Specification
Specification是一款code方向的AI技能，核心价值是Generate or update specification documents for new or existing functionality，可用于解决开发者在code领域的实际问题，帮助用户提升效率、自动化重复任务或优化工作流。
Generate or update specification documents for new or existing functionality.
mkdir -p ./skills/specification && curl -sfL https://raw.githubusercontent.com/github/awesome-copilot/main/skills/specification/SKILL.md -o ./skills/specification/SKILL.mdRun in terminal / PowerShell. Requires curl (Unix) or PowerShell 5+ (Windows).
Skill Content
# Specification mode instructions
You are in specification mode. You work with the codebase to generate or update specification documents for new or existing functionality.
A specification must define the requirements, constraints, and interfaces for the solution components in a manner that is clear, unambiguous, and structured for effective use by Generative AIs. Follow established documentation standards and ensure the content is machine-readable and self-contained.
**Best Practices for AI-Ready Specifications:**
- Use precise, explicit, and unambiguous language.
- Clearly distinguish between requirements, constraints, and recommendations.
- Use structured formatting (headings, lists, tables) for easy parsing.
- Avoid idioms, metaphors, or context-dependent references.
- Define all acronyms and domain-specific terms.
- Include examples and edge cases where applicable.
- Ensure the document is self-contained and does not rely on external context.
If asked, you will create the specification as a specification file.
The specification should be saved in the [/spec/](/spec/) directory and named according to the following convention: `spec-[a-z0-9-]+.md`, where the name should be descriptive of the specification's content and starting with the highlevel purpose, which is one of [schema, tool, data, infrastructure, process, architecture, or design].
The specification file must be formatted in well formed Markdown.
Specification files must follow the template below, ensuring that all sections are filled out appropriately. The front matter for the markdown should be structured correctly as per the example following:
---
title: [Concise Title Describing the Specification's Focus]
version: [Optional: e.g., 1.0, Date]
date_created: [YYYY-MM-DD]
last_updated: [Optional: YYYY-MM-DD]
owner: [Optional: Team/Individual responsible for this spec]
tags: [Optional: List of relevant tags or categories, e.g., `infrastructure`, `process`, `design`, `app` etc]
---
# Introduction
[A short concise introduction to the specification and the goal it is intended to achieve.]
## 1. Purpose & Scope
[Provide a clear, concise description of the specification's purpose and the scope of its application. State the intended audience and any assumptions.]
## 2. Definitions
[List and define all acronyms, abbreviations, and domain-specific terms used in this specification.]
## 3. Requirements, Constraints & Guidelines
[Explicitly list all requirements, constraints, rules, and guidelines. Use bullet points or tables for clarity.]
- **REQ-001**: Requirement 1
- **SEC-001**: Security Requirement 1
- **[3 LETTERS]-001**: Other Requirement 1
- **CON-001**: Constraint 1
- **GUD-001**: Guideline 1
- **PAT-001**: Pattern to follow 1
## 4. Interfaces & Data Contracts
[Describe the interfaces, APIs, data contracts, or integration points. Use tables or code blocks for schemas and examples.]
## 5. Acceptance Criteria
[Define clear, testable acceptance criteria for each requirement using Given-When-Then format where appropriate.]
- **AC-001**: Given [context], When [action], Then [expected outcome]
- **AC-002**: The system shall [specific behavior] when [condition]
- **AC-003**: [Additional acceptance criteria as needed]
## 6. Test Automation Strategy
[Define the testing approach, frameworks, and automation requirements.]
- **Test Levels**: Unit, Integration, End-to-End
- **Frameworks**: MSTest, FluentAssertions, Moq (for .NET applications)
- **Test Data Management**: [approach for test data creation and cleanup]
- **CI/CD Integration**: [automated testing in GitHub Actions pipelines]
- **Coverage Requirements**: [minimum code coverage thresholds]
- **Performance Testing**: [approach for load and performance testing]
## 7. Rationale & Context
[Explain the reasoning behind the requirements, constraints, and guidelines. Provide context for design decisions.]
## 8. Dependencies & External Integrations
[Define the external systems, services, and 🎯 Best For
- Technical writers
- API documentation teams
- Developers scaffolding new projects
- Prototype builders
- Claude users
💡 Use Cases
- Generating JSDoc/TSDoc comments
- Writing README files for new projects
- Bootstrapping React components
- Creating API route handlers
📖 How to Use This Skill
- 1
Install the Skill
Copy the install command from the Terminal tab and run it. The SKILL.md file downloads to your local skills directory.
- 2
Load into Your AI Assistant
Open Claude or GitHub Copilot and reference the skill. Paste the SKILL.md content or use the system prompt tab.
- 3
Apply Specification to Your Work
Open your project in the AI assistant and ask it to apply the skill. Start with a small module to verify the output quality.
- 4
Review and Refine
Review AI suggestions before committing. Run tests, check for regressions, and iterate on the skill output.
❓ Frequently Asked Questions
Does it follow my documentation style?
Most documentation skills respect existing style. Provide a style guide or example in your prompt.
Can I customize the generated output?
Yes — modify the skill's prompt instructions to match your project conventions and coding style.
Is Specification compatible with Cursor and VS Code?
Yes — this skill works with any AI coding assistant including Cursor, VS Code with Copilot, and JetBrains IDEs.
Do I need specific dependencies for Specification?
Check the install command and Works With section. Most code skills only require the AI assistant and your codebase.
How do I install Specification?
Copy the install command from the Terminal tab and run it. The skill downloads to ./skills/specification/SKILL.md, ready to use.
⚠️ Common Mistakes to Avoid
Auto-generating without reviewing
AI documentation can contain inaccuracies. Always verify technical accuracy.
Using generated code without understanding
Understand what generated code does before shipping it to production.
Skipping validation
Always test AI-generated code changes, even for simple refactors.
Missing dependency updates
Check if the skill requires updated dependencies or new packages.