Skills Directory

Finalize prompt file using the role of an AI agent to polish the prompt for the end user.

Create new Agent Skills for GitHub Copilot from prompts or by duplicating this template. Use when asked to "create a skill", "make a new ski

Interactive, input-tool powered, task refinement workflow: interrogates scope, deliverables, constraints before carrying out the task; Requi

This workflow guides you through a systematic approach to identify missing features, prioritize them, and create detailed specifications for

Suggest relevant GitHub Copilot Custom Agents files from the awesome-copilot repository based on current repository context and chat history

Create agent skills for Microsoft technologies using Learn MCP tools. Use when users want to create a skill that teaches agents about any Mi

Skill converted from mcp-create-declarative-agent.prompt.md

Generate complete Power Platform custom connector with MCP integration for Copilot Studio - includes schema generation, troubleshooting, and

When user is asking for guidance for which role to assign to an identity given desired permissions, this agent helps them understand the rol

Skill converted from mcp-create-adaptive-cards.prompt.md

Complete development kit for Microsoft 365 Copilot declarative agents with three comprehensive workflows (basic, advanced, validation), Type

Generate a complete TypeSpec declarative agent with instructions, capabilities, and conversation starters for Microsoft 365 Copilot

Create Agent Users in Microsoft Entra ID from Agent Identities, enabling AI agents to act as digital workers with user identity capabilities

skill does absolutely nothing, as well as the agent that uses it.

This skill should be used when the user asks to "create an agent", "add an agent", "write a subagent", "agent frontmatter", "when to use des

Tracks token spend across an agent run and stops or downgrades before it blows the budget. Use when running long autonomous tasks with a har

Enforces safe context compaction for long-running agents. Use whenever an agent run is expected to exceed roughly 40 turns or 100K tokens.

Adds a cheap classification step ahead of expensive model calls. Use in any high-volume production workload.

Prevents an agent from acting on state it never checked. Use in any agent with file, shell or API write access.

Stops treating an advertised context window as usable capacity. Use when designing any long-context feature.

Improves tool definitions so a model picks the right one. Use when authoring or reviewing MCP servers and tool schemas.