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github/devops-rollout-plan

生成完整的發布計畫，包含預檢檢查、逐步部署、驗證信號、回滾程序及基礎設施與應用變更的通報計畫。

devops-rollout-plan 是什麼？

此技能專為 DevOps 工程師與平台團隊設計，能用於生成結構化且全面的部署發布計畫。它會自動納入預檢檢查清單（例如服務狀態、依賴項就緒性）、逐步部署步驟、驗證信號（如監控指標、端點測試）、回滾程序（觸發條件與執行步驟）及利害關係人通報範本。此技能支援多種平台（如 AWS、Kubernetes 或自託管環境），可透過分析輸入的基礎設施或應用變更請求，輸出可執行的 Markdown 文件。其工作流程基於 SKILL.md 文件中定義的提示範本，能確保每次發布都涵蓋風險評估與變更管理的最佳實踐，特別適合 CI/CD 流程與變更審查委員會（CAB）使用。

相容平台~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/github/awesome-copilot/tree/main/skills/devops-rollout-plan

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說明文件

devops-rollout-plan 是做什麼的？

生成完整的發布計畫，包含預檢檢查、逐步部署、驗證信號、回滾程序及基礎設施與應用變更的通報計畫。

功能

  • 自動產生預檢檢查清單，確保發布環境就緒
  • 提供逐步部署步驟，支援金絲雀發布或藍綠部署策略
  • 內建驗證信號，可定義監控指標、端點健康檢查或日誌模式
  • 包含回滾程序，指定觸發條件、回滾步驟與驗證方法
  • 通報計畫範本，涵蓋利害關係人、時程與回滾通知

使用方式

# 範例：為服務升級產生發布計畫
github/devops-rollout-plan --service "api-gateway" --version "v2.1.0" --environment "staging"

輸入參數

參數說明範例
--service目標服務名稱api-gateway
--version部署版本v2.1.0
--environment部署環境staging, production
--rollback-strategy回滾策略immediate, gradual

產出範例

發布計畫摘要

# 發布計畫：api-gateway v2.1.0 部署至 staging

## 預檢檢查
- [ ] 確認服務依賴項（資料庫、快取、佇列）皆為健康狀態
- [ ] 驗證 Docker 映像檔標籤 `api-gateway:v2.1.0` 存在於 Container Registry
- [ ] 確認 Kubernetes 叢集節點資源充足
- [ ] 檢查最後一次成功的 CI/CD 管線執行

## 逐步部署
1. 更新 Kubernetes Deployment 映像檔版本
2. 逐步增加新 Pod 副本數（先啟動 1 個 Pod 作為金絲雀）
3. 監控錯誤率與延遲指標，持續 5 分鐘
4. 若指標正常，逐步取代所有舊 Pod
5. 完成後執行端點冒煙測試

## 驗證信號
- HTTP 200 回應率 >= 99.9%
- P99 延遲 < 200ms
- 錯誤日誌數量未增加

## 回滾程序
- **觸發條件**：驗證信號失敗或異常警報觸發
- **步驟**：
  1. 立即將 Deployment 映像檔版本還原為 `api-gateway:v2.0.0`
  2. 確認所有 Pod 重新啟動並服務恢復正常
  3. 通知團隊與持份者
- **驗證**：執行預檢檢查中的健康檢查腳本

## 通報計畫
- **部署開始前**：通知 Slack 頻道 `#deployments` 與 `#oncall`
- **部署完成後**：若成功，發送摘要至電子郵件群組 `[email protected]`
- **回滾啟動時**：立即呼叫 PagerDuty 並更新 Jira 工單

支援的平台

  • AWS（ECS, EKS, Lambda）
  • Kubernetes（K8s）
  • 自託管 VM 或裸機

相依性

  • 需要已安裝並設定 kubectl 或 aws-cli
  • 環境變數：CLUSTER_NAME、CONTAINER_REGISTRY、SLACK_WEBHOOK_URL

貢獻

歡迎提交 Issue 或 Pull Request 以改善本技能。

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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