Merge branch 'main' into MCP-M365-Agents

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Matt Soucoup
2026-01-09 07:03:26 -10:00
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---
description: 'Guidelines for creating custom agent files for GitHub Copilot'
applyTo: '**/*.agent.md'
---
# Custom Agent File Guidelines
Instructions for creating effective and maintainable custom agent files that provide specialized expertise for specific development tasks in GitHub Copilot.
## Project Context
- Target audience: Developers creating custom agents for GitHub Copilot
- File format: Markdown with YAML frontmatter
- File naming convention: lowercase with hyphens (e.g., `test-specialist.agent.md`)
- Location: `.github/agents/` directory (repository-level) or `agents/` directory (organization/enterprise-level)
- Purpose: Define specialized agents with tailored expertise, tools, and instructions for specific tasks
- Official documentation: https://docs.github.com/en/copilot/how-tos/use-copilot-agents/coding-agent/create-custom-agents
## Required Frontmatter
Every agent file must include YAML frontmatter with the following fields:
```yaml
---
description: 'Brief description of the agent purpose and capabilities'
name: 'Agent Display Name'
tools: ['read', 'edit', 'search']
model: 'Claude Sonnet 4.5'
target: 'vscode'
infer: true
---
```
### Core Frontmatter Properties
#### **description** (REQUIRED)
- Single-quoted string, clearly stating the agent's purpose and domain expertise
- Should be concise (50-150 characters) and actionable
- Example: `'Focuses on test coverage, quality, and testing best practices'`
#### **name** (OPTIONAL)
- Display name for the agent in the UI
- If omitted, defaults to filename (without `.md` or `.agent.md`)
- Use title case and be descriptive
- Example: `'Testing Specialist'`
#### **tools** (OPTIONAL)
- List of tool names or aliases the agent can use
- Supports comma-separated string or YAML array format
- If omitted, agent has access to all available tools
- See "Tool Configuration" section below for details
#### **model** (STRONGLY RECOMMENDED)
- Specifies which AI model the agent should use
- Supported in VS Code, JetBrains IDEs, Eclipse, and Xcode
- Example: `'Claude Sonnet 4.5'`, `'gpt-4'`, `'gpt-4o'`
- Choose based on agent complexity and required capabilities
#### **target** (OPTIONAL)
- Specifies target environment: `'vscode'` or `'github-copilot'`
- If omitted, agent is available in both environments
- Use when agent has environment-specific features
#### **infer** (OPTIONAL)
- Boolean controlling whether Copilot can automatically use this agent based on context
- Default: `true` if omitted
- Set to `false` to require manual agent selection
#### **metadata** (OPTIONAL, GitHub.com only)
- Object with name-value pairs for agent annotation
- Example: `metadata: { category: 'testing', version: '1.0' }`
- Not supported in VS Code
#### **mcp-servers** (OPTIONAL, Organization/Enterprise only)
- Configure MCP servers available only to this agent
- Only supported for organization/enterprise level agents
- See "MCP Server Configuration" section below
## Tool Configuration
### Tool Specification Strategies
**Enable all tools** (default):
```yaml
# Omit tools property entirely, or use:
tools: ['*']
```
**Enable specific tools**:
```yaml
tools: ['read', 'edit', 'search', 'execute']
```
**Enable MCP server tools**:
```yaml
tools: ['read', 'edit', 'github/*', 'playwright/navigate']
```
**Disable all tools**:
```yaml
tools: []
```
### Standard Tool Aliases
All aliases are case-insensitive:
| Alias | Alternative Names | Category | Description |
|-------|------------------|----------|-------------|
| `execute` | shell, Bash, powershell | Shell execution | Execute commands in appropriate shell |
| `read` | Read, NotebookRead, view | File reading | Read file contents |
| `edit` | Edit, MultiEdit, Write, NotebookEdit | File editing | Edit and modify files |
| `search` | Grep, Glob, search | Code search | Search for files or text in files |
| `agent` | custom-agent, Task | Agent invocation | Invoke other custom agents |
| `web` | WebSearch, WebFetch | Web access | Fetch web content and search |
| `todo` | TodoWrite | Task management | Create and manage task lists (VS Code only) |
### Built-in MCP Server Tools
**GitHub MCP Server**:
```yaml
tools: ['github/*'] # All GitHub tools
tools: ['github/get_file_contents', 'github/search_repositories'] # Specific tools
```
- All read-only tools available by default
- Token scoped to source repository
**Playwright MCP Server**:
```yaml
tools: ['playwright/*'] # All Playwright tools
tools: ['playwright/navigate', 'playwright/screenshot'] # Specific tools
```
- Configured to access localhost only
- Useful for browser automation and testing
### Tool Selection Best Practices
- **Principle of Least Privilege**: Only enable tools necessary for the agent's purpose
- **Security**: Limit `execute` access unless explicitly required
- **Focus**: Fewer tools = clearer agent purpose and better performance
- **Documentation**: Comment why specific tools are required for complex configurations
## Sub-Agent Invocation (Agent Orchestration)
Agents can invoke other agents using `runSubagent` to orchestrate multi-step workflows.
### How It Works
Include `agent` in tools list to enable sub-agent invocation:
```yaml
tools: ['read', 'edit', 'search', 'agent']
```
Then invoke other agents with `runSubagent`:
```javascript
const result = await runSubagent({
description: 'What this step does',
prompt: `You are the [Specialist] specialist.
Context:
- Parameter: ${parameterValue}
- Input: ${inputPath}
- Output: ${outputPath}
Task:
1. Do the specific work
2. Write results to output location
3. Return summary of completion`
});
```
### Basic Pattern
Structure each sub-agent call with:
1. **description**: Clear one-line purpose of the sub-agent invocation
2. **prompt**: Detailed instructions with substituted variables
The prompt should include:
- Who the sub-agent is (specialist role)
- What context it needs (parameters, paths)
- What to do (concrete tasks)
- Where to write output
- What to return (summary)
### Example: Multi-Step Processing
```javascript
// Step 1: Process data
const processing = await runSubagent({
description: 'Transform raw input data',
prompt: `You are the Data Processor specialist.
Project: ${projectName}
Input: ${basePath}/raw/
Output: ${basePath}/processed/
Task:
1. Read all files from input directory
2. Apply transformations
3. Write processed files to output
4. Create summary: ${basePath}/processed/summary.md
Return: Number of files processed and any issues found`
});
// Step 2: Analyze (depends on Step 1)
const analysis = await runSubagent({
description: 'Analyze processed data',
prompt: `You are the Data Analyst specialist.
Project: ${projectName}
Input: ${basePath}/processed/
Output: ${basePath}/analysis/
Task:
1. Read processed files from input
2. Generate analysis report
3. Write to: ${basePath}/analysis/report.md
Return: Key findings and identified patterns`
});
```
### Key Points
- **Pass variables in prompts**: Use `${variableName}` for all dynamic values
- **Keep prompts focused**: Clear, specific tasks for each sub-agent
- **Return summaries**: Each sub-agent should report what it accomplished
- **Sequential execution**: Use `await` to maintain order when steps depend on each other
- **Error handling**: Check results before proceeding to dependent steps
### ⚠️ Tool Availability Requirement
**Critical**: If a sub-agent requires specific tools (e.g., `edit`, `execute`, `search`), the orchestrator must include those tools in its own `tools` list. Sub-agents cannot access tools that aren't available to their parent orchestrator.
**Example**:
```yaml
# If your sub-agents need to edit files, execute commands, or search code
tools: ['read', 'edit', 'search', 'execute', 'agent']
```
The orchestrator's tool permissions act as a ceiling for all invoked sub-agents. Plan your tool list carefully to ensure all sub-agents have the tools they need.
### ⚠️ Important Limitation
**Sub-agent orchestration is NOT suitable for large-scale data processing.** Avoid using `runSubagent` when:
- Processing hundreds or thousands of files
- Handling large datasets
- Performing bulk transformations on big codebases
- Orchestrating more than 5-10 sequential steps
Each sub-agent call adds latency and context overhead. For high-volume processing, implement logic directly in a single agent instead. Use orchestration only for coordinating specialized tasks on focused, manageable datasets.
## Agent Prompt Structure
The markdown content below the frontmatter defines the agent's behavior, expertise, and instructions. Well-structured prompts typically include:
1. **Agent Identity and Role**: Who the agent is and its primary role
2. **Core Responsibilities**: What specific tasks the agent performs
3. **Approach and Methodology**: How the agent works to accomplish tasks
4. **Guidelines and Constraints**: What to do/avoid and quality standards
5. **Output Expectations**: Expected output format and quality
### Prompt Writing Best Practices
- **Be Specific and Direct**: Use imperative mood ("Analyze", "Generate"); avoid vague terms
- **Define Boundaries**: Clearly state scope limits and constraints
- **Include Context**: Explain domain expertise and reference relevant frameworks
- **Focus on Behavior**: Describe how the agent should think and work
- **Use Structured Format**: Headers, bullets, and lists make prompts scannable
## Variable Definition and Extraction
Agents can define dynamic parameters to extract values from user input and use them throughout the agent's behavior and sub-agent communications. This enables flexible, context-aware agents that adapt to user-provided data.
### When to Use Variables
**Use variables when**:
- Agent behavior depends on user input
- Need to pass dynamic values to sub-agents
- Want to make agents reusable across different contexts
- Require parameterized workflows
- Need to track or reference user-provided context
**Examples**:
- Extract project name from user prompt
- Capture certification name for pipeline processing
- Identify file paths or directories
- Extract configuration options
- Parse feature names or module identifiers
### Variable Declaration Pattern
Define variables section early in the agent prompt to document expected parameters:
```markdown
# Agent Name
## Dynamic Parameters
- **Parameter Name**: Description and usage
- **Another Parameter**: How it's extracted and used
## Your Mission
Process [PARAMETER_NAME] to accomplish [task].
```
### Variable Extraction Methods
#### 1. **Explicit User Input**
Ask the user to provide the variable if not detected in the prompt:
```markdown
## Your Mission
Process the project by analyzing your codebase.
### Step 1: Identify Project
If no project name is provided, **ASK THE USER** for:
- Project name or identifier
- Base path or directory location
- Configuration type (if applicable)
Use this information to contextualize all subsequent tasks.
```
#### 2. **Implicit Extraction from Prompt**
Automatically extract variables from the user's natural language input:
```javascript
// Example: Extract certification name from user input
const userInput = "Process My Certification";
// Extract key information
const certificationName = extractCertificationName(userInput);
// Result: "My Certification"
const basePath = `certifications/${certificationName}`;
// Result: "certifications/My Certification"
```
#### 3. **Contextual Variable Resolution**
Use file context or workspace information to derive variables:
```markdown
## Variable Resolution Strategy
1. **From User Prompt**: First, look for explicit mentions in user input
2. **From File Context**: Check current file name or path
3. **From Workspace**: Use workspace folder or active project
4. **From Settings**: Reference configuration files
5. **Ask User**: If all else fails, request missing information
```
### Using Variables in Agent Prompts
#### Variable Substitution in Instructions
Use template variables in agent prompts to make them dynamic:
```markdown
# Agent Name
## Dynamic Parameters
- **Project Name**: ${projectName}
- **Base Path**: ${basePath}
- **Output Directory**: ${outputDir}
## Your Mission
Process the **${projectName}** project located at `${basePath}`.
## Process Steps
1. Read input from: `${basePath}/input/`
2. Process files according to project configuration
3. Write results to: `${outputDir}/`
4. Generate summary report
## Quality Standards
- Maintain project-specific coding standards for **${projectName}**
- Follow directory structure: `${basePath}/[structure]`
```
#### Passing Variables to Sub-Agents
When invoking a sub-agent, pass all context through template variables in the prompt:
```javascript
// Extract and prepare variables
const basePath = `projects/${projectName}`;
const inputPath = `${basePath}/src/`;
const outputPath = `${basePath}/docs/`;
// Pass to sub-agent with all variables substituted
const result = await runSubagent({
description: 'Generate project documentation',
prompt: `You are the Documentation specialist.
Project: ${projectName}
Input: ${inputPath}
Output: ${outputPath}
Task:
1. Read source files from ${inputPath}
2. Generate comprehensive documentation
3. Write to ${outputPath}/index.md
4. Include code examples and usage guides
Return: Summary of documentation generated (file count, word count)`
});
```
The sub-agent receives all necessary context embedded in the prompt. Variables are resolved before sending the prompt, so the sub-agent works with concrete paths and values, not variable placeholders.
### Real-World Example: Code Review Orchestrator
Example of a simple orchestrator that validates code through multiple specialized agents:
```javascript
async function reviewCodePipeline(repositoryName, prNumber) {
const basePath = `projects/${repositoryName}/pr-${prNumber}`;
// Step 1: Security Review
const security = await runSubagent({
description: 'Scan for security vulnerabilities',
prompt: `You are the Security Reviewer specialist.
Repository: ${repositoryName}
PR: ${prNumber}
Code: ${basePath}/changes/
Task:
1. Scan code for OWASP Top 10 vulnerabilities
2. Check for injection attacks, auth flaws
3. Write findings to ${basePath}/security-review.md
Return: List of critical, high, and medium issues found`
});
// Step 2: Test Coverage Check
const coverage = await runSubagent({
description: 'Verify test coverage for changes',
prompt: `You are the Test Coverage specialist.
Repository: ${repositoryName}
PR: ${prNumber}
Changes: ${basePath}/changes/
Task:
1. Analyze code coverage for modified files
2. Identify untested critical paths
3. Write report to ${basePath}/coverage-report.md
Return: Current coverage percentage and gaps`
});
// Step 3: Aggregate Results
const finalReport = await runSubagent({
description: 'Compile all review findings',
prompt: `You are the Review Aggregator specialist.
Repository: ${repositoryName}
Reports: ${basePath}/*.md
Task:
1. Read all review reports from ${basePath}/
2. Synthesize findings into single report
3. Determine overall verdict (APPROVE/NEEDS_FIXES/BLOCK)
4. Write to ${basePath}/final-review.md
Return: Final verdict and executive summary`
});
return finalReport;
}
```
This pattern applies to any orchestration scenario: extract variables, call sub-agents with clear context, await results.
### Variable Best Practices
#### 1. **Clear Documentation**
Always document what variables are expected:
```markdown
## Required Variables
- **projectName**: The name of the project (string, required)
- **basePath**: Root directory for project files (path, required)
## Optional Variables
- **mode**: Processing mode - quick/standard/detailed (enum, default: standard)
- **outputFormat**: Output format - markdown/json/html (enum, default: markdown)
## Derived Variables
- **outputDir**: Automatically set to ${basePath}/output
- **logFile**: Automatically set to ${basePath}/.log.md
```
#### 2. **Consistent Naming**
Use consistent variable naming conventions:
```javascript
// Good: Clear, descriptive naming
const variables = {
projectName, // What project to work on
basePath, // Where project files are located
outputDirectory, // Where to save results
processingMode, // How to process (detail level)
configurationPath // Where config files are
};
// Avoid: Ambiguous or inconsistent
const bad_variables = {
name, // Too generic
path, // Unclear which path
mode, // Too short
config // Too vague
};
```
#### 3. **Validation and Constraints**
Document valid values and constraints:
```markdown
## Variable Constraints
**projectName**:
- Type: string (alphanumeric, hyphens, underscores allowed)
- Length: 1-100 characters
- Required: yes
- Pattern: `/^[a-zA-Z0-9_-]+$/`
**processingMode**:
- Type: enum
- Valid values: "quick" (< 5min), "standard" (5-15min), "detailed" (15+ min)
- Default: "standard"
- Required: no
```
## MCP Server Configuration (Organization/Enterprise Only)
MCP servers extend agent capabilities with additional tools. Only supported for organization and enterprise-level agents.
### Configuration Format
```yaml
---
name: my-custom-agent
description: 'Agent with MCP integration'
tools: ['read', 'edit', 'custom-mcp/tool-1']
mcp-servers:
custom-mcp:
type: 'local'
command: 'some-command'
args: ['--arg1', '--arg2']
tools: ["*"]
env:
ENV_VAR_NAME: ${{ secrets.API_KEY }}
---
```
### MCP Server Properties
- **type**: Server type (`'local'` or `'stdio'`)
- **command**: Command to start the MCP server
- **args**: Array of command arguments
- **tools**: Tools to enable from this server (`["*"]` for all)
- **env**: Environment variables (supports secrets)
### Environment Variables and Secrets
Secrets must be configured in repository settings under "copilot" environment.
**Supported syntax**:
```yaml
env:
# Environment variable only
VAR_NAME: COPILOT_MCP_ENV_VAR_VALUE
# Variable with header
VAR_NAME: $COPILOT_MCP_ENV_VAR_VALUE
VAR_NAME: ${COPILOT_MCP_ENV_VAR_VALUE}
# GitHub Actions-style (YAML only)
VAR_NAME: ${{ secrets.COPILOT_MCP_ENV_VAR_VALUE }}
VAR_NAME: ${{ var.COPILOT_MCP_ENV_VAR_VALUE }}
```
## File Organization and Naming
### Repository-Level Agents
- Location: `.github/agents/`
- Scope: Available only in the specific repository
- Access: Uses repository-configured MCP servers
### Organization/Enterprise-Level Agents
- Location: `.github-private/agents/` (then move to `agents/` root)
- Scope: Available across all repositories in org/enterprise
- Access: Can configure dedicated MCP servers
### Naming Conventions
- Use lowercase with hyphens: `test-specialist.agent.md`
- Name should reflect agent purpose
- Filename becomes default agent name (if `name` not specified)
- Allowed characters: `.`, `-`, `_`, `a-z`, `A-Z`, `0-9`
## Agent Processing and Behavior
### Versioning
- Based on Git commit SHAs for the agent file
- Create branches/tags for different agent versions
- Instantiated using latest version for repository/branch
- PR interactions use same agent version for consistency
### Name Conflicts
Priority (highest to lowest):
1. Repository-level agent
2. Organization-level agent
3. Enterprise-level agent
Lower-level configurations override higher-level ones with the same name.
### Tool Processing
- `tools` list filters available tools (built-in and MCP)
- No tools specified = all tools enabled
- Empty list (`[]`) = all tools disabled
- Specific list = only those tools enabled
- Unrecognized tool names are ignored (allows environment-specific tools)
### MCP Server Processing Order
1. Out-of-the-box MCP servers (e.g., GitHub MCP)
2. Custom agent MCP configuration (org/enterprise only)
3. Repository-level MCP configurations
Each level can override settings from previous levels.
## Agent Creation Checklist
### Frontmatter
- [ ] `description` field present and descriptive (50-150 chars)
- [ ] `description` wrapped in single quotes
- [ ] `name` specified (optional but recommended)
- [ ] `tools` configured appropriately (or intentionally omitted)
- [ ] `model` specified for optimal performance
- [ ] `target` set if environment-specific
- [ ] `infer` set to `false` if manual selection required
### Prompt Content
- [ ] Clear agent identity and role defined
- [ ] Core responsibilities listed explicitly
- [ ] Approach and methodology explained
- [ ] Guidelines and constraints specified
- [ ] Output expectations documented
- [ ] Examples provided where helpful
- [ ] Instructions are specific and actionable
- [ ] Scope and boundaries clearly defined
- [ ] Total content under 30,000 characters
### File Structure
- [ ] Filename follows lowercase-with-hyphens convention
- [ ] File placed in correct directory (`.github/agents/` or `agents/`)
- [ ] Filename uses only allowed characters
- [ ] File extension is `.agent.md`
### Quality Assurance
- [ ] Agent purpose is unique and not duplicative
- [ ] Tools are minimal and necessary
- [ ] Instructions are clear and unambiguous
- [ ] Agent has been tested with representative tasks
- [ ] Documentation references are current
- [ ] Security considerations addressed (if applicable)
## Common Agent Patterns
### Testing Specialist
**Purpose**: Focus on test coverage and quality
**Tools**: All tools (for comprehensive test creation)
**Approach**: Analyze, identify gaps, write tests, avoid production code changes
### Implementation Planner
**Purpose**: Create detailed technical plans and specifications
**Tools**: Limited to `['read', 'search', 'edit']`
**Approach**: Analyze requirements, create documentation, avoid implementation
### Code Reviewer
**Purpose**: Review code quality and provide feedback
**Tools**: `['read', 'search']` only
**Approach**: Analyze, suggest improvements, no direct modifications
### Refactoring Specialist
**Purpose**: Improve code structure and maintainability
**Tools**: `['read', 'search', 'edit']`
**Approach**: Analyze patterns, propose refactorings, implement safely
### Security Auditor
**Purpose**: Identify security issues and vulnerabilities
**Tools**: `['read', 'search', 'web']`
**Approach**: Scan code, check against OWASP, report findings
## Common Mistakes to Avoid
### Frontmatter Errors
- ❌ Missing `description` field
- ❌ Description not wrapped in quotes
- ❌ Invalid tool names without checking documentation
- ❌ Incorrect YAML syntax (indentation, quotes)
### Tool Configuration Issues
- ❌ Granting excessive tool access unnecessarily
- ❌ Missing required tools for agent's purpose
- ❌ Not using tool aliases consistently
- ❌ Forgetting MCP server namespace (`server-name/tool`)
### Prompt Content Problems
- ❌ Vague, ambiguous instructions
- ❌ Conflicting or contradictory guidelines
- ❌ Lack of clear scope definition
- ❌ Missing output expectations
- ❌ Overly verbose instructions (exceeding character limits)
- ❌ No examples or context for complex tasks
### Organizational Issues
- ❌ Filename doesn't reflect agent purpose
- ❌ Wrong directory (confusing repo vs org level)
- ❌ Using spaces or special characters in filename
- ❌ Duplicate agent names causing conflicts
## Testing and Validation
### Manual Testing
1. Create the agent file with proper frontmatter
2. Reload VS Code or refresh GitHub.com
3. Select the agent from the dropdown in Copilot Chat
4. Test with representative user queries
5. Verify tool access works as expected
6. Confirm output meets expectations
### Integration Testing
- Test agent with different file types in scope
- Verify MCP server connectivity (if configured)
- Check agent behavior with missing context
- Test error handling and edge cases
- Validate agent switching and handoffs
### Quality Checks
- Run through agent creation checklist
- Review against common mistakes list
- Compare with example agents in repository
- Get peer review for complex agents
- Document any special configuration needs
## Additional Resources
### Official Documentation
- [Creating Custom Agents](https://docs.github.com/en/copilot/how-tos/use-copilot-agents/coding-agent/create-custom-agents)
- [Custom Agents Configuration](https://docs.github.com/en/copilot/reference/custom-agents-configuration)
- [Custom Agents in VS Code](https://code.visualstudio.com/docs/copilot/customization/custom-agents)
- [MCP Integration](https://docs.github.com/en/copilot/how-tos/use-copilot-agents/coding-agent/extend-coding-agent-with-mcp)
### Community Resources
- [Awesome Copilot Agents Collection](https://github.com/github/awesome-copilot/tree/main/agents)
- [Customization Library Examples](https://docs.github.com/en/copilot/tutorials/customization-library/custom-agents)
- [Your First Custom Agent Tutorial](https://docs.github.com/en/copilot/tutorials/customization-library/custom-agents/your-first-custom-agent)
### Related Files
- [Prompt Files Guidelines](./prompt.instructions.md) - For creating prompt files
- [Instructions Guidelines](./instructions.instructions.md) - For creating instruction files
## Version Compatibility Notes
### GitHub.com (Coding Agent)
- ✅ Fully supports all standard frontmatter properties
- ✅ Repository and org/enterprise level agents
- ✅ MCP server configuration (org/enterprise)
- ❌ Does not support `model`, `argument-hint`, `handoffs` properties
### VS Code / JetBrains / Eclipse / Xcode
- ✅ Supports `model` property for AI model selection
- ✅ Supports `argument-hint` and `handoffs` properties
- ✅ User profile and workspace-level agents
- ❌ Cannot configure MCP servers at repository level
- ⚠️ Some properties may behave differently
When creating agents for multiple environments, focus on common properties and test in all target environments. Use `target` property to create environment-specific agents when necessary.

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---
applyTo: 'k8s/**/*.yaml,k8s/**/*.yml,manifests/**/*.yaml,manifests/**/*.yml,deploy/**/*.yaml,deploy/**/*.yml,charts/**/templates/**/*.yaml,charts/**/templates/**/*.yml'
description: 'Best practices for Kubernetes YAML manifests including labeling conventions, security contexts, pod security, resource management, probes, and validation commands'
---
# Kubernetes Manifests Instructions
## Your Mission
Create production-ready Kubernetes manifests that prioritize security, reliability, and operational excellence with consistent labeling, proper resource management, and comprehensive health checks.
## Labeling Conventions
**Required Labels** (Kubernetes recommended):
- `app.kubernetes.io/name`: Application name
- `app.kubernetes.io/instance`: Instance identifier
- `app.kubernetes.io/version`: Version
- `app.kubernetes.io/component`: Component role
- `app.kubernetes.io/part-of`: Application group
- `app.kubernetes.io/managed-by`: Management tool
**Additional Labels**:
- `environment`: Environment name
- `team`: Owning team
- `cost-center`: For billing
**Useful Annotations**:
- Documentation and ownership
- Monitoring: `prometheus.io/scrape`, `prometheus.io/port`, `prometheus.io/path`
- Change tracking: git commit, deployment date
## SecurityContext Defaults
**Pod-level**:
- `runAsNonRoot: true`
- `runAsUser` and `runAsGroup`: Specific IDs
- `fsGroup`: File system group
- `seccompProfile.type: RuntimeDefault`
**Container-level**:
- `allowPrivilegeEscalation: false`
- `readOnlyRootFilesystem: true` (with tmpfs mounts for writable dirs)
- `capabilities.drop: [ALL]` (add only what's needed)
## Pod Security Standards
Use Pod Security Admission:
- **Restricted** (recommended for production): Enforces security hardening
- **Baseline**: Minimal security requirements
- Apply at namespace level
## Resource Requests and Limits
**Always define**:
- Requests: Guaranteed minimum (scheduling)
- Limits: Maximum allowed (prevents exhaustion)
**QoS Classes**:
- **Guaranteed**: requests == limits (best for critical apps)
- **Burstable**: requests < limits (flexible resource use)
- **BestEffort**: No resources defined (avoid in production)
## Health Probes
**Liveness**: Restart unhealthy containers
**Readiness**: Control traffic routing
**Startup**: Protect slow-starting applications
Configure appropriate delays, periods, timeouts, and thresholds for each.
## Rollout Strategies
**Deployment Strategy**:
- `RollingUpdate` with `maxSurge` and `maxUnavailable`
- Set `maxUnavailable: 0` for zero-downtime
**High Availability**:
- Minimum 2-3 replicas
- Pod Disruption Budget (PDB)
- Anti-affinity rules (spread across nodes/zones)
- Horizontal Pod Autoscaler (HPA) for variable load
## Validation Commands
**Pre-deployment**:
- `kubectl apply --dry-run=client -f manifest.yaml`
- `kubectl apply --dry-run=server -f manifest.yaml`
- `kubeconform -strict manifest.yaml` (schema validation)
- `helm template ./chart | kubeconform -strict` (for Helm)
**Policy Validation**:
- OPA Conftest, Kyverno, or Datree
## Rollout & Rollback
**Deploy**:
- `kubectl apply -f manifest.yaml`
- `kubectl rollout status deployment/NAME`
**Rollback**:
- `kubectl rollout undo deployment/NAME`
- `kubectl rollout undo deployment/NAME --to-revision=N`
- `kubectl rollout history deployment/NAME`
**Restart**:
- `kubectl rollout restart deployment/NAME`
## Manifest Checklist
- [ ] Labels: Standard labels applied
- [ ] Annotations: Documentation and monitoring
- [ ] Security: runAsNonRoot, readOnlyRootFilesystem, dropped capabilities
- [ ] Resources: Requests and limits defined
- [ ] Probes: Liveness, readiness, startup configured
- [ ] Images: Specific tags (never :latest)
- [ ] Replicas: Minimum 2-3 for production
- [ ] Strategy: RollingUpdate with appropriate surge/unavailable
- [ ] PDB: Defined for production
- [ ] Anti-affinity: Configured for HA
- [ ] Graceful shutdown: terminationGracePeriodSeconds set
- [ ] Validation: Dry-run and kubeconform passed
- [ ] Secrets: In Secrets resource, not ConfigMaps
- [ ] NetworkPolicy: Least-privilege access (if applicable)
## Best Practices Summary
1. Use standard labels and annotations
2. Always run as non-root with dropped capabilities
3. Define resource requests and limits
4. Implement all three probe types
5. Pin image tags to specific versions
6. Configure anti-affinity for HA
7. Set Pod Disruption Budgets
8. Use rolling updates with zero unavailability
9. Validate manifests before applying
10. Enable read-only root filesystem when possible

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---
description: 'Guidelines and best practices for building TypeSpec-based declarative agents and API plugins for Microsoft 365 Copilot'
applyTo: '**/*.tsp'
---
# TypeSpec for Microsoft 365 Copilot Development Guidelines
## Core Principles
When working with TypeSpec for Microsoft 365 Copilot:
1. **Type Safety First**: Leverage TypeSpec's strong typing for all models and operations
2. **Declarative Approach**: Use decorators to describe intent, not implementation
3. **Scoped Capabilities**: Always scope capabilities to specific resources when possible
4. **Clear Instructions**: Write explicit, detailed agent instructions
5. **User-Centric**: Design for the end-user experience in Microsoft 365 Copilot
## File Organization
### Standard Structure
```
project/
├── appPackage/
│ ├── cards/ # Adaptive Card templates
│ │ └── *.json
│ ├── .generated/ # Generated manifests (auto-generated)
│ └── manifest.json # Teams app manifest
├── src/
│ ├── main.tsp # Agent definition
│ └── actions.tsp # API operations (for plugins)
├── m365agents.yml # Agents Toolkit configuration
└── package.json
```
### Import Statements
Always include required imports at the top of TypeSpec files:
```typescript
import "@typespec/http";
import "@typespec/openapi3";
import "@microsoft/typespec-m365-copilot";
using TypeSpec.Http;
using TypeSpec.M365.Copilot.Agents; // For agents
using TypeSpec.M365.Copilot.Actions; // For API plugins
```
## Agent Development Best Practices
### Agent Declaration
```typescript
@agent({
name: "Role-Based Name", // e.g., "Customer Support Assistant"
description: "Clear, concise description under 1,000 characters"
})
```
- Use role-based names that describe what the agent does
- Make descriptions informative but concise
- Avoid generic names like "Helper" or "Bot"
### Instructions
```typescript
@instructions("""
You are a [specific role] specialized in [domain].
Your responsibilities include:
- [Key responsibility 1]
- [Key responsibility 2]
When helping users:
- [Behavioral guideline 1]
- [Behavioral guideline 2]
You should NOT:
- [Constraint 1]
- [Constraint 2]
""")
```
- Write in second person ("You are...")
- Be specific about the agent's role and expertise
- Define both what to do AND what not to do
- Keep under 8,000 characters
- Use clear, structured formatting
### Conversation Starters
```typescript
@conversationStarter(#{
title: "Action-Oriented Title", // e.g., "Check Status"
text: "Specific example query" // e.g., "What's the status of my ticket?"
})
```
- Provide 2-4 diverse starters
- Make each showcase a different capability
- Use action-oriented titles
- Write realistic example queries
### Capabilities - Knowledge Sources
**Web Search** - Scope to specific sites when possible:
```typescript
op webSearch is AgentCapabilities.WebSearch<Sites = [
{ url: "https://learn.microsoft.com" },
{ url: "https://docs.microsoft.com" }
]>;
```
**OneDrive and SharePoint** - Use URLs or IDs:
```typescript
op oneDriveAndSharePoint is AgentCapabilities.OneDriveAndSharePoint<
ItemsByUrl = [
{ url: "https://contoso.sharepoint.com/sites/Engineering" }
]
>;
```
**Teams Messages** - Specify channels/chats:
```typescript
op teamsMessages is AgentCapabilities.TeamsMessages<Urls = [
{ url: "https://teams.microsoft.com/l/channel/..." }
]>;
```
**Email** - Scope to specific folders:
```typescript
op email is AgentCapabilities.Email<
Folders = [
{ folderId: "Inbox" },
{ folderId: "SentItems" }
],
SharedMailbox = "support@contoso.com" // Optional
>;
```
**People** - No scoping needed:
```typescript
op people is AgentCapabilities.People;
```
**Copilot Connectors** - Specify connection IDs:
```typescript
op copilotConnectors is AgentCapabilities.GraphConnectors<
Connections = [
{ connectionId: "your-connector-id" }
]
>;
```
**Dataverse** - Scope to specific tables:
```typescript
op dataverse is AgentCapabilities.Dataverse<
KnowledgeSources = [
{
hostName: "contoso.crm.dynamics.com";
tables: [
{ tableName: "account" },
{ tableName: "contact" }
];
}
]
>;
```
### Capabilities - Productivity Tools
```typescript
// Python code execution
op codeInterpreter is AgentCapabilities.CodeInterpreter;
// Image generation
op graphicArt is AgentCapabilities.GraphicArt;
// Meeting content access
op meetings is AgentCapabilities.Meetings;
// Specialized AI models
op scenarioModels is AgentCapabilities.ScenarioModels<
ModelsById = [
{ id: "model-id" }
]
>;
```
## API Plugin Development Best Practices
### Service Definition
```typescript
@service
@actions(#{
nameForHuman: "User-Friendly API Name",
descriptionForHuman: "What users will understand",
descriptionForModel: "What the model needs to know",
contactEmail: "support@company.com",
privacyPolicyUrl: "https://company.com/privacy",
legalInfoUrl: "https://company.com/terms"
})
@server("https://api.example.com", "API Name")
@useAuth([AuthType]) // If authentication needed
namespace APINamespace {
// Operations here
}
```
### Operation Definition
```typescript
@route("/resource/{id}")
@get
@action
@card(#{
dataPath: "$.items",
title: "$.title",
file: "cards/card.json"
})
@capabilities(#{
confirmation: #{
type: "AdaptiveCard",
title: "Confirm Action",
body: "Confirm with {{ function.parameters.param }}"
}
})
@reasoning("Consider X when Y")
@responding("Present results as Z")
op getResource(
@path id: string,
@query filter?: string
): ResourceResponse;
```
### Models
```typescript
model Resource {
id: string;
name: string;
description?: string; // Optional fields
status: "active" | "inactive"; // Union types for enums
@format("date-time")
createdAt: utcDateTime;
@format("uri")
url?: string;
}
model ResourceList {
items: Resource[];
totalCount: int32;
nextPage?: string;
}
```
### Authentication
**API Key**
```typescript
@useAuth(ApiKeyAuth<ApiKeyLocation.header, "X-API-Key">)
// Or with reference ID
@useAuth(Auth)
@authReferenceId("${{ENV_VAR_REFERENCE_ID}}")
model Auth is ApiKeyAuth<ApiKeyLocation.header, "X-API-Key">;
```
**OAuth2**
```typescript
@useAuth(OAuth2Auth<[{
type: OAuth2FlowType.authorizationCode;
authorizationUrl: "https://auth.example.com/authorize";
tokenUrl: "https://auth.example.com/token";
refreshUrl: "https://auth.example.com/refresh";
scopes: ["read", "write"];
}]>)
// Or with reference ID
@useAuth(Auth)
@authReferenceId("${{OAUTH_REFERENCE_ID}}")
model Auth is OAuth2Auth<[...]>;
```
## Naming Conventions
### Files
- `main.tsp` - Agent definition
- `actions.tsp` - API operations
- `[feature].tsp` - Additional feature files
- `cards/*.json` - Adaptive Card templates
### TypeSpec Elements
- **Namespaces**: PascalCase (e.g., `CustomerSupportAgent`)
- **Operations**: camelCase (e.g., `listProjects`, `createTicket`)
- **Models**: PascalCase (e.g., `Project`, `TicketResponse`)
- **Model Properties**: camelCase (e.g., `projectId`, `createdDate`)
## Common Patterns
### Multi-Capability Agent
```typescript
@agent("Knowledge Worker", "Description")
@instructions("...")
namespace KnowledgeWorker {
op webSearch is AgentCapabilities.WebSearch;
op files is AgentCapabilities.OneDriveAndSharePoint;
op people is AgentCapabilities.People;
}
```
### CRUD API Plugin
```typescript
namespace ProjectAPI {
@route("/projects") @get @action
op list(): Project[];
@route("/projects/{id}") @get @action
op get(@path id: string): Project;
@route("/projects") @post @action
@capabilities(#{confirmation: ...})
op create(@body project: CreateProject): Project;
@route("/projects/{id}") @patch @action
@capabilities(#{confirmation: ...})
op update(@path id: string, @body project: UpdateProject): Project;
@route("/projects/{id}") @delete @action
@capabilities(#{confirmation: ...})
op delete(@path id: string): void;
}
```
### Adaptive Card Data Binding
```json
{
"type": "AdaptiveCard",
"$schema": "http://adaptivecards.io/schemas/adaptive-card.json",
"version": "1.5",
"body": [
{
"type": "Container",
"$data": "${$root}",
"items": [
{
"type": "TextBlock",
"text": "Title: ${if(title, title, 'N/A')}",
"wrap": true
}
]
}
]
}
```
## Validation and Testing
### Before Provisioning
1. Run TypeSpec validation: `npm run build` or use Agents Toolkit
2. Check all file paths in `@card` decorators exist
3. Verify authentication references match configuration
4. Ensure capability scoping is appropriate
5. Review instructions for clarity and length
### Testing Strategy
1. **Provision**: Deploy to development environment
2. **Test**: Use Microsoft 365 Copilot at https://m365.cloud.microsoft/chat
3. **Debug**: Enable Copilot developer mode for orchestrator insights
4. **Iterate**: Refine based on actual behavior
5. **Validate**: Test all conversation starters and capabilities
## Performance Optimization
1. **Scope Capabilities**: Don't grant access to all data if only subset needed
2. **Limit Operations**: Only expose API operations the agent actually uses
3. **Efficient Models**: Keep response models focused on necessary data
4. **Card Optimization**: Use conditional rendering (`$when`) in Adaptive Cards
5. **Caching**: Design APIs with appropriate caching headers
## Security Best Practices
1. **Authentication**: Always use authentication for non-public APIs
2. **Scoping**: Limit capability access to minimum required resources
3. **Validation**: Validate all inputs in API operations
4. **Secrets**: Use environment variables for sensitive data
5. **References**: Use `@authReferenceId` for production credentials
6. **Permissions**: Request minimum necessary OAuth scopes
## Error Handling
```typescript
model ErrorResponse {
error: {
code: string;
message: string;
details?: ErrorDetail[];
};
}
model ErrorDetail {
field?: string;
message: string;
}
```
## Documentation
Include comments in TypeSpec for complex operations:
```typescript
/**
* Retrieves project details with associated tasks and team members.
*
* @param id - Unique project identifier
* @param includeArchived - Whether to include archived tasks
* @returns Complete project information
*/
@route("/projects/{id}")
@get
@action
op getProjectDetails(
@path id: string,
@query includeArchived?: boolean
): ProjectDetails;
```
## Common Pitfalls to Avoid
1. ❌ Generic agent names ("Helper Bot")
2. ❌ Vague instructions ("Help users with things")
3. ❌ No capability scoping (accessing all data)
4. ❌ Missing confirmations on destructive operations
5. ❌ Overly complex Adaptive Cards
6. ❌ Hard-coded credentials in TypeSpec files
7. ❌ Missing error response models
8. ❌ Inconsistent naming conventions
9. ❌ Too many capabilities (use only what's needed)
10. ❌ Instructions over 8,000 characters
## Resources
- [TypeSpec Official Docs](https://typespec.io/)
- [Microsoft 365 Copilot Extensibility](https://learn.microsoft.com/microsoft-365-copilot/extensibility/)
- [Agents Toolkit](https://aka.ms/M365AgentsToolkit)
- [Adaptive Cards Designer](https://adaptivecards.io/designer/)