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Agent Skills

A repository of skills, prompts, and MCP configurations for AI coding agents working with Azure SDKs and Microsoft AI Foundry services.

⚠️ Fresh Information First

Azure SDKs and Foundry APIs change constantly. Never work with stale knowledge.

Before implementing anything with Azure/Foundry SDKs:

  1. Search official docs first — Use the Microsoft Docs MCP (microsoft-docs) to get current API signatures, parameters, and patterns
  2. Verify SDK versions — Check pip show <package> for installed versions; APIs differ between versions
  3. Don't trust cached knowledge — Your training data is outdated. The SDK you "know" may have breaking changes.
# Always do this first
1. Search Microsoft Learn for current docs
2. Check Context7 for indexed Foundry documentation (updated daily)
3. Verify against actual installed package version

If you skip this step and use outdated patterns, you will produce broken code.

Core Principles

These principles reduce common LLM coding mistakes. Apply them to every task.

1. Think Before Coding

Don't assume. Don't hide confusion. Surface tradeoffs.

  • State assumptions explicitly. If uncertain, ask.
  • If multiple interpretations exist, present them — don't pick silently.
  • If a simpler approach exists, say so. Push back when warranted.
  • If something is unclear, stop. Name what's confusing. Ask.

2. Simplicity First

Minimum code that solves the problem. Nothing speculative.

  • No features beyond what was asked.
  • No abstractions for single-use code.
  • No "flexibility" or "configurability" that wasn't requested.
  • No error handling for impossible scenarios.
  • If you write 200 lines and it could be 50, rewrite it.

The test: Would a senior engineer say this is overcomplicated? If yes, simplify.

3. Surgical Changes

Touch only what you must. Clean up only your own mess.

When editing existing code: - Don't "improve" adjacent code, comments, or formatting. - Don't refactor things that aren't broken. - Match existing style, even if you'd do it differently. - If you notice unrelated dead code, mention it — don't delete it.

When your changes create orphans: - Remove imports/variables/functions that YOUR changes made unused. - Don't remove pre-existing dead code unless asked.

The test: Every changed line should trace directly to the user's request.

4. Goal-Driven Execution (TDD)

Define success criteria. Loop until verified.

Transform tasks into verifiable goals:

Instead of... Transform to...
"Add validation" "Write tests for invalid inputs, then make them pass"
"Fix the bug" "Write a test that reproduces it, then make it pass"
"Refactor X" "Ensure tests pass before and after"

For multi-step tasks, state a brief plan: 

1. [Step] → verify: [check]
2. [Step] → verify: [check]
3. [Step] → verify: [check]

Strong success criteria let you loop independently. Weak criteria ("make it work") require constant clarification.

Clean Architecture

Follow these layered boundaries when building features:

┌─────────────────────────────────────┐
│           Presentation              │  ← Routers, API endpoints
├─────────────────────────────────────┤
│           Application               │  ← Use cases, orchestration
├─────────────────────────────────────┤
│             Domain                  │  ← Entities, business rules
├─────────────────────────────────────┤
│          Infrastructure             │  ← Database, external APIs
└─────────────────────────────────────┘

Rules: - Dependencies point inward (outer layers depend on inner layers) - Domain layer has no external dependencies - Infrastructure implements interfaces defined in inner layers - Each layer should be testable in isolation

SDK Quick Reference

Package Purpose Install
azure-ai-projects Foundry project client, agents, evals, connections pip install azure-ai-projects
azure-ai-agents Standalone agents client (use via projects) pip install azure-ai-agents
azure-search-documents Azure AI Search SDK pip install azure-search-documents
azure-identity Authentication pip install azure-identity

Authentication Pattern

Always use DefaultAzureCredential for production:

from azure.identity import DefaultAzureCredential
from azure.ai.projects import AIProjectClient

credential = DefaultAzureCredential()
client = AIProjectClient(
    endpoint="https://<resource>.services.ai.azure.com/api/projects/<project>",
    credential=credential
)

Environment Variables

AZURE_AI_PROJECT_ENDPOINT=https://<resource>.services.ai.azure.com/api/projects/<project>
AZURE_AI_MODEL_DEPLOYMENT_NAME=gpt-4o-mini

Skills

Skills are domain-specific knowledge packages in .github/skills/. Each has a SKILL.md with: - YAML frontmatter (name, description) — triggers skill loading - Markdown body — loaded only when skill activates

⚠️ Temporary duplication note: Skills in .github/skills/ are duplicated from .github/plugins/*/skills/ to support npx skills add microsoft/skills installation. The plugin directories remain the canonical source. This duplication is temporary until the skills installer supports symlinks/pointer files.

Quick Start

# Install skills using skills.sh
npx skills add microsoft/skills

Skill Catalog

Location: .github/skills/ • 133 skills • See README.md#skill-catalog

Language Skills Suffix Examples
Core 6 — mcp-builder, skill-creator, copilot-sdk
Python 41 -py azure-ai-projects-py, azure-cosmos-py, azure-ai-ml-py
.NET 29 -dotnet azure-ai-projects-dotnet, azure-resource-manager-cosmosdb-dotnet, azure-security-keyvault-keys-dotnet
TypeScript 25 -ts azure-ai-projects-ts, azure-storage-blob-ts, aspire-ts
Java 26 -java azure-ai-projects-java, azure-cosmos-java, azure-eventhub-java

Skill Selection

Only load skills relevant to the current task. Loading all skills causes context rot — diluted attention and conflated patterns.

Creating New Skills

Detailed guide: Load the /skill-creator skill for comprehensive instructions.

Prerequisites: User MUST provide SDK context: - SDK package name (e.g., azure-ai-agents) - Documentation URL or GitHub repository - Target language (py/dotnet/ts/java)

Quick workflow:

  1. Create skill in .github/skills/<skill-name>/SKILL.md 

    # Naming: azure-<service>-<language>
# Example: azure-ai-agents-py

  2. Categorize with symlink in skills/<language>/<category>/ 

    cd skills/python/foundry
ln -s ../../../.github/skills/azure-ai-agents-py agents

  3. Create tests

  4. references/acceptance-criteria.md — correct/incorrect patterns

  5. tests/scenarios/<skill>/scenarios.yaml — test scenarios

  6. Verify 

    cd tests && pnpm harness <skill-name> --mock --verbose

  7. Update README.md — Add to skill catalog

Product area categories:

Category Skills
foundry AI agents, projects, inference, search
data Storage, Cosmos DB, Tables
messaging Event Hubs, Service Bus, Event Grid
monitoring OpenTelemetry, App Insights
identity Authentication, credentials
security Key Vault
integration API Management, App Configuration

MCP Servers

Pre-configured Model Context Protocol servers in .vscode/mcp.json provide additional capabilities:

MCP Purpose
microsoft-docs Search Microsoft Learn — Official Azure/Foundry docs. Use this FIRST.
context7 Indexed Foundry documentation with semantic search (updated daily via GitHub workflow)
deepwiki Ask questions about GitHub repositories

Development Tools

MCP Purpose
github GitHub API operations
playwright Browser automation and testing
terraform Infrastructure as code
eslint JavaScript/TypeScript linting

Utilities

MCP Purpose
sequentialthinking Step-by-step reasoning for complex problems
memory Persistent memory across sessions
markitdown Convert documents to markdown

Usage: MCPs are available when configured in your editor. Use microsoft-docs to search official documentation before implementing Azure SDK code.

Conventions

Code Style

  • Prefer async/await for all Azure SDK I/O
  • Use context managers: with client: or async with client:
  • Close clients explicitly or use context managers
  • Use create_or_update_* for idempotent operations
  • Use type hints on all function signatures

Git & GitHub

  • Always use gh CLI for GitHub operations (PRs, issues, etc.) — never the MCP github-create_pull_request tool
  • Use gh pr create for pull requests, gh issue create for issues

Clean Code Checklist

Before completing any code change:

  • Functions do one thing
  • Names are descriptive and intention-revealing
  • No magic numbers or strings (use constants)
  • Error handling is explicit (no empty catch blocks)
  • No commented-out code
  • Tests cover the change

Testing Patterns

# Arrange
service = ProjectService()
expected = Project(id="123", name="test")

# Act
result = await service.get_project("123")

# Assert
assert result == expected

For Azure SDK tests: - Use pytest-asyncio for async tests - Mock Azure clients at the service boundary - Test both success and error paths

Workflow: Adding a Feature

  1. Clarify — Understand the requirement. Ask if unclear.
  2. Test First — Write a failing test that defines success.
  3. Implement — Write minimum code to pass the test.
  4. Refactor — Clean up while tests stay green.
  5. Verify — Run full test suite, check types, lint.
# Example workflow
pytest tests/test_feature.py -v     # Run specific tests
mypy src/                           # Type check
ruff check src/                     # Lint

Do's and Don'ts

Do

  • ✅ Use DefaultAzureCredential for authentication
  • ✅ Use async/await for all Azure SDK operations
  • ✅ Write tests before or alongside implementation
  • ✅ Keep functions small and focused
  • ✅ Match existing patterns in the codebase
  • ✅ Use gh CLI for all GitHub operations (PRs, issues, releases)

Don't

  • ❌ Hardcode credentials or endpoints
  • ❌ Suppress type errors (as any, @ts-ignore, # type: ignore)
  • ❌ Leave empty exception handlers
  • ❌ Refactor unrelated code while fixing bugs
  • ❌ Add dependencies without justification
  • ❌ Use GitHub MCP tools for write operations (enterprise token restrictions)

Success Indicators

These principles are working if you see:

  • Fewer unnecessary changes in diffs
  • Fewer rewrites due to overcomplication
  • Clarifying questions come before implementation (not after mistakes)
  • Clean, minimal PRs without drive-by refactoring
  • Tests that document expected behavior