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:

## 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:

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)

Creating Azure SDK Skills

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

Token Budget Guidelines (REQUIRED)

Every Azure SDK skill MUST stay within these token limits:

Enforcement:

• Exceeding max limit → refactor into /references/ subdirectories
• When approaching 500 lines → move entire sections to reference files
• Annotate with <!-- Token Count: ~XXXX (target: 1100, max: 1500) --> immediately below the skill's H1

Reference Extraction Guide (REQUIRED)

Decide what goes in SKILL.md vs. /references/ using these signals:

Content extraction rules:

• Batch operations → /references/batch-operations.md
• Error handling (beyond try-except) → /references/error-handling.md
• Performance tuning → /references/performance.md
• Alternative workflows → /references/workflows-comparison.md
• Streaming/events → /references/streaming.md
• Advanced auth → /references/auth-strategies.md
• Tool integration → /references/tools.md
• Breaking changes → /references/migration.md

Decision: Keep common case in SKILL.md, move edge cases to /references/.

Core Workflow Discipline (REQUIRED)

Every Azure SDK skill must clarify which workflow(s) it documents.

Case 1: Single clear "core workflow" (majority of services)

If one pattern handles ~80% of use cases:

1. Designate it as the core workflow
2. Show ONLY this workflow in SKILL.md (one complete, runnable example)
3. Defer alternatives to /references/:
   • Batch operations → /references/batch-operations.md
   • Error handling → /references/error-handling.md
   • Performance tuning → /references/performance.md
   • Alternative workflows → /references/workflows-comparison.md

Example: Azure Key Vault Secrets (core workflow: retrieve a secret using managed identity). Alternative authentication workflows in /references/: local development with DefaultAzureCredential, workload identity, and service-principal credentials (client secret or certificate).

Case 2: Multiple equally-valid "core workflows" (e.g., authentication strategies, deployment targets)

If no single pattern dominates:

1. Include every hero scenario in SKILL.md, even when that means multiple equally valid workflows
2. Show one complete, runnable example for each hero scenario in SKILL.md
3. Use /references/workflows-comparison.md for trade-offs, secondary variations, and deeper context that would otherwise bloat the main file
4. Do NOT treat valid alternatives as "advanced" when they are core to real usage — they're equally valid, just different contexts

Example: Azure Identity SDK has several hero scenarios. Keep the primary local-development and production-safe credential flows in SKILL.md, then use /references/credential-types.md for deeper comparisons across AzureCliCredential, workload identity, service principal variants, and other secondary credential choices.

Decision rule: If you're unsure, ask: "Would a user choosing the other approach call what I wrote wrong?" If yes, it's another hero scenario and belongs in SKILL.md. If no, it can be summarized and linked from /references/.

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 — For Python skills, prefer DefaultAzureCredential: use it as-is for local development, and constrain it for production by setting AZURE_TOKEN_CREDENTIALS to prod (or a specific target credential name). A specific Microsoft Entra Token credential such as ManagedIdentityCredential or WorkloadIdentityCredential may be used directly instead. 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 (per core workflow discipline above)
6. Feature Tables — Clients, methods, tools
7. Best Practices — Numbered list
8. Reference Links — Table linking to /references/*.md (for Azure SDK skills, include capabilities.md + non-hero-scenarios.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` for local development.** It works as-is with Azure CLI / VS Code / Developer CLI. For production, either constrain `DefaultAzureCredential` to production-safe credentials or use a specific credential directly. 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 works as-is.
credential = DefaultAzureCredential()
# Production alternative: constrain DefaultAzureCredential with AZURE_TOKEN_CREDENTIALS.
# 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);

// Go
import (
  "context"

  "github.com/Azure/azure-sdk-for-go/sdk/azidentity"
  "github.com/Azure/azure-sdk-for-go/sdk/storage/azblob"
)

ctx := context.Background()

// Local dev: DefaultAzureCredential. Production: set AZURE_TOKEN_CREDENTIALS=prod or AZURE_TOKEN_CREDENTIALS=<specific_credential>
cred, err := azidentity.NewDefaultAzureCredential(nil)
if err != nil {
  panic(err)
}

// Or use a specific credential directly in production:
// cred, err := azidentity.NewManagedIdentityCredential(nil)

client, err := azblob.NewClient("https://<account>.blob.core.windows.net/", cred, nil)
if err != nil {
  panic(err)
}

_ = client
_ = ctx

// 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.

Anti-Patterns: What NOT to Do (REQUIRED Reading)

These patterns cause bloat and inefficiency. Every skill author must review this section before writing.

Anti-Pattern 1: "Exhaustive API Reference"

• ❌ Don't: List all 50 SDK methods in a feature table with code samples for every variant
• ✅ Do: Show 3-5 core methods in a table; link to official Azure API reference for exhaustive list
• Token cost: Listing all methods + examples = 400-600 tokens wasted
• User impact: Overwhelming cognitive load; users don't know what to use

Anti-Pattern 2: "Multiple Ways to Solve One Problem"

• ❌ Don't: "Here's approach A, B, C, and D to paginate results" in the main body
• ✅ Do: "Use ItemPaged for sync pagination" (primary example); link alternatives to /references/
• Token cost: Each alternate approach = 50-100 tokens; 5 approaches = skill becomes inefficient
• User impact: Decision paralysis; users re-read everything

Anti-Pattern 3: "Beginner + Intermediate + Advanced in One Skill"

• ❌ Don't: Skill that goes from "what is a client?" to "custom retry policies" to "circuit breaker patterns"
• ✅ Do: Core workflow covers 80% use case; advanced patterns in /references/
• Token cost: Every skill level adds 200-300 tokens; three levels = 600-900 extra tokens
• User impact: Experts bored, beginners overwhelmed; nobody gets what they need

Anti-Pattern 4: "Restating Official Documentation"

• ❌ Don't: "The CosmosClient constructor takes an endpoint (string) and credential (TokenCredential). The endpoint identifies the Azure Cosmos resource..."
• ✅ Do: Show code: client = CosmosClient(endpoint, credential). Link to official docs: microsoft-docs MCP.
• Token cost: Verbose explanation = 50-100 tokens per parameter; large APIs waste 300+ tokens
• User impact: Redundant; official docs are authoritative, skill should show usage not repeat them

Anti-Pattern 5: "Verbose Explanation When Example Suffices"

• ❌ Don't: "To create a client, you first instantiate the class using the constructor, passing the endpoint and credential parameters. The endpoint is a string that identifies your resource..."
• ✅ Do: Show code immediately: with CosmosClient(endpoint, credential) as client:

Efficiency Validation (REQUIRED - Phase 2)

During authoring, validate skill efficiency manually, then run the Vally eval if the skill has one under tests/scenarios/<skill-name>/vally/.

1. Measure token count:

Use a token counter or model playground to measure each section. Compare to the Token Budget Guidelines targets above. If any section exceeds max, move content to /references/.

2. Run anti-pattern checklist:

• No exhaustive API reference (show 3-5 core methods, not 50)
• No multiple solutions to one problem in SKILL.md
• No beginner+intermediate+advanced mixed
• No restating official docs (code first, link to microsoft-docs)
• No verbose prose (examples first, minimal text)

3. Example count audit:

• 1 complete example per hero scenario / core workflow documented in SKILL.md. For Python SDKs that support both sync and async, the paired sync + async examples for the same workflow count as one workflow, not two.
• Feature table includes 3-5 core methods (not comprehensive API)
• Max 1 example per best practice bullet

4. Frontmatter validation:

• name matches .github/skills/<name>/SKILL.md
• description includes trigger keywords
• description is concise (~200 chars is a good target; schema max is 1,024 chars)
• If included, optional benchmark_tokens_* and benchmark_quality_* metadata fields are flat strings under metadata

4b. Authentication guidance validation (critical for all credentials):

• If skill uses Azure Identity credentials, verify guidance against the current official credential docs for that language/package (Microsoft Learn where available; otherwise the upstream SDK repo or package docs)
• For Python skills, development guidance may recommend DefaultAzureCredential (supports multiple dev credential types)
• For Python skills, production guidance: DefaultAzureCredential alone (unconstrained) is not sufficient; require either AZURE_TOKEN_CREDENTIALS=prod (or a specific target credential) to constrain the chain, or a specific credential (e.g., ManagedIdentityCredential) used directly
• For Rust skills, development/production guidance reflects the actual supported credentials (DeveloperToolsCredential for local dev; a specific production credential such as ManagedIdentityCredential for production)
• Link to /references/auth-strategies.md or official docs for production credential selection

4c. Run Vally lint/eval (if the skill has a spec under tests/scenarios/<skill-name>/vally/):

# If the eval spec uses the shared Rust custom grader plugin, build it first.
(cd tests/scenarios/_shared/vally/grader-plugins/rust-cargo-build-failure && npm install && npm run build)

vally lint --eval-spec tests/scenarios/<skill-name>/vally/eval.yaml \
  --grader-plugin tests/scenarios/_shared/vally/grader-plugins/rust-cargo-build-failure \
  --strict

vally eval --eval-spec tests/scenarios/<skill-name>/vally/eval.yaml \
  --grader-plugin tests/scenarios/_shared/vally/grader-plugins/rust-cargo-build-failure

• vally lint passes with no errors
• vally eval passes (no error-severity findings) when COPILOT_TOKEN is available; otherwise lint-only is acceptable, matching the Vally Evaluation workflow behavior
• Skills without a vally/ spec skip this step — it is optional per skill, not required for every skill

5. Spot check:

• Can a user copy the core workflow and run it immediately?
• Do all examples follow best practices (context managers, appropriate credentials)?
• Are all environment variables documented?

Output: After validation, annotate the skill header with measured token count:

# Azure Service SDK

<!-- Token Count: ~1180 (target: 1100, max: 1500) -->

Standard Verb Patterns

Azure SDKs use consistent verbs across all languages:

Language-Specific Patterns

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

• Python: ItemPaged, LROPoller, context managers, Sphinx docstrings. When the SDK provides both sync and async clients, present both forms as first-class options; do not express a preference for either. When the SDK is sync-only or async-only, document the available mode only. Do not mix sync and async within a single code example. Always show with / async with context managers.
• .NET: Response<T>, Pageable<T>, Operation<T>, mocking support
• Java: Builder pattern, PagedIterable/PagedFlux, Reactor types
• TypeScript: PagedAsyncIterableIterator, AbortSignal, browser considerations
• Go: context.Context as first arg, runtime.Pager[T] via New*Pager() + More()/NextPage(ctx), runtime.Poller[T] via Begin* + PollUntilDone(ctx, nil), to.Ptr(...) helpers, and typed *azcore.ResponseError
• Rust: Installation via cargo add, dependency rule for azure_core, 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, TypeScript, and Go 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. **Do not mix sync and async clients in the same call path.** Use either `azure.xxx` sync clients or `azure.xxx.aio` async clients within a single call path — do not combine both.
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. For code that runs in Azure, either constrain `DefaultAzureCredential` with `AZURE_TOKEN_CREDENTIALS=prod` (or a specific target credential) or use a specific token credential directly (e.g. `ManagedIdentityCredential`, `WorkloadIdentityCredential`).

Variants to apply when the SDK shape differs:

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

• Do not interleave sync and async calls within a single example. When the SDK provides both sync and async clients, show each mode in its own complete, self-contained example — a ### Sync subsection and an ### Async subsection — giving both equal prominence. When the SDK is sync-only or async-only, show only the available mode.
• 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

These rules MUST be explicitly written into every Rust skill's ## Best Practices section as the first items:

1. Use cargo add to manage dependencies, never edit Cargo.toml directly. Always use cargo add <crate> or cargo remove <crate> instead of manually modifying the manifest file. Official crates are published on crates.io and should be added via cargo.

2. Add azure_core to Cargo.toml only when you import azure_core types directly. If your code imports types like azure_core::http::Url, azure_core::http::RequestContent, or azure_core::error::ErrorKind, explicitly add azure_core to your dependencies. If you only use types re-exported by service crates (e.g., via use azure_storage_blob::BlobClient), a direct azure_core dependency is optional.

3. 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.

4. 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.

5. 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).

6. 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.

Example Effective Skills (Benchmark Only Structure-Compliant Skills)

Only benchmark Azure SDK skills that already use the required references/ layout (references/capabilities.md plus references/non-hero-scenarios.md). Older skills that predate that structure can still be useful for style ideas, but do not mirror them directly until they are brought into compliance.

A valid benchmark skill should:

1. Stay at or under the 1,500-token absolute max (see Token Budget Guidelines above)
2. Cover the hero workflow (CRUD or primary operations), not every feature variant
3. Show 1-2 examples per concept, not 3-5
4. Use tables for API summary (credential types, RBAC roles, client hierarchy)
5. Link to official docs via microsoft-docs MCP instead of duplicating
6. Move advanced patterns to /references/
7. Include references/capabilities.md and references/non-hero-scenarios.md

Before writing your skill: Apply the checklist above directly, then mirror only the structure patterns that fit your use case.

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` for local development.** It works as-is with Azure CLI / VS Code / Developer CLI. For production, either constrain `DefaultAzureCredential` to production-safe credentials or use a specific credential directly. 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 works as-is.
credential = DefaultAzureCredential()

# Production alternative: constrain DefaultAzureCredential with AZURE_TOKEN_CREDENTIALS.
# 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/capabilities.md](references/capabilities.md)             | Capability index (hero coverage + links to deep-dives) |
| [references/non-hero-scenarios.md](references/non-hero-scenarios.md) | Concrete non-hero examples                             |
| [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

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