Use when long-lived agents, project memories, system prompts, or playbooks are repeatedly summarized, rewritten, or optimized and risk losing domain detail, provenance, or execution learnings.
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Generates a complete, working application from an OpenAPI specification using the active framework's conventions: models, routes, validation, and tests. Use when scaffolding a service from an existing OpenAPI/Swagger spec rather than hand-writing endpoints.
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安装前可以查看交给 AI 代理的指令内容。
Attribution: Sourced from github/awesome-copilot by GitHub Community, licensed MIT. Modified for this collection.
Your goal is to generate a complete, working application from an OpenAPI specification using the active framework's conventions and best practices.
OpenAPI Specification: Provide either:
https://api.example.com/openapi.json)Project Details (if not in spec):
The generated application will include:
project-name/
├── README.md # Setup and usage instructions
├── [build-config] # Framework-specific build files (pom.xml, build.gradle, package.json, etc.)
├── src/
│ ├── main/
│ │ ├── [language]/
│ │ │ ├── controllers/ # HTTP endpoint handlers
│ │ │ ├── services/ # Business logic
│ │ │ ├── models/ # Data models and DTOs
│ │ │ ├── repositories/ # Data access (if applicable)
│ │ │ └── config/ # Application configuration
│ │ └── resources/ # Configuration files
│ └── test/
│ ├── [language]/
│ │ ├── controllers/ # Controller tests
│ │ └── services/ # Service tests
│ └── resources/ # Test configuration
├── .gitignore
├── .env.example # Environment variables template
└── docker-compose.yml # Optional: Docker setup (if applicable)
After generation:
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Use when long-lived agents, project memories, system prompts, or playbooks are repeatedly summarized, rewritten, or optimized and risk losing domain detail, provenance, or execution learnings.
中文简介正在准备中。当前显示原文说明。
Use this skill when the user explicitly asks to map, document, or onboard into an existing codebase. Trigger for prompts like "map this codebase", "document this architecture", "onboard me to this repo", or "create codebase docs". Do not trigger for routine feature implementation, bug fixes, or narrow code edits unless the user asks for repository-level discovery.
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Adds educational comments to a code file so it becomes a learning resource, asking for the file (with a list of close matches) if none is given. Use when preparing code for learners, onboarding, or teaching.
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Write, debug, and optimize Adobe Illustrator automation scripts using ExtendScript (JavaScript/JSX). Use when creating or modifying scripts that manipulate documents, layers, paths, text frames, colors, symbols, artboards, or any Illustrator DOM objects. Covers the complete JavaScript object model, coordinate system, measurement units, export workflows, and scripting best practices.
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Patterns and techniques for adding governance, safety, and trust controls to AI agent systems. Use this skill when: - Building AI agents that call external tools (APIs, databases, file systems) - Implementing policy-based access controls for agent tool usage - Adding semantic intent classification to detect dangerous prompts - Creating trust scoring systems for multi-agent workflows - Building audit trails for agent actions and decisions - Enforcing rate limits, content filters, or tool restrictions on agents - Working with any agent framework (PydanticAI, CrewAI, OpenAI Agents, LangChain, AutoGen)
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Check any AI agent codebase against the OWASP Agentic Security Initiative (ASI) Top 10 risks. Use this skill when: - Evaluating an agent system's security posture before production deployment - Running a compliance check against OWASP ASI 2026 standards - Mapping existing security controls to the 10 agentic risks - Generating a compliance report for security review or audit - Comparing agent framework security features against the standard - Any request like "is my agent OWASP compliant?", "check ASI compliance", or "agentic security audit"
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