skill-creator

name	skill-creator
description	Guide for creating effective skills for AI coding agents working with Azure SDKs and Microsoft Foundry services. Use when creating new skills or updating existing skills.

Skill Creator

Guide for creating skills that extend AI agent capabilities, with emphasis on Azure SDKs and Microsoft Foundry.

Required Context: When creating SDK or API skills, users MUST provide the SDK package name, documentation URL, or repository reference for the skill to be based on.

About Skills

Skills are modular knowledge packages that transform general-purpose agents into specialized experts:

1. Procedural knowledge — Multi-step workflows for specific domains
2. SDK expertise — API patterns, authentication, error handling for Azure services
3. Domain context — Schemas, business logic, company-specific patterns
4. Bundled resources — Scripts, references, templates for complex tasks

---

Core Principles

1. Concise is Key

The context window is a shared resource. Challenge each piece: "Does this justify its token cost?"

For domain/procedural skills: Agents are already capable. Only add what they don't already know.

For SDK/API skills: Users MUST provide SDK package name, documentation URL, or repository reference. The skill cannot be created without this context.

2. Fresh Documentation First

Azure SDKs change constantly. Skills should instruct agents to verify documentation:

## Before Implementation

Search `microsoft-docs` MCP for current API patterns:

- Query: "[SDK name] [operation] python"
- Verify: Parameters match your installed SDK version

3. Degrees of Freedom

Match specificity to implementation constraints. High freedom when approaches vary; low freedom when precise execution is required:

Freedom	When	Example
High	Multiple valid approaches	Text guidelines
Medium	Preferred pattern with variation	Pseudocode
Low	Must be exact	Specific scripts

4. Progressive Disclosure

Skills load in three levels:

1. Metadata (~100 words) — Always in context
2. SKILL.md body (<5k words) — When skill triggers
3. References (unlimited) — As needed

Keep SKILL.md under 500 lines. Split into reference files when approaching this limit.

---

Skill Structure

Quick reference:

skill-name/
├── SKILL.md (required)
│   ├── YAML frontmatter (name, description)
│   └── Markdown instructions
└── Bundled Resources (optional)
    ├── scripts/      — Executable code
    ├── references/   — Documentation loaded as needed
    └── assets/       — Output resources (templates, images)

For Azure SDK skills, follow the Skill Section Order below. For domain skills, use your judgment to organize logically.

SKILL.md Essentials

• Frontmatter: name and description (description triggers the skill)
• Body: Keep under 500 lines; split large skills into reference files

Bundled Resources (Optional)

Type	When to Include	Examples
scripts/	Reused code patterns	Auth setup, CLI scripts
references/	Detailed patterns/schemas	API docs, migration guides
assets/	Output templates	Boilerplate code, images

---

Creating Azure SDK Skills

When creating skills for Azure SDKs, follow these patterns consistently.

Skill Section Order

Follow this structure (based on existing Azure SDK skills):

1. Title — # SDK Name
2. Installation — pip install, npm install, etc.
3. Environment Variables — Required configuration, with an inline comment explaining when it's required . If using DefaultAzureCredentialin production,include AZURE_TOKEN_CREDENTIALS (set to prod or <specific_credential>)
4. Authentication & Lifecycle — Use a specific Microsoft Entra Token credential like ManagedIdentityCredential or WorkloadIdentityCredential for production. DefaultAzureCredential is only recommended for local development. To use DefaultAzureCredential in production, set the environment variable AZURE_TOKEN_CREDENTIALS to prod or the specific target credential. For Python skills, this section MUST start with the standard callout block (see Required Authentication & Lifecycle Callout (Python) below).
5. Core Workflow — Minimal viable example
6. Feature Tables — Clients, methods, tools
7. Best Practices — Numbered list
8. Reference Links — Table linking to /references/*.md

Required Authentication & Lifecycle Callout (Python)

Scope: Python skills (-py suffix) only. Other languages may follow their own idioms.

Every Python Azure SDK skill MUST open its ## Authentication & Lifecycle section with the following callout block, verbatim, before any code samples. This makes the two non-negotiable rules visible to users before they read or copy any client setup code.

## Authentication & Lifecycle

> **🔑 Two rules apply to every code sample below:**
>
> 1. **Prefer `DefaultAzureCredential`.** It works locally (Azure CLI / VS Code / Developer CLI) and in Azure (managed identity, workload identity) with no code change. Avoid connection strings, account/API keys — they bypass Entra audit and rotation.
>    - Local dev: `DefaultAzureCredential` works as-is.
>    - Production: set `AZURE_TOKEN_CREDENTIALS=prod` (or `AZURE_TOKEN_CREDENTIALS=<specific_credential>`) to constrain the credential chain to production-safe credentials.
> 2. **Wrap every client in a context manager** so HTTP transports, sockets, and token caches are released deterministically:
>    - Sync: `with <Client>(...) as client:`
>    - Async: `async with <Client>(...) as client:` **and** `async with DefaultAzureCredential() as credential:` (from `azure.identity.aio`)
>
> Snippets may abbreviate this setup, but production code should always follow both rules.

Placement rules:

• Insert immediately under the ## Authentication & Lifecycle heading, before the first code sample.
• Do not paraphrase or restructure the wording — the consistency across skills is the point.
• If the SDK does not support Entra ID at all (rare — e.g. some legacy speech REST endpoints, websocket APIs that require subscription keys), keep rule #2 (context managers) and replace rule #1 with a single sentence noting the SDK requires API-key auth and explaining why Entra is not yet available.
• If the SDK is async-only (e.g. azure-ai-voicelive), keep both rules but show only the async form in the bullets.
• Skip the callout entirely for non-Azure Python skills with no client lifecycle (e.g. pydantic-models-py).

Code sample enforcement. Every client construction in the skill body must demonstrate both rules:

• Show with / async with on every client instantiation in usage examples (not just the auth section).
• Show DefaultAzureCredential in the primary auth example. Do not delete API-key examples for SDKs where keys are still officially supported — many existing users (especially in regulated environments still completing their Entra rollout) need a copy-pastable working sample. Demote the keyed snippet into a clearly-labeled ### Legacy: API Key (existing keyed deployments) subsection placed after the primary DefaultAzureCredential block in the same ## Authentication & Lifecycle section. Include a one-line note that new code should use DefaultAzureCredential and that the keyed path is for existing deployments. Also add the <SERVICE>_KEY env var back to the Environment Variables block with a # Only required for the legacy API-key auth path below comment.
• A handful of services have key-specific quirks worth calling out in the Legacy subsection (e.g. azure-ai-translation-text requires a region= parameter when using a key against the global endpoint, because token-credential auth requires a custom subdomain endpoint). Surface these in the demoted block rather than dropping the example.
• For async examples, wrap DefaultAzureCredential from azure.identity.aio in async with credential: alongside the client.

Authentication Pattern (All Languages)

For local development, use DefaultAzureCredential which supports multiple auth methods. For production, use a specific credential type or configure DefaultAzureCredential with environment variable AZURE_TOKEN_CREDENTIALS set to prod or specify the target credential.

If configuring a Rust skill, use DeveloperToolsCredential for local development and ManagedIdentityCredential for production. The Rust SDK does not support DefaultAzureCredential, so explicitly use the appropriate credential in each environment.

# Python — note: client is wrapped in `with` for deterministic cleanup
from azure.identity import DefaultAzureCredential, ManagedIdentityCredential
# Local dev: DefaultAzureCredential. Production: set AZURE_TOKEN_CREDENTIALS=prod or AZURE_TOKEN_CREDENTIALS=<specific_credential>
credential = DefaultAzureCredential(require_envvar=True)
# Or use a specific credential directly in production:
# See https://learn.microsoft.com/python/api/overview/azure/identity-readme?view=azure-python#credential-classes
# credential = ManagedIdentityCredential()
with ServiceClient(endpoint, credential) as client:
    client.do_thing()

// C#
using Azure.Identity;

// Local dev: DefaultAzureCredential. Production: set AZURE_TOKEN_CREDENTIALS=prod or AZURE_TOKEN_CREDENTIALS=<specific_credential>
var credential = new DefaultAzureCredential(
    DefaultAzureCredential.DefaultEnvironmentVariableName
);
// Or use a specific credential directly in production:
// See https://learn.microsoft.com/dotnet/api/overview/azure/identity-readme?view=azure-dotnet#credential-classes
// var credential = new ManagedIdentityCredential();
var client = new ServiceClient(new Uri(endpoint), credential);

// Java
import com.azure.identity.AzureIdentityEnvVars;
import com.azure.identity.DefaultAzureCredentialBuilder;
import com.azure.identity.ManagedIdentityCredential;
import com.azure.identity.ManagedIdentityCredentialBuilder;

// Local dev: DefaultAzureCredential. Production: set AZURE_TOKEN_CREDENTIALS=prod or AZURE_TOKEN_CREDENTIALS=<specific_credential>
TokenCredential credential = new DefaultAzureCredentialBuilder()
    .requireEnvVars(AzureIdentityEnvVars.AZURE_TOKEN_CREDENTIALS)
    .build();
// Or use a specific credential directly in production:
// See https://learn.microsoft.com/java/api/overview/azure/identity-readme?view=azure-java-stable#credential-classes
// TokenCredential credential = new ManagedIdentityCredentialBuilder().build();
ServiceClient client = new ServiceClientBuilder()
    .endpoint(endpoint)
    .credential(credential)
    .buildClient();

// TypeScript
import {
  DefaultAzureCredential,
  ManagedIdentityCredential,
} from "@azure/identity";
// Local dev: DefaultAzureCredential. Production: set AZURE_TOKEN_CREDENTIALS=prod or AZURE_TOKEN_CREDENTIALS=<specific_credential>
const credential = new DefaultAzureCredential({
  requiredEnvVars: ["AZURE_TOKEN_CREDENTIALS"],
});
// Or use a specific credential directly in production:
// See https://learn.microsoft.com/javascript/api/overview/azure/identity-readme?view=azure-node-latest#credential-classes
// const credential = new ManagedIdentityCredential();
const client = new ServiceClient(endpoint, credential);

// Rust
use azure_identity::DeveloperToolsCredential;
use azure_storage_blob::BlobServiceClient;

let credential = DeveloperToolsCredential::new(); // Local dev
let client = BlobServiceClient::new(
    "https://<account>.blob.core.windows.net/",
    credential,
    None,
)?;

Never hardcode credentials. Use environment variables.

Standard Verb Patterns

Azure SDKs use consistent verbs across all languages:

Verb	Behavior
create	Create new; fail if exists
upsert	Create or update
get	Retrieve; error if missing
list	Return collection
delete	Succeed even if missing
begin	Start long-running operation

Language-Specific Patterns

See references/azure-sdk-patterns.md for detailed patterns including:

• Python: ItemPaged, LROPoller, context managers, Sphinx docstrings. Pick sync or async for the whole skill and stick with it — never mix. Always show with / async with context managers first.
• .NET: Response<T>, Pageable<T>, Operation<T>, mocking support
• Java: Builder pattern, PagedIterable/PagedFlux, Reactor types
• TypeScript: PagedAsyncIterableIterator, AbortSignal, browser considerations
• Rust: Response<T>, Pager<T>, RequestContent::from(), .into_model(), explicit credential types, RBAC roles for Entra ID authentication.

Required Best Practices in Every Skill (User-Facing)

Python, .Net, Java, and Typescript languages

These two rules are not just authoring conventions for the skill itself — they MUST be explicitly written into every generated skill's ## Best Practices section so end users who follow the skill apply them in their own code.

Add both items verbatim (adapted only for language/SDK specifics) as the first two items of the Best Practices list. Do not assume users will infer them from examples.

Standard wording (Python; adapt for other languages):

1. **Pick sync OR async and stay consistent.** Do not mix `azure.xxx` sync clients with `azure.xxx.aio` async clients in the same call path. Choose one mode per module.
2. **Always use context managers for clients and async credentials.** Wrap every client in `with Client(...) as client:` (sync) or `async with Client(...) as client:` (async). For async `DefaultAzureCredential` from `azure.identity.aio`, also use `async with credential:` so tokens and transports are cleaned up.
3. **Use `DefaultAzureCredential`** for code that runs locally. Use a specific token credential (e.g. `ManagedIdentityCredential`, `WorkloadIdentityCredential`) for code that runs in Azure.

Variants to apply when the SDK shape differs:

Skill type	Adjust item #1 to	Adjust item #2 to
Async-only SDK (e.g. voicelive)	"This SDK is async-only; use the .aio namespace throughout."	keep standard
Async-first framework (agent framework, m365-agents)	"This SDK is async-first — use async def handlers and async with throughout."	keep standard
Provider-pattern (OpenTelemetry exporters/distro)	keep standard	"Call provider.shutdown() / flush() at process exit to flush telemetry — providers are not context managers."
REST-over-httpx skills	keep standard	"Use with httpx.Client(...) as client: (sync) or async with httpx.AsyncClient(...) as client: (async) so connections pool and close deterministically."
Identity skill	keep standard	"Use credentials as context managers (with DefaultAzureCredential() as credential:) when they own token caches / HTTP transports you want cleaned up; for async, use async with on credentials from azure.identity.aio."
FastAPI (non-Azure)	"Pick def or async def per endpoint based on whether you call async I/O; do not mix sync and blocking calls in one handler."	"Manage long-lived resources (DB pools, HTTP clients) in lifespan and inject via Depends; use with/async with for per-request resources."
Pure model/schema skill (no I/O, e.g. pydantic)	skip both — not applicable	skip

Enforcement in code examples. Every code example inside the skill must itself obey both rules, so the skill demonstrates what it prescribes:

• Do not show sync and async calls interleaved in the same example. If you must show both modes, keep the primary example in one mode and isolate the alternative into a single ### Async variant (or ### Sync variant) subsection with its own complete example.
• Every client instantiation in every example must be wrapped in with / async with. The only permitted exception is the mandatory Authentication snippet (which illustrates the credential + client construction pattern) and framework lifespan patterns where a client is owned by the app (e.g. FastAPI lifespan).
• When async credentials from azure.identity.aio appear in an example, wrap them in async with credential: alongside the client.

Rust Language

1. Use DeveloperToolsCredential for local development and ManagedIdentityCredential for production. The Rust SDK does not support DefaultAzureCredential, so explicitly use the appropriate credential in each environment.

2. Use RequestContent::from() to wrap upload data. When uploading data (e.g., blobs), wrap the content in RequestContent::from(your_data) to ensure proper handling by the SDK.

3. Assign appropriate RBAC roles for Entra ID auth. For production authentication using Entra ID, ensure the identity has the necessary RBAC role assigned (e.g., "Storage Blob Data Contributor" for blob write access).

4. Always verify package versions using crates.io. Before using a package, check its version on crates.io to ensure you are using a stable and supported release.

Handling Deprecated or Rebranded SDKs

When an Azure SDK has been deprecated or rebranded, update skills to guide users toward the current package while maintaining backward compatibility:

1. Add a migration notice at the top of the skill:

> **⚠️ MIGRATION NOTICE**: The [Old Service Name] has been rebranded to **[New Service Name]**. While the package `old-package-name` remains available for compatibility, **new projects should use `new-package-name`** which provides the latest features and updates.
>
> **For new projects**: Use the `new-package-name` package instead.
>
> **This skill remains valid** for existing projects using `old-package-name`, but be aware you're using the legacy package name. The API patterns shown here are compatible with both packages.

2. Show both installation options:

## Installation

### Legacy Package (Old Name)

\`\`\`xml
<dependency>
<groupId>com.azure</groupId>
<artifactId>azure-old-package</artifactId>
<version>4.2.0</version>
</dependency>
\`\`\`

### Recommended Package (New Name)

**For new projects, use the rebranded package:**

\`\`\`xml
<dependency>
<groupId>com.azure</groupId>
<artifactId>azure-new-package</artifactId>
<version>1.0.0</version>
</dependency>
\`\`\`

> **Note**: The API patterns in this skill apply to both packages. Replace package names and imports as needed when using `azure-new-package`.

3. When to create a new skill vs. update existing:

• Update existing skill if the API is largely compatible (same or similar class/method names)
• Create new skill + migration guide if the API changed significantly (use references/migration.md)
• Always cross-reference between old and new skills

Examples:

• azure-ai-formrecognizer-java → azure-ai-documentintelligence (rebranded service)
• azure-communication-callingserver-java → azure-communication-callautomation (deprecated, with migration guide)

Example: Azure SDK Skill Structure

---
name: skill-creator
description: |
  Azure AI Example SDK for Python. Use for [specific service features].
  Triggers: "example service", "create example", "list examples".
---

# Azure AI Example SDK

## Installation

\`\`\`bash
pip install azure-ai-example
\`\`\`

## Environment Variables

\`\`\`bash
AZURE_EXAMPLE_ENDPOINT=https://<resource>.example.azure.com
AZURE_TOKEN_CREDENTIALS=prod # Required only if DefaultAzureCredential is used in production
\`\`\`

## Authentication & Lifecycle

> **🔑 Two rules apply to every code sample below:**
>
> 1. **Prefer `DefaultAzureCredential`.** It works locally (Azure CLI / VS Code / Developer CLI) and in Azure (managed identity, workload identity) with no code change. Avoid connection strings, account/API keys — they bypass Entra audit and rotation.
>    - Local dev: `DefaultAzureCredential` works as-is.
>    - Production: set `AZURE_TOKEN_CREDENTIALS=prod` (or `AZURE_TOKEN_CREDENTIALS=<specific_credential>`) to constrain the credential chain to production-safe credentials.
> 2. **Wrap every client in a context manager** so HTTP transports, sockets, and token caches are released deterministically:
>    - Sync: `with <Client>(...) as client:`
>    - Async: `async with <Client>(...) as client:` **and** `async with DefaultAzureCredential() as credential:` (from `azure.identity.aio`)
>
> Snippets may abbreviate this setup, but production code should always follow both rules.

\`\`\`python
from azure.identity import DefaultAzureCredential, ManagedIdentityCredential
from azure.ai.example import ExampleClient

# Local dev: DefaultAzureCredential. Production: set AZURE_TOKEN_CREDENTIALS=prod or AZURE_TOKEN_CREDENTIALS=<specific_credential>

credential = DefaultAzureCredential(require_envvar=True)

# Or use a specific credential directly in production:

# See https://learn.microsoft.com/python/api/overview/azure/identity-readme?view=azure-python#credential-classes

# credential = ManagedIdentityCredential()
with ExampleClient(
    endpoint=os.environ["AZURE_EXAMPLE_ENDPOINT"],
    credential=credential,
) as client:
    item = client.get_item("example")
\`\`\`

## Core Workflow

\`\`\`python
with ExampleClient(endpoint=endpoint, credential=credential) as client:
    # Create
    item = client.create_item(name="example", data={...})

    # List (pagination handled automatically)
    for item in client.list_items():
        print(item.name)

    # Long-running operation
    poller = client.begin_process(item.id)
    result = poller.result()

    # Cleanup
    client.delete_item(item.id)
\`\`\`

## Reference Files

| File                                               | Contents                 |
| -------------------------------------------------- | ------------------------ |
| [references/tools.md](references/tools.md)         | Tool integrations        |
| [references/streaming.md](references/streaming.md) | Event streaming patterns |

---

Skill Creation Process

1. Gather SDK Context — User provides SDK/API reference (REQUIRED)
2. Understand — Research SDK patterns from official docs
3. Plan — Identify reusable resources and product area category
4. Create — Write SKILL.md in .github/skills/<skill-name>/
5. Categorize — Create symlink in skills/<language>/<category>/
6. Test — Create acceptance criteria and test scenarios
7. Document — Update README.md skill catalog
8. Iterate — Refine based on real usage

Step 1: Gather SDK Context (REQUIRED)

Before creating any SDK skill, the user MUST provide:

Required	Example	Purpose
SDK Package	azure-ai-agents, Azure.AI.OpenAI	Identifies the exact SDK
Documentation URL	https://learn.microsoft.com/en-us/azure/ai-services/...	Primary source of truth
Repository (optional)	Azure/azure-sdk-for-python	For code patterns

Prompt the user if not provided:

To create this skill, I need:
1. The SDK package name (e.g., azure-ai-projects)
2. The Microsoft Learn documentation URL or GitHub repo
3. The target language (py/dotnet/ts/java)

Search official docs first:

# Use microsoft-docs MCP to get current API patterns
# Query: "[SDK name] [operation] [language]"
# Verify: Parameters match the latest SDK version

Step 2: Understand the Skill

Gather concrete examples:

• "What SDK operations should this skill cover?"
• "What triggers should activate this skill?"
• "What errors do developers commonly encounter?"

Example Task	Reusable Resource
Same auth code each time	Code example in SKILL.md
Complex streaming patterns	references/streaming.md
Tool configurations	references/tools.md
Error handling patterns	references/error-handling.md

Step 3: Plan Product Area Category

Skills are organized by language and product area in the skills/ directory via symlinks.

Product Area Categories:

Category	Description	Examples
foundry	AI Foundry, agents, projects, inference	azure-ai-agents-py, azure-ai-projects-py
data	Storage, Cosmos DB, Tables, Data Lake	azure-cosmos-py, azure-storage-blob-py
messaging	Event Hubs, Service Bus, Event Grid	azure-eventhub-py, azure-servicebus-py
monitoring	OpenTelemetry, App Insights, Query	azure-monitor-opentelemetry-py
identity	Authentication, DefaultAzureCredential	azure-identity-py
security	Key Vault, secrets, keys, certificates	azure-keyvault-py
integration	API Management, App Configuration	azure-appconfiguration-py
compute	Batch, ML compute	azure-compute-batch-java
container	Container Registry, ACR	azure-containerregistry-py

Determine the category based on:

1. Azure service family (Storage → data, Event Hubs → messaging)
2. Primary use case (AI agents → foundry)
3. Existing skills in the same service area

Step 4: Create the Skill

Location: .github/skills/<skill-name>/SKILL.md

Naming convention:

• azure-<service>-<subservice>-<language>
• Examples: azure-ai-agents-py, azure-cosmos-java, azure-storage-blob-ts

For Azure SDK skills:

1. Search microsoft-docs MCP for current API patterns
2. Verify against installed SDK version
3. Follow the section order above
4. Include cleanup code in examples
5. Add feature comparison tables

Write bundled resources first, then SKILL.md.

Frontmatter:

---
name: skill-name-py
description: |
  Azure Service SDK for Python. Use for [specific features].
  Triggers: "service name", "create resource", "specific operation".
---

Step 5: Categorize with Symlinks

After creating the skill in .github/skills/, create a symlink in the appropriate category:

# Pattern: skills/<language>/<category>/<short-name> -> ../../../.github/skills/<full-skill-name>

# Example for azure-ai-agents-py in python/foundry:
cd skills/python/foundry
ln -s ../../../.github/skills/azure-ai-agents-py agents

# Example for azure-cosmos-db-py in python/data:
cd skills/python/data
ln -s ../../../.github/skills/azure-cosmos-db-py cosmos-db

Symlink naming:

• Use short, descriptive names (e.g., agents, cosmos, blob)
• Remove the azure- prefix and language suffix
• Match existing patterns in the category

Verify the symlink:

ls -la skills/python/foundry/agents
# Should show: agents -> ../../../.github/skills/azure-ai-agents-py

Step 6: Create Tests

Every skill MUST have acceptance criteria and test scenarios.

6.1 Create Acceptance Criteria

Location: tests/scenarios/<skill-name>/acceptance-criteria.md

Source materials (in priority order):

1. Official Microsoft Learn docs (via microsoft-docs MCP)
2. SDK source code from the repository
3. Existing reference files in the skill

Format:

# Acceptance Criteria: <skill-name>

**SDK**: `package-name`
**Repository**: https://github.com/Azure/azure-sdk-for-<language>
**Purpose**: Skill testing acceptance criteria

---

## 1. Correct Import Patterns

### 1.1 Client Imports

#### ✅ CORRECT: Main Client

\`\`\`python
from azure.ai.mymodule import MyClient
from azure.identity import DefaultAzureCredential
\`\`\`

#### ❌ INCORRECT: Wrong Module Path

\`\`\`python
from azure.ai.mymodule.models import MyClient # Wrong - Client is not in models
\`\`\`

## 2. Authentication Patterns

#### ✅ CORRECT: DefaultAzureCredential + context manager

\`\`\`python
credential = DefaultAzureCredential()
with MyClient(endpoint, credential) as client:
    client.do_thing()
\`\`\`

#### ❌ INCORRECT: Hardcoded Credentials

\`\`\`python
client = MyClient(endpoint, api_key="hardcoded")  # Security risk
\`\`\`

#### ❌ INCORRECT: Connection string / account key when Entra is supported

\`\`\`python
client = MyClient.from_connection_string(os.environ["CONNECTION_STRING"])  # Bypasses Entra audit/rotation
\`\`\`

#### ❌ INCORRECT: Bare client without context manager

\`\`\`python
client = MyClient(endpoint, credential)  # Leaks HTTP transport on exception / interpreter exit
client.do_thing()
\`\`\`

Critical patterns to document:

• Import paths (these vary significantly between Azure SDKs)
• Authentication patterns
• Client initialization
• Async variants (.aio modules)
• Common anti-patterns

6.2 Create Test Scenarios

Location: tests/scenarios/<skill-name>/scenarios.yaml

config:
  model: gpt-4
  max_tokens: 2000
  temperature: 0.3

scenarios:
  - name: basic_client_creation
    prompt: |
      Create a basic example using the Azure SDK.
      Include proper authentication and client initialization.
    expected_patterns:
      - "DefaultAzureCredential"
      - "MyClient"
      - "with MyClient"  # enforce context manager
    forbidden_patterns:
      - "api_key="
      - "hardcoded"
      - "from_connection_string"  # prefer Entra over connection strings
    tags:
      - basic
      - authentication
    mock_response: |
      import os
      from azure.identity import DefaultAzureCredential
      from azure.ai.mymodule import MyClient

      credential = DefaultAzureCredential()
      with MyClient(
          endpoint=os.environ["AZURE_ENDPOINT"],
          credential=credential,
      ) as client:
          # ... rest of working example
          pass

Scenario design principles:

• Each scenario tests ONE specific pattern or feature
• expected_patterns — patterns that MUST appear
• forbidden_patterns — common mistakes that must NOT appear
• mock_response — complete, working code that passes all checks
• tags — for filtering (basic, async, streaming, tools)

6.3 Run Tests

cd tests
pnpm install

# Check skill is discovered
pnpm harness --list

# Run in mock mode (fast, deterministic)
pnpm harness <skill-name> --mock --verbose

# Run with Ralph Loop (iterative improvement)
pnpm harness <skill-name> --ralph --mock --max-iterations 5 --threshold 85

Success criteria:

• All scenarios pass (100% pass rate)
• No false positives (mock responses always pass)
• Patterns catch real mistakes

Step 7: Update Documentation

After creating the skill:

1. Update README.md — Add the skill to the appropriate language section in the Skill Catalog

   • Update total skill count (line ~73: > N skills in...)
   • Update Skill Explorer link count (line ~15: Browse all N skills)
   • Update language count table (lines ~77-83)
   • Update language section count (e.g., > N skills • suffix: -py)
   • Update category count (e.g., <summary><strong>Foundry & AI</strong> (N skills)</summary>)
   • Add skill row in alphabetical order within its category
   • Update test coverage summary (line ~622: **N skills with N test scenarios**)
   • Update test coverage table — update skill count, scenario count, and top skills for the language

2. Regenerate GitHub Pages data — Run the extraction script to update the docs site

   cd docs-site && npx tsx scripts/extract-skills.ts
   

   This updates docs-site/src/data/skills.json which feeds the Astro-based docs site. Then rebuild the docs site:

   cd docs-site && npm run build
   

   This outputs to docs/ which is served by GitHub Pages.

3. Verify AGENTS.md — Ensure the skill count is accurate

---

Progressive Disclosure Patterns

Pattern 1: High-Level Guide with References

# SDK Name

## Quick Start

[Minimal example]

## Advanced Features

- **Streaming**: See [references/streaming.md](references/streaming.md)
- **Tools**: See [references/tools.md](references/tools.md)

Pattern 2: Language Variants

azure-service-skill/
├── SKILL.md (overview + language selection)
└── references/
    ├── python.md
    ├── dotnet.md
    ├── java.md
    └── typescript.md

Pattern 3: Feature Organization

azure-ai-agents/
├── SKILL.md (core workflow)
└── references/
    ├── tools.md
    ├── streaming.md
    ├── async-patterns.md
    └── error-handling.md

---

Design Pattern References

Reference	Contents
references/workflows.md	Sequential and conditional workflows
references/output-patterns.md	Templates and examples
references/azure-sdk-patterns.md	Language-specific Azure SDK patterns

---

Anti-Patterns

Don't	Why
Create skill without SDK context	Users must provide package name/docs URL
Put "when to use" in body	Body loads AFTER triggering
Hardcode credentials	Security risk
Skip authentication section	Agents will improvise poorly
Use outdated SDK patterns	APIs change; search docs first
Include README.md	Agents don't need meta-docs
Deeply nest references	Keep one level deep
Skip acceptance criteria	Skills without tests can't be validated
Skip symlink categorization	Skills won't be discoverable by category
Use wrong import paths	Azure SDKs have specific module structures
Omit sync/async + context-manager bullets from Best Practices in Python skills	End users won't follow rules that aren't written down; examples alone aren't enough
Mix sync and async in the same Python example	Demonstrates the anti-pattern the skill is supposed to prevent

---

Checklist

Before completing a skill:

Prerequisites:

• User provided SDK package name or documentation URL
• Verified SDK patterns via microsoft-docs MCP

Skill Creation:

• Description includes what AND when (trigger phrases)
• SKILL.md under 500 lines
• Authentication guidance matches language-specific recommendations (including Rust credential guidance)
• Includes cleanup/delete in examples
• References organized by feature
• (Python skills only) Best Practices section contains the two user-facing rules (sync-or-async consistency + context managers for clients and async credentials), using the variant matched to the skill type
• (Python skills only) Every code example obeys both rules (no mixed sync/async; every client wrapped in with / async with)

Categorization:

• Skill created in .github/skills/<skill-name>/
• Symlink created in skills/<language>/<category>/<short-name>
• Symlink points to ../../../.github/skills/<skill-name>

Testing:

• tests/scenarios/<skill-name>/acceptance-criteria.md created with correct/incorrect patterns
• tests/scenarios/<skill-name>/scenarios.yaml created
• All scenarios pass (pnpm harness <skill> --mock)
• Import paths documented precisely

Documentation:

• README.md skill catalog updated
• Instructs to search microsoft-docs MCP for current APIs