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Found 5,790 Skills
Use when working with AI agent protocols, standards, and interoperability specifications. Covers MCP, A2A, ACP, Agent Skills, AGENTS.md, ADL, x402, AP2, MCP Apps, and cagent. USE FOR: agent protocol selection, comparing MCP vs A2A vs ACP, understanding agent standards ecosystem, choosing payment protocols DO NOT USE FOR: specific protocol implementation details (use the sub-skills: mcp, a2a, acp, x402, etc.)
AI/LLM: Use when crafting system prompts, optimizing LLM outputs, or improving agent instructions. NOT for general coding.
Initialize a repository for ASDLC adoption with AGENTS.md and directory structure
Use when building custom Kiro AI agents or when user asks for agent configurations - provides JSON structure, tool configuration, prompt patterns, and security best practices for specialized development assistants
Integrate OpenAI Agents SDK with You.com MCP server - Hosted and Streamable HTTP support for Python and TypeScript. - MANDATORY TRIGGERS: OpenAI Agents SDK, OpenAI agents, openai-agents, @openai/agents, integrating OpenAI with MCP - Use when: developer mentions OpenAI Agents SDK, needs MCP integration with OpenAI agents
Integrate Claude Agent SDK with You.com HTTP MCP server for Python and TypeScript. Use when developer mentions Claude Agent SDK, Anthropic Agent SDK, or integrating Claude with MCP tools.
Proposal-first development workflow with commit hygiene and decision authority rules. Enforces: propose before modifying, atomic commits, no force flags, warnings-as-errors. Use for any project where AI agents are primary developers and need guardrails.
Maintain project documentation with clear human/agent separation. Use when setting up a new project, auditing docs, creating plans, or managing agent working memory. Triggers include "set up docs", "create a plan", "audit documentation", "init project structure", or any task involving project documentation conventions.
Use when checking cross-file consistency: tools vs frontmatter, agent references, duplicate rules, contradictions.
Create implementation plans with tasks grouped by subsystem. Related tasks share agent context; groups parallelize across subsystems.
Create and maintain a control-system metalayer for autonomous code-agent development in any repository. Use when you need explicit control primitives (setpoints, sensors, controller policy, actuators, feedback loop, stability and entropy controls), repo command/rule governance, and a scalable folder topology that lets agents operate safely and keep improving over time.
This skill should be used when the user asks to "create chatbot", "virtual agent", "VA topic", "NLU", "conversation", "chat flow", "topic block", or any ServiceNow Virtual Agent development.