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:

---

Core Principles

1. Concise is Key

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

Default assumption: Agents are already capable. Only add what they don't already know.

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 task fragility:

| 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:

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

---

Skill Structure

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)

SKILL.md

Bundled Resources

| Type | Purpose | When to Include |

|------|---------|-----------------|

| scripts/ | Deterministic operations | Same code rewritten repeatedly |

| references/ | Detailed patterns | API docs, schemas, detailed guides |

| assets/ | Output resources | Templates, images, boilerplate |

Don't include: README.md, CHANGELOG.md, installation guides.

---

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):

Authentication Pattern (All Languages)

Always use DefaultAzureCredential:

# Python
from azure.identity import DefaultAzureCredential
credential = DefaultAzureCredential()
client = ServiceClient(endpoint, credential)

// C#
var credential = new DefaultAzureCredential();
var client = new ServiceClient(new Uri(endpoint), credential);

// Java
TokenCredential credential = new DefaultAzureCredentialBuilder().build();
ServiceClient client = new ServiceClientBuilder()
    .endpoint(endpoint)
    .credential(credential)
    .buildClient();

// TypeScript
import { DefaultAzureCredential } from "@azure/identity";
const credential = new DefaultAzureCredential();
const client = new ServiceClient(endpoint, credential);

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:

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
\`\`\`

## Authentication

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

credential = DefaultAzureCredential()
client = ExampleClient(
    endpoint=os.environ["AZURE_EXAMPLE_ENDPOINT"],
    credential=credential
)
\`\`\`

## Core Workflow

\`\`\`python
# 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

Step 1: Gather SDK Context (REQUIRED)

Before creating any SDK skill, the user MUST provide:

| Required | Example | Purpose |

|----------|---------|---------|

| SDK Package | azure-ai-agents, Azure.AI.OpenAI | Identifies the exact SDK |

| Documentation URL | https://learn.microsoft.com/en-us/azure/ai-services/... | Primary source of truth |

| Repository (optional) | Azure/azure-sdk-for-python | For code patterns |

Prompt the user if not provided:

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

Search official docs first:

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

Step 2: Understand the Skill

Gather concrete examples:

| 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:

Step 4: Create the Skill

Location: .github/skills//SKILL.md

Naming convention:

For Azure SDK skills:

Write bundled resources first, then SKILL.md.

Frontmatter:

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

Step 5: Categorize with Symlinks

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

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

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

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

Symlink naming:

Verify the symlink:

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

Step 6: Create Tests

Every skill MUST have acceptance criteria and test scenarios.

#### 6.1 Create Acceptance Criteria

Location: .github/skills//references/acceptance-criteria.md

Source materials (in priority order):

Format:

# Acceptance Criteria: <skill-name>

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

---

## 1. Correct Import Patterns

### 1.1 Client Imports

#### ✅ CORRECT: Main Client
\`\`\`python
from azure.ai.mymodule import MyClient
from azure.identity import DefaultAzureCredential
\`\`\`

#### ❌ INCORRECT: Wrong Module Path
\`\`\`python
from azure.ai.mymodule.models import MyClient  # Wrong - Client is not in models
\`\`\`

## 2. Authentication Patterns

#### ✅ CORRECT: DefaultAzureCredential
\`\`\`python
credential = DefaultAzureCredential()
client = MyClient(endpoint, credential)
\`\`\`

#### ❌ INCORRECT: Hardcoded Credentials
\`\`\`python
client = MyClient(endpoint, api_key="hardcoded")  # Security risk
\`\`\`

Critical patterns to document:

#### 6.2 Create Test Scenarios

Location: tests/scenarios//scenarios.yaml

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

scenarios:
  - name: basic_client_creation
    prompt: |
      Create a basic example using the Azure SDK.
      Include proper authentication and client initialization.
    expected_patterns:
      - "DefaultAzureCredential"
      - "MyClient"
    forbidden_patterns:
      - "api_key="
      - "hardcoded"
    tags:
      - basic
      - authentication
    mock_response: |
      import os
      from azure.identity import DefaultAzureCredential
      from azure.ai.mymodule import MyClient
      
      credential = DefaultAzureCredential()
      client = MyClient(
          endpoint=os.environ["AZURE_ENDPOINT"],
          credential=credential
      )
      # ... rest of working example

Scenario design principles:

#### 6.3 Run Tests

cd tests
pnpm install

# Check skill is discovered
pnpm harness --list

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

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

Success criteria:

Step 7: Update Documentation

After creating the skill:

```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.

---

Progressive Disclosure Patterns

Pattern 1: High-Level Guide with References

# SDK Name

## Quick Start
[Minimal example]

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

Pattern 2: Language Variants

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

Pattern 3: Feature Organization

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

---

Design Pattern References

| Reference | Contents |

|-----------|----------|

| references/workflows.md | Sequential and conditional workflows |

| references/output-patterns.md | Templates and examples |

| references/azure-sdk-patterns.md | Language-specific Azure SDK patterns |

---

Anti-Patterns

| Don't | Why |

|-------|-----|

| Create skill without SDK context | Users must provide package name/docs URL |

| Put "when to use" in body | Body loads AFTER triggering |

| Hardcode credentials | Security risk |

| Skip authentication section | Agents will improvise poorly |

| Use outdated SDK patterns | APIs change; search docs first |

| Include README.md | Agents don't need meta-docs |

| Deeply nest references | Keep one level deep |

| Skip acceptance criteria | Skills without tests can't be validated |

| Skip symlink categorization | Skills won't be discoverable by category |

| Use wrong import paths | Azure SDKs have specific module structures |

---

Checklist

Before completing a skill:

Prerequisites:

Skill Creation:

Categorization:

Testing:

Documentation: