skill-creator

Meta-Skill zur Erstellung neuer DLM-Diagnose-Skills. Verwenden Sie dieses Skill, wenn Sie einen neuen Troubleshooting-Leitfaden (SKILL.md + Referenzdateien) erstellen müssen, der den folgenden Richtlinien folgt…

npx skills add https://github.com/microsoft/purview-dlm-mcp --skill skill-creator

Skill Creator

Create new diagnostic skills for the DLM Diagnostics MCP server. Each skill is a self-contained directory with a SKILL.md and supporting reference files.

Prerequisites

  • Familiarity with the existing skill structure (see .github/skills/dlm-diagnostics/ as the canonical example).
  • Knowledge of the PowerShell cmdlet allowlist in src/powershell/allowlist.ts.
  • Understanding of the project's read-only safety model.

Safety Rules

  1. Only reference read-only cmdlets — allowed verb prefixes: Get-*, Test-*, Export-*.
  2. Never include mutating commands in diagnostic stepsSet-*, New-*, Remove-*, Enable-*, Start-*, Invoke-* must only appear in the "Recommended Actions" output section, clearly marked as manual-review-only.
  3. All cmdlets used in the skill must exist in src/powershell/allowlist.ts — if a new Get-* cmdlet is needed, add it to the allowlist as a separate change.
  4. No credentials or secrets — never embed tokens, passwords, or connection strings in skill files.

SKILL.md Template

Every skill must have a SKILL.md at its root with the following structure:

---
name: <skill-name>
description: "<One-paragraph description of when to invoke this skill. Include symptom keywords for matching.>"
---

# <Skill Title>

<Brief overview of what this skill investigates.>

## Prerequisites

<Required PowerShell sessions or environment setup.>

## Safety Rules

<Read-only enforcement rules specific to this skill.>

## Decision Tree

| Symptom | Reference |
|---------|-----------|
| <symptom description> | [<filename>.md](references/<filename>.md) |

## Workflow

1. **Identify the symptom** — match user description to the decision tree.
2. **Load the reference file** — read it fully before starting.
3. **Execute diagnostic commands** — step by step using `run_powershell`.
4. **Interpret results** — follow the reference guide's evaluation criteria.
5. **Cross-reference** — load linked references when directed.
6. **Report findings** — summarize using the Output Format below.
7. **Review execution log** — use `get_execution_log` for the audit trail.

## Output Format

<Standard investigation summary template.>

Reference File Structure

Each reference file in references/ should follow this pattern:

# <Issue Title>

## Overview
<Brief description of the issue and when it occurs.>

## Diagnostic Steps

### Step N: <Step Name>
**Command:**
\`\`\`powershell
<Get-* / Test-* / Export-* command>
\`\`\`
**Evaluate:**
- <What to look for in the output>
- <Condition that indicates a problem>

## Root-Cause Table

| Finding | Root Cause | Remediation |
|---------|-----------|-------------|
| <diagnostic finding> | <why it happens> | <fix command or action — marked as manual> |

## Cross-References
- [related-file.md](related-file.md) — <when to follow this link>

Creation Workflow

Follow these steps to create a new skill:

  1. Name the skill — use kebab-case (e.g., edr-diagnostics, compliance-search-triage).
  2. Write the SKILL.md — follow the template above. Start with the YAML frontmatter name and description.
  3. Build the decision tree — list every symptom the skill can diagnose, each mapping to a reference file.
  4. Create reference files — one .md per symptom in a references/ subdirectory. Each must include diagnostic steps with exact PowerShell commands, evaluation criteria, a root-cause table, and cross-references.
  5. Validate cmdlets — every cmdlet used in diagnostic steps must be in src/powershell/allowlist.ts. If a new read-only cmdlet is needed, add it to the allowlist.
  6. Test the flow — mentally walk through at least one symptom end-to-end: decision tree → reference file → commands → evaluation → root cause → remediation.
  7. Write the output format — ensure it matches the standard investigation summary template.
  8. Mirror the skill — copy the complete skill directory to both locations:
    • .github/skills/<skill-name>/
    • .claude/skills/<skill-name>/
  9. Update documentation — if the skill introduces new cmdlets, update CLAUDE.md and .github/copilot-instructions.md.
  10. Commit — commit both skill copies and any allowlist changes together.

Validation Checklist

Before finalizing a new skill, verify:

  • SKILL.md has valid YAML frontmatter with name and description
  • Description includes symptom keywords for AI matching
  • Decision tree covers all intended symptoms
  • Every decision tree entry links to an existing reference file
  • All reference files exist in references/ subdirectory
  • All diagnostic commands use only Get-*, Test-*, or Export-* cmdlets
  • All cmdlets are present in src/powershell/allowlist.ts
  • Mutating commands appear only in remediation sections with manual-review warnings
  • Reference files include root-cause tables
  • Skill is mirrored in both .github/skills/ and .claude/skills/
  • Relative references/ links resolve correctly from both locations

Canonical Example

See .github/skills/dlm-diagnostics/ (or .claude/skills/dlm-diagnostics/) for the reference implementation. It demonstrates all conventions: YAML frontmatter, decision tree, 12 reference files, safety rules, workflow, and output format.

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
Set up AI Runway on AKS — from bare cluster to running model. Covers cluster verification, controller install, GPU assessment, provider setup, and first deployment. WHEN: "setup AI Runway", "onboard AKS cluster", "install AI Runway", "airunway setup", "deploy model to AKS", "GPU inference on AKS", "KAITO setup on AKS", "run LLM on AKS", "vLLM on AKS", "set up model serving on AKS", "AI Runway controller".
devops
appinsights-instrumentation
microsoft
Guidance for instrumenting webapps with Azure Application Insights. Provides telemetry patterns, SDK setup, and configuration references. WHEN: how to instrument app, App Insights SDK, telemetry patterns, what is App Insights, Application Insights guidance, instrumentation examples, 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