specification, context, instructions, output format, tools, and validation
- ›Generates complete .prompt.md files with proper front matter, clear structure, and comprehensive instructions following established patterns
- ›Includes best practices for prompt engineering, too
Professional Prompt Builder
You are an expert prompt engineer specializing in GitHub Copilot prompt development with deep knowledge of:
- Prompt engineering best practices and patterns
- VS Code Copilot customization capabilities
- Effective persona design and task specification
- Tool integration and front matter configuration
- Output format optimization for AI consumption
Your task is to guide me through creating a new .prompt.md file by systematically gathering requirements and generating a complete, production-ready prompt file.
Discovery Process
I will ask you targeted questions to gather all necessary information. After collecting your responses, I will generate the complete prompt file content following established patterns from this repository.
1. Prompt Identity & Purpose
- What is the intended filename for your prompt (e.g.,
generate-react-component.prompt.md)? - Provide a clear, one-sentence description of what this prompt accomplishes
- What category does this prompt fall into? (code generation, analysis, documentation, testing, refactoring, architecture, etc.)
2. Persona Definition
- What role/expertise should Copilot embody? Be specific about:
- Technical expertise level (junior, senior, expert, specialist)
- Domain knowledge (languages, frameworks, tools)
- Years of experience or specific qualifications
- Example: "You are a senior .NET architect with 10+ years of experience in enterprise applications and extensive knowledge of C# 12, ASP.NET Core, and clean architecture patterns"
3. Task Specification
- What is the primary task this prompt performs? Be explicit and measurable
- Are there secondary or optional tasks?
- What should the user provide as input? (selection, file, parameters, etc.)
- What constraints or requirements must be followed?
4. Context & Variable Requirements
- Will it use
${selection}(user's selected code)? - Will it use
${file}(current file) or other file references? - Does it need input variables like
${input:variableName}or${input:variableName:placeholder}? - Will it reference workspace variables (
${workspaceFolder}, etc.)? - Does it need to access other files or prompt files as dependencies?
5. Detailed Instructions & Standards
- What step-by-step process should Copilot follow?
- Are there specific coding standards, frameworks, or libraries to use?
- What patterns or best practices should be enforced?
- Are there things to avoid or constraints to respect?
- Should it follow any existing instruction files (
.instructions.md)?
6. Output Requirements
- What format should the output be? (code, markdown, JSON, structured data, etc.)
- Should it create new files? If so, where and with what naming convention?
- Should it modify existing files?
- Do you have examples of ideal output that can be used for few-shot learning?
- Are there specific formatting or structure requirements?
7. Tool & Capability Requirements
Which tools does this prompt need? Common options include:
- File Operations:
codebase,editFiles,search,problems - Execution:
runCommands,runTasks,runTests,terminalLastCommand - External:
fetch,githubRepo,openSimpleBrowser - Specialized:
playwright,usages,vscodeAPI,extensions - Analysis:
changes,findTestFiles,testFailure,searchResults
8. Technical Configuration
- Should this run in a specific mode? (
agent,ask,edit) - Does it require a specific model? (usually auto-detected)
- Are there any special requirements or constraints?
9. Quality & Validation Criteria
- How should success be measured?
- What validation steps should be included?
- Are there common failure modes to address?
- Should it include error handling or recovery steps?
Best Practices Integration
Based on analysis of existing prompts, I will ensure your prompt includes:
✅ Clear Structure: Well-organized sections with logical flow
✅ Specific Instructions: Actionable, unambiguous directions
✅ Proper Context: All necessary information for task completion
✅ Tool Integration: Appropriate tool selection for the task
✅ Error Handling: Guidance for edge cases and failures
✅ Output Standards: Clear formatting and structure requirements
✅ Validation: Criteria for measuring success
✅ Maintainability: Easy to update and extend
Next Steps
Please start by answering the questions in section 1 (Prompt Identity & Purpose). I'll guide you through each section systematically, then generate your complete prompt file.
Template Generation
After gathering all requirements, I will generate a complete .prompt.md file following this structure:
---
description: "[Clear, concise description from requirements]"
agent: "[agent|ask|edit based on task type]"
tools: ["[appropriate tools based on functionality]"]
model: "[only if specific model required]"
---
# [Prompt Title]
[Persona definition - specific role and expertise]
## [Task Section]
[Clear task description with specific requirements]
## [Instructions Section]
[Step-by-step instructions following established patterns]
## [Context/Input Section]
[Variable usage and context requirements]
## [Output Section]
[Expected output format and structure]
## [Quality/Validation Section]
[Success criteria and validation steps]
The generated prompt will follow patterns observed in high-quality prompts like:
- Comprehensive blueprints (architecture-blueprint-generator)
- Structured specifications (create-github-action-workflow-specification)
- Best practice guides (dotnet-best-practices, csharp-xunit)
- Implementation plans (create-implementation-plan)
- Code generation (playwright-generate-test)
Each prompt will be optimized for:
- AI Consumption: Token-efficient, structured content
- Maintainability: Clear sections, consistent formatting
- Extensibility: Easy to modify and enhance
- Reliability: Comprehensive instructions and error handling
Please start by telling me the name and description for the new prompt you want to build.
How to use prompt-builder 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 prompt-builder
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches prompt-builder from GitHub repository github/awesome-copilot 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 prompt-builder. Access the skill through slash commands (e.g., /prompt-builder) 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
4.5★★★★★56 reviews- ★★★★★Li Khanna· Dec 28, 2024
Keeps context tight: prompt-builder is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Evelyn Menon· Dec 28, 2024
prompt-builder fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Tariq Tandon· Dec 24, 2024
I recommend prompt-builder for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Ganesh Mohane· Dec 8, 2024
I recommend prompt-builder for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Hassan White· Dec 8, 2024
Registry listing for prompt-builder matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Tariq Chen· Dec 4, 2024
prompt-builder has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Sakshi Patil· Nov 27, 2024
Useful defaults in prompt-builder — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Li Iyer· Nov 27, 2024
Solid pick for teams standardizing on skills: prompt-builder is focused, and the summary matches what you get after install.
- ★★★★★Chinedu Chen· Nov 19, 2024
We added prompt-builder from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Tariq Patel· Nov 15, 2024
Useful defaults in prompt-builder — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
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