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github/azure-deployment-preflight

执行对Azure Bicep部署的全面预检验证,包括模板语法验证、what-if分析和权限检查。在进行任何Azure部署前使用此技能,预览更改,识别潜在问题,确保部署成功。

azure-deployment-preflight 是什么?

该技能用于在执行Azure Bicep资源部署之前进行全面的预检验证。它集成了模板语法检查、what-if部署模拟和权限验证,帮助开发者提前发现并解决潜在问题,从而避免部署失败或意外变更。支持通过`az deployment group create`、`az deployment sub create`等命令自动完成验证流程,并输出详细的错误信息和部署预览。主要适用于需要部署到Azure(如使用Bicep或`azd provision`)的场景,尤其是当开发者提及“验证Bicep”、“查看部署预览”、“检查权限”或“准备azd provision”时,应自动激活该技能。该技能与Claude Code、GitHub Copilot等AI编码助手兼容,可显著提升Azure部署的可靠性。

兼容平台~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/github/awesome-copilot/tree/main/skills/azure-deployment-preflight

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文档

azure-deployment-preflight 是做什么的?

对Azure Bicep部署执行全面的预检验证,包括模板语法验证、what-if分析和权限检查。在进行任何Azure部署前使用此技能,预览更改,识别潜在问题,确保部署成功。当用户提及部署到Azure、验证Bicep文件、检查部署权限、预览基础设施更改、运行what-if或准备azd provision时激活。

功能

  • 语法验证:对Bicep文件运行az bicep build,确保模板语法正确。
  • What-If分析:使用az deployment group what-if或az deployment sub what-if模拟部署,预览将对Azure资源产生的影响。
  • 权限检查:验证用于部署的服务主体或用户账户是否具有所需的RBAC权限。
  • 依赖检查:确保所有必需的资源(如资源组、网络、密钥保管库等)已存在且配置正确。
  • 配置验证:检查azuredeploy.parameters.json等参数文件是否正确。
  • 成本估算:可选地提供部署的预估成本。

用法示例

# 语法验证
az bicep build --file main.bicep

# What-If分析(资源组范围)
az deployment group what-if \
  --resource-group myResourceGroup \
  --template-file main.bicep \
  --parameters @azuredeploy.parameters.json

# What-If分析(订阅范围)
az deployment sub what-if \
  --location eastus \
  --template-file main.bicep \
  --parameters @azuredeploy.parameters.json

# 权限检查(创建验证部署)
az deployment group validate \
  --resource-group myResourceGroup \
  --template-file main.bicep \
  --parameters @azuredeploy.parameters.json

触发条件

当用户提出以下任何请求时,自动激活此技能:

  • “帮我验证这个Bicep文件”
  • “在对Azure部署之前,帮我预览一下有什么变化”
  • “检查我的资源组权限是否正确”
  • “运行一次what-if分析”
  • “准备azd provision,但先预检一下”
  • “我的Bicep模板有问题,帮我排查”
  • “我需要确保这个部署能成功”

输出示例

语法验证通过

✅ Bicep语法验证通过

What-If输出(部分示例)

资源:Microsoft.Storage/storageAccounts (mystorageaccount)
  - 将创建
  - 属性更改:
    - sku.name: "Standard_LRS" -> "Standard_GRS"

资源:Microsoft.Network/virtualNetworks (myvnet)
  - 保持不变

资源:Microsoft.Compute/virtualMachines (myvm)
  - 将创建
  - 属性:...

权限检查结果

✅ 当前用户/服务主体具有对资源组 'myResourceGroup' 的 'Contributor' 权限。
或
❌ 缺少权限:需要 'Microsoft.Resources/deployments/write' 操作。

限制

  • What-If分析不保证部署100%成功(例如,配额限制或延迟的RBAC传播)。
  • 权限检查基于Azure RBAC,不涵盖Azure Policy或Azure Blueprints的限制。
  • 建议在CI/CD管道中也包含此预检步骤。

版本历史

  • v1.0: 初始版本,包含语法验证、What-If分析和权限检查。

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

github/acquire-codebase-knowledge

Use this skill when the user explicitly asks to map, document, or onboard into an existing codebase. Trigger for prompts like "map this codebase", "document this architecture", "onboard me to this repo", or "create codebase docs". Do not trigger for routine feature implementation, bug fixes, or narrow code edits unless the user asks for repository-level discovery.

github/add-educational-comments

Add educational comments to the file specified, or prompt asking for file to comment if one is not provided.

github/agent-governance

Patterns and techniques for adding governance, safety, and trust controls to AI agent systems. Use this skill when: - Building AI agents that call external tools (APIs, databases, file systems) - Implementing policy-based access controls for agent tool usage - Adding semantic intent classification to detect dangerous prompts - Creating trust scoring systems for multi-agent workflows - Building audit trails for agent actions and decisions - Enforcing rate limits, content filters, or tool restrictions on agents - Working with any agent framework (PydanticAI, CrewAI, OpenAI Agents, LangChain, AutoGen)

github/agentic-eval

Patterns and techniques for evaluating and improving AI agent outputs. Use this skill when: - Implementing self-critique and reflection loops - Building evaluator-optimizer pipelines for quality-critical generation - Creating test-driven code refinement workflows - Designing rubric-based or LLM-as-judge evaluation systems - Adding iterative improvement to agent outputs (code, reports, analysis) - Measuring and improving agent response quality

github/agent-owasp-compliance

Check any AI agent codebase against the OWASP Agentic Security Initiative (ASI) Top 10 risks. Use this skill when: - Evaluating an agent system's security posture before production deployment - Running a compliance check against OWASP ASI 2026 standards - Mapping existing security controls to the 10 agentic risks - Generating a compliance report for security review or audit - Comparing agent framework security features against the standard - Any request like "is my agent OWASP compliant?", "check ASI compliance", or "agentic security audit"

github/ai-prompt-engineering-safety-review

Comprehensive AI prompt engineering safety review and improvement prompt. Analyzes prompts for safety, bias, security vulnerabilities, and effectiveness while providing detailed improvement recommendations with extensive frameworks, testing methodologies, and educational content.

github/appinsights-instrumentation

Instrument a webapp to send useful telemetry data to Azure App Insights

github/apple-appstore-reviewer

Serves as a reviewer of the codebase with instructions on looking for Apple App Store optimizations or rejection reasons.

github/architecture-blueprint-generator

Comprehensive project architecture blueprint generator that analyzes codebases to create detailed architectural documentation. Automatically detects technology stacks and architectural patterns, generates visual diagrams, documents implementation patterns, and provides extensible blueprints for maintaining architectural consistency and guiding new development.

github/arch-linux-triage

Triage and resolve Arch Linux issues with pacman, systemd, and rolling-release best practices.

github/arize-ai-provider-integration

Creates, reads, updates, and deletes Arize AI integrations that store LLM provider credentials used by evaluators and other Arize features. Supports any LLM provider (e.g. OpenAI, Anthropic, Azure OpenAI, AWS Bedrock, Vertex AI, Gemini, NVIDIA NIM). Use when the user mentions AI integration, LLM provider credentials, create integration, list integrations, update credentials, delete integration, or connecting an LLM provider to Arize.

github/arize-annotation

Creates and manages annotation configs (categorical, continuous, freeform label schemas) and annotation queues (human review workflows) on Arize. Applies human annotations to project spans via the Python SDK. Use when the user mentions annotation config, annotation queue, label schema, human feedback, bulk annotate spans, update_annotations, labeling queue, annotate record, or human review.

github/arize-dataset

Creates, manages, and queries Arize datasets and examples. Covers dataset CRUD, appending examples, exporting data, and file-based dataset creation using the ax CLI. Use when the user needs test data, evaluation examples, or mentions create dataset, list datasets, export dataset, append examples, dataset version, golden dataset, or test set.

github/arize-evaluator

Handles LLM-as-judge evaluation workflows on Arize including creating/updating evaluators, running evaluations on spans or experiments, managing tasks, trigger-run operations, column mapping, and continuous monitoring. Use when the user mentions create evaluator, LLM judge, hallucination, faithfulness, correctness, relevance, run eval, score spans, score experiment, trigger-run, column mapping, continuous monitoring, or improve evaluator prompt.

github/arize-experiment

Creates, runs, and analyzes Arize experiments for evaluating and comparing model performance. Covers experiment CRUD, exporting runs, comparing results, and evaluation workflows using the ax CLI. Use when the user mentions create experiment, run experiment, compare models, model performance, evaluate AI, experiment results, benchmark, A/B test models, or measure accuracy.

github/arize-instrumentation

Adds Arize AX tracing to an LLM application for the first time. Follows a two-phase agent-assisted flow to analyze the codebase then implement instrumentation after user confirmation. Use when the user wants to instrument their app, add tracing from scratch, set up LLM observability, integrate OpenTelemetry or openinference, or get started with Arize tracing.

github/arize-link

Generates deep links to the Arize UI for traces, spans, sessions, datasets, labeling queues, evaluators, and annotation configs. Produces clickable URLs for sharing Arize resources with team members. Use when the user wants to link to or open a trace, span, session, dataset, evaluator, or annotation config in the Arize UI.

github/arize-prompt-optimization

Optimizes, improves, and debugs LLM prompts using production trace data, evaluations, and annotations. Extracts prompts from spans, gathers performance signal, and runs a data-driven optimization loop using the ax CLI. Use when the user mentions optimize prompt, improve prompt, make AI respond better, improve output quality, prompt engineering, prompt tuning, or system prompt improvement.

github/arize-trace

Downloads, exports, and inspects existing Arize traces and spans to understand what an LLM app is doing or debug runtime issues. Covers exporting traces by ID, spans by ID, sessions by ID, and root-cause investigation using the ax CLI. Use when the user wants to look at existing trace data, see what their LLM app is doing, export traces, download spans, investigate errors, or analyze behavior regressions.

github/aspire

Aspire skill covering the Aspire CLI, AppHost orchestration, service discovery, integrations, MCP server, VS Code extension, Dev Containers, GitHub Codespaces, templates, dashboard, and deployment. Use when the user asks to create, run, debug, configure, deploy, or troubleshoot an Aspire distributed application.

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