skill-creator

Leitfaden zum Erstellen effektiver GitHub-Copilot-Skills (.github/skills/) in diesem Repository. Verwenden Sie ihn beim Erstellen eines neuen Skills, beim Aktualisieren eines bestehenden Skills oder wenn…

npx skills add https://github.com/microsoft/vscode-documentdb --skill skill-creator

Skill Creator for GitHub Copilot

Create and maintain skills in .github/skills/ that extend GitHub Copilot's capabilities with project-specific knowledge.

What Are Skills

Skills are SKILL.md files that provide specialized, procedural knowledge that Copilot doesn't inherently have. They turn Copilot from a general assistant into a domain expert for specific tasks in this codebase.

Skills provide:

  • Specialized workflows — multi-step procedures for project-specific domains
  • Domain expertise — architecture patterns, conventions, business logic
  • Bundled references — detailed docs loaded only when needed

Skill Anatomy

.github/skills/
└── skill-name/
    ├── SKILL.md              (required — frontmatter + instructions)
    └── references/           (optional — detailed docs, loaded on demand)

SKILL.md Structure

---
name: my-skill-name
description: What this skill does and WHEN to use it. Include trigger words and scenarios. This is the primary mechanism for Copilot to decide whether to load the skill body.
---

# Skill Title

Concise instructions for using this skill.

## When to Use

- Bullet list of triggering scenarios

## Core Content

(workflow, patterns, examples)

Wiring the Skill

After creating the SKILL.md, register it in .github/copilot-instructions.md under the <skills> section:

<skill>
<name>my-skill-name</name>
<description>Same or similar description as in SKILL.md frontmatter</description>
<file>${workspaceFolder}\.github\skills\my-skill-name\SKILL.md</file>
</skill>

Note: Replace ${workspaceFolder} with the absolute path to the repository root on your machine (e.g. \home\user\repos\vscode-documentdb). VS Code resolves skill file paths at runtime and currently requires absolute paths.

Copilot reads skill metadata (name + description) to decide when to trigger. The SKILL.md body is loaded only after triggering.

Core Principles

1. Concise is Key

The context window is shared with conversation history, other instructions, and user requests. Challenge each paragraph: "Does Copilot already know this?" and "Does this justify its token cost?"

  • Prefer concise examples over verbose explanations
  • Only include what Copilot doesn't already know — skip general TypeScript/React knowledge
  • Target under 300 lines for SKILL.md body; split into references if larger

2. Progressive Disclosure

Use a three-level loading system:

  1. Metadata (name + description) — always visible (~50 words)
  2. SKILL.md body — loaded when skill triggers
  3. References — loaded on demand by Copilot when deeper detail is needed

For skills with multiple variants or extensive reference material, keep the core workflow in SKILL.md and move details to references/:

## Advanced Topics

- **Detailed format spec**: See [FORMAT.md](./FORMAT.md)
- **Migration patterns**: See [references/migration.md](./references/migration.md)

3. Match Freedom to Task Fragility

Freedom LevelWhenExample
High (text guidance)Multiple valid approachesArchitecture recommendations
Medium (patterns with examples)Preferred pattern existstRPC router creation
Low (exact steps)Fragile, error-proneRelease note formatting

4. Do NOT Include

  • README.md, CHANGELOG.md, or auxiliary documentation
  • Setup/installation instructions for the skill itself
  • User-facing documentation — skills are for Copilot, not humans
  • Information Copilot already knows (general language features, common libraries)

Skill Creation Workflow

Step 1: Understand the Domain

Identify concrete scenarios the skill addresses:

  • What tasks trigger it?
  • What does Copilot get wrong without it?
  • What project-specific knowledge is needed?

Step 2: Plan Contents

For each scenario, determine:

  • What code patterns or templates are repeatedly needed?
  • What reference material should be available?
  • What pitfalls must be called out?

Step 3: Write the Skill

  1. Create .github/skills/{skill-name}/SKILL.md
  2. Write frontmatter: name and description (description is the trigger — be comprehensive about when to use)
  3. Write body: Core workflow, patterns, examples. Keep it lean.
  4. Add references (optional): Split out detailed format specs, schemas, or variant-specific docs
  5. Register in .github/copilot-instructions.md

Step 4: Validate

  • Ensure the description covers all trigger scenarios
  • Verify code examples follow project conventions (see .github/copilot-instructions.md and .github/instructions/)
  • Check that referenced files exist and paths are correct
  • Confirm the skill doesn't duplicate information already in .github/instructions/ files

Existing Skills Reference

SkillPurpose
writing-release-notesRelease notes and changelog generation
accessibility-aria-expertAccessibility issues in React/Fluent UI webviews
webview-trpc-messagingtRPC communication between extension host and webviews

Example: Minimal Skill

---
name: my-pattern
description: Implements the XYZ pattern for this codebase. Use when creating new XYZ instances, modifying existing XYZ behavior, or debugging XYZ-related issues.
---

# XYZ Pattern

## When to Use

- Creating a new XYZ
- Modifying XYZ behavior
- Debugging XYZ issues

## Pattern

\`\`\`typescript
// core pattern example
\`\`\`

## Common Pitfalls

- Don't do X because Y
- Always do Z when W

Mehr Skills von microsoft

oss-growth
microsoft
OSS-Wachstums-Hacker-Persona
agent-framework-azure-ai-py
microsoft
Erstellen Sie Azure AI Foundry-Agents mit dem Microsoft Agent Framework Python SDK (agent-framework-azure-ai). Verwenden Sie dies beim Erstellen persistenter Agents mit AzureAIAgentsProvider, bei der Nutzung gehosteter Tools (Code-Interpreter, Dateisuche, Websuche), bei der Integration von MCP-Servern, bei der Verwaltung von Konversationsthreads oder bei der Implementierung von Streaming-Antworten. Umfasst Funktionstools, strukturierte Ausgaben und Multi-Tool-Agents.
development
airunway-aks-setup
microsoft
Richte AI Runway auf AKS ein – vom leeren Cluster bis zum laufenden Modell. Umfasst Cluster-Überprüfung, Controller-Installation, GPU-Bewertung, Provider-Einrichtung und erste Bereitstellung. WANN: „AI Runway einrichten“, „AKS-Cluster onboarden“, „AI Runway installieren“, „airunway setup“, „Modell auf AKS bereitstellen“, „GPU-Inferenz auf AKS“, „KAITO-Setup auf AKS“, „LLM auf AKS ausführen“, „vLLM auf AKS“, „Modell-Serving auf AKS einrichten“, „AI Runway-Controller“.
devops
appinsights-instrumentation
microsoft
Leitfaden zur Instrumentierung von Webanwendungen mit Azure Application Insights. Bietet Telemetriemuster, SDK-Einrichtung und Konfigurationsreferenzen. WANN: wie man eine App instrumentiert, App Insights SDK, Telemetriemuster, was ist App Insights, Application Insights-Anleitung, Instrumentierungsbeispiele, APM-Best Practices.
devops
applicationinsights-web-ts
microsoft
Instrumentieren Sie Browser-/Web-Apps mit dem Application Insights JavaScript SDK (@microsoft/applicationinsights-web). Verwenden Sie es für Real User Monitoring (RUM) – Seitenaufrufe, Klicks, AJAX/Fetch-Abhängigkeiten, Ausnahmen, benutzerdefinierte Ereignisse und browser-seitige GenAI-Agent-Traces, die mit Backend-OpenTelemetry-Traces korreliert werden. Umfasst SDK-Loader-Skript und npm-Setup, Framework-Erweiterungen (React, React Native, Angular), Click Analytics, Telemetrie-Initialisierer und OTel-GenAI-Semantik-Konventionen für Agent-/Tool-/Modell-Spans, die vom Browser ausgegeben werden.
devops
azure-ai-anomalydetector-java
microsoft
Erstellen Sie Anomalieerkennungsanwendungen mit dem Azure AI Anomaly Detector SDK für Java. Verwenden Sie dies bei der Implementierung von univariater/multivariater Anomalieerkennung, Zeitreihenanalyse oder KI-gestützter Überwachung.
development
azure-ai-language-conversations-py
microsoft
Implementieren Sie Conversational Language Understanding (CLU) mit dem azure-ai-language-conversations Python SDK. Verwenden Sie dies, wenn Sie mit ConversationAnalysisClient arbeiten, um Gesprächsabsichten und Entitäten zu analysieren, NLP-Funktionen zu erstellen oder Sprachverständnis in Anwendungen zu integrieren.
development
azure-ai-ml-py
microsoft
Azure Machine Learning SDK v2 für Python. Verwenden für ML-Workspaces, Jobs, Modelle, Datensätze, Compute und Pipelines. Auslöser: „azure-ai-ml“, „MLClient“, „Workspace“, „Modell-Registry“, „Trainings-Jobs“, „Datensätze“.
development