# Skill Creator

> 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: `zuoguyoupan2023/skill-creator` (Agent Skill, multi-file: 7 files)
- Install (CLI): `npx skillmds@latest add zuoguyoupan2023/skill-creator`
- Raw SKILL.md: https://api.skillmd.com/api/skills/zuoguyoupan2023/skill-creator/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: DevOps & Infra
- Author: zuoguyoupan2023 (https://skillmd.com/u/zuoguyoupan2023)
- Updated: 2026-09-22
- Page: https://skillmd.com/skills/zuoguyoupan2023/skill-creator

---


# 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:

```markdown
## 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 `DefaultAzureCredential`in 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)](#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.

```markdown
## 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
# 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()
```

```csharp
// 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
// 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
// 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
// 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):**

```markdown
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](https://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:**

```markdown
> **⚠️ 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:**

```markdown
## 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

```markdown
---
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:**

```bash
# 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:**

```yaml
---
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:

```bash
# 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:**

```bash
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:**

```markdown
# 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`

```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

```bash
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

   ```bash
   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:

   ```bash
   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

```markdown
# 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