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

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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.

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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/	Feature deep-dives and overflow examples	capabilities.md index, non-hero-scenarios.md, API docs
assets/	Output templates	Boilerplate code, images

---

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:

Section	Target	Absolute Max
Installation + Env Vars	100 tokens	150
Authentication & Lifecycle	200 tokens	300
Core Workflow (1 example)	300 tokens	400
Feature Tables	200 tokens	300
Best Practices (6-8 items)	200 tokens	250
References (reference/ links)	100 tokens	150
Total SKILL.md	~1100 tokens	~1500 tokens

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

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Reference Extraction Guide (REQUIRED)

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

Signal	Move to /references/	Keep in SKILL.md
Use frequency	<20% of typical use	~80%+ of workflows
Cognitive load	Advanced patterns, multiple options	Single happy path
Example length	>10 lines, multiple paths	1-5 lines, single path

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/.

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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:

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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:

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: