skill-creator

作者: microsoft

Meta-skill for creating new DLM diagnostic skills. Use this skill when you need to author a new troubleshooting guide (SKILL.md + reference files) that follows…

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.

来自 microsoft 的更多技能

oss-growth
microsoft
OSS增长黑客角色
agent-framework-azure-ai-py
microsoft
使用Microsoft Agent Framework Python SDK(agent-framework-azure-ai)构建Azure AI Foundry代理。在创建使用AzureAIAgentsProvider的持久化代理、使用托管工具(代码解释器、文件搜索、网络搜索)、集成MCP服务器、管理对话线程或实现流式响应时使用。涵盖函数工具、结构化输出和多工具代理。
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
使用Azure Application Insights对Web应用进行插桩的指南。提供遥测模式、SDK设置和配置参考。适用场景:如何对应用进行插桩、App Insights SDK、遥测模式、什么是App Insights、Application Insights指南、插桩示例、APM最佳实践。
devops
applicationinsights-web-ts
microsoft
使用Application Insights JavaScript SDK(@microsoft/applicationinsights-web)为浏览器/Web应用添加检测。用于真实用户监控(RUM)——页面视图、点击、AJAX/fetch依赖项、异常、自定义事件,以及与后端OpenTelemetry追踪关联的浏览器端GenAI代理追踪。涵盖SDK加载器脚本和npm设置、框架扩展(React、React Native、Angular)、点击分析、遥测初始化器,以及从浏览器发出的代理/工具/模型跨度所遵循的OTel GenAI语义约定。
devops
azure-ai-anomalydetector-java
microsoft
使用适用于 Java 的 Azure AI 异常检测器 SDK 构建异常检测应用程序。在实现单变量/多变量异常检测、时间序列分析或 AI 驱动的监控时使用。
development
azure-ai-language-conversations-py
microsoft
使用azure-ai-language-conversations Python SDK实现对话语言理解(CLU)。当使用ConversationAnalysisClient分析对话意图和实体、构建NLP功能或将语言理解集成到应用程序中时使用。
development
azure-ai-ml-py
microsoft
Azure Machine Learning SDK v2 for Python。用于机器学习工作区、作业、模型、数据集、计算资源和管道。 触发词:“azure-ai-ml”、“MLClient”、“工作区”、“模型注册表”、“训练作业”、“数据集”。
development