typespec-create-agent
Generate a complete TypeSpec declarative agent with instructions, capabilities, and conversation starters for Microsoft 365 Copilot
Best use case
typespec-create-agent is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Generate a complete TypeSpec declarative agent with instructions, capabilities, and conversation starters for Microsoft 365 Copilot
Teams using typespec-create-agent should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/typespec-create-agent/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How typespec-create-agent Compares
| Feature / Agent | typespec-create-agent | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Generate a complete TypeSpec declarative agent with instructions, capabilities, and conversation starters for Microsoft 365 Copilot
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
# Create TypeSpec Declarative Agent
Create a complete TypeSpec declarative agent for Microsoft 365 Copilot with the following structure:
## Requirements
Generate a `main.tsp` file with:
1. **Agent Declaration**
- Use `@agent` decorator with a descriptive name and description
- Name should be 100 characters or less
- Description should be 1,000 characters or less
2. **Instructions**
- Use `@instructions` decorator with clear behavioral guidelines
- Define the agent's role, expertise, and personality
- Specify what the agent should and shouldn't do
- Keep under 8,000 characters
3. **Conversation Starters**
- Include 2-4 `@conversationStarter` decorators
- Each with a title and example query
- Make them diverse and showcase different capabilities
4. **Capabilities** (based on user needs)
- `WebSearch` - for web content with optional site scoping
- `OneDriveAndSharePoint` - for document access with URL filtering
- `TeamsMessages` - for Teams channel/chat access
- `Email` - for email access with folder filtering
- `People` - for organization people search
- `CodeInterpreter` - for Python code execution
- `GraphicArt` - for image generation
- `GraphConnectors` - for Copilot connector content
- `Dataverse` - for Dataverse data access
- `Meetings` - for meeting content access
## Template Structure
```typescript
import "@typespec/http";
import "@typespec/openapi3";
import "@microsoft/typespec-m365-copilot";
using TypeSpec.Http;
using TypeSpec.M365.Copilot.Agents;
@agent({
name: "[Agent Name]",
description: "[Agent Description]"
})
@instructions("""
[Detailed instructions about agent behavior, role, and guidelines]
""")
@conversationStarter(#{
title: "[Starter Title 1]",
text: "[Example query 1]"
})
@conversationStarter(#{
title: "[Starter Title 2]",
text: "[Example query 2]"
})
namespace [AgentName] {
// Add capabilities as operations here
op capabilityName is AgentCapabilities.[CapabilityType]<[Parameters]>;
}
```
## Best Practices
- Use descriptive, role-based agent names (e.g., "Customer Support Assistant", "Research Helper")
- Write instructions in second person ("You are...")
- Be specific about the agent's expertise and limitations
- Include diverse conversation starters that showcase different features
- Only include capabilities the agent actually needs
- Scope capabilities (URLs, folders, etc.) when possible for better performance
- Use triple-quoted strings for multi-line instructions
## Examples
Ask the user:
1. What is the agent's purpose and role?
2. What capabilities does it need?
3. What knowledge sources should it access?
4. What are typical user interactions?
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