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Agent Skill · 前端设计

deployment-automation(部署自动化)

用于辅助云资源、部署、容器、基础设施和运维自动化任务。它适合让 Agent 检查配置、整理部署步骤、分析资源状态、生成排障思路或辅助云服务接入。使用时需要明确目标环境、账号权限、区域和资源组,区分本地测试与生产操作;涉及删除资源、重启服务、修改网络或权限配置时,应先确认影响范围。

deployment-automation 是前端设计类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为接触敏感数据;许可证信息还不明确,第三方扫描已通过,按安装量在前端设计类的 10,216 个 Skill 里排第 2,739。

总安装 1,952周安装 83GitHub Stars 11下载 684核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:deployment-automation(部署自动化)
来源仓库:https://github.com/akillness/oh-my-skills
仓库路径:skills/deployment-automation
安装命令:
npx skills add https://github.com/akillness/oh-my-skills --skill deployment-automation
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/akillness/oh-my-skills --skill deployment-automation

这个 Skill 是做什么的

前端设计类 Skill 关注页面结构、视觉规范和组件写法,输出通常是可运行的界面代码。

用于辅助云资源管理、部署流程、容器编排和运维自动化任务。适合检查配置状态、整理发布步骤、分析资源健康度或生成排障方案。使用时需确认目标环境、账号权限和资源组,区分本地测试与生产操作。涉及删除资源、修改网络或权限时,应先评估影响范围并确认安全策略。

deployment-automation 属于前端设计类 Skill,可作为该场景下的辅助能力补充。

来源仓库内路径:skills/deployment-automation

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/akillness/oh-my-skills --skill deployment-auto…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要接触敏感数据,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

deployment-automation 怎么安装?

在终端执行 npx skills add https://github.com/akillness/oh-my-skills --skill deployment-automation。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

deployment-automation 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

deployment-automation 需要什么权限?

权限级别是接触敏感数据。该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

deployment-automation 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

deployment-automation 用的人多吗?

公开数据是累计安装 1,952 次,近一周安装 83 次,下载 684 次,GitHub 11 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

deployment-automation 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 需要接触敏感数据,装到常用环境前要先评估影响面。
  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理前端设计类任务、又不想每次都手写提示词的人。

不太适合

  • 环境里有大量密钥、凭证,不方便给第三方 Skill 读取的人。
  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

前端设计类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
frontend-design

前端设计 · Codex、Claude

8,823,360126,072只读MIT通过
vercel-composition-patterns

Vercel 组合模式 · Codex、Claude

3,858,81625,893需要联网MIT通过
vercel-react-native-skills

Vercel React native skills 前端 · Codex、Claude

2,468,66425,878只读MIT通过
adapt

adapt 搜索 · Codex、Claude

2,027,04023,125操作浏览器未明确通过
bolder

bolder 分析 · Codex、Claude

2,004,28823,130只读未明确通过
deployment-automation

部署自动化 · 本页

1,95211接触敏感数据未明确通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

Deployment Automation

Use this skill when the job is safe release execution after the system is already buildable.

deployment-automation is the repo's release-execution anchor for:

  • preview releases and shareable review environments
  • staging → production promotion and sign-off gates
  • rollout strategy choice: replace, rolling, blue-green, canary, progressive
  • post-deploy verification and stop conditions
  • rollback response and bad-release containment
  • release-hardening packets around an existing deploy path

Read these support docs before deciding the packet:

When to use this skill

  • A preview or branch deployment exists, but nobody agrees what it is supposed to prove before merge.
  • A release already works mechanically, but staging/prod promotion still depends on tribal knowledge.
  • The main decision is rollout strategy, release gates, verification depth, or rollback readiness.
  • A deploy failed and the next job is containment, rollback, and safe recovery rather than broad platform redesign.
  • The team needs a vendor-neutral release packet before using a platform-specific operator skill.
  • You need to tighten an existing deploy path with preflight checks, sign-off logic, and explicit stop conditions.

When not to use this skill

  • The main job is writing or refactoring GitHub Actions / GitLab / Jenkins / Buildkite workflows → workflow-automation
  • The main job is installing Docker, kubectl, cloud CLIs, auth bootstrap, or making the machine runnable → system-environment-setup
  • The main job is dashboards, alerts, traces, or long-lived telemetry architecture → monitoring-observability
  • The main job is Vercel-specific linking, domains, aliases, environment variables, or claim URLs → vercel-deploy
  • The system cannot build, package, or run tests yet → fix build/setup problems before reopening deployment automation
  • The main job is secret rotation, IAM policy, signing, or compliance architecture → route to the relevant security skill

Instructions

Step 1: Freeze the release profile

Normalize the request before suggesting steps.

release_profile:
  mode: preview-release | environment-promotion | container-paas-rollout | kubernetes-rollout | rollback-response | release-hardening
  runtime_shape: static-frontend | web-app | api-service | worker-job | multi-service | unknown
  artifact_shape: platform-build | image | build-output | package | unknown
  target_environment: preview | staging | production | mixed | unknown
  promotion_model: same-artifact-promotion | rebuild-per-env | direct-deploy | unknown
  rollout_strategy: replace | rolling | blue-green | canary | progressive | feature-flag-assisted | unknown
  stateful_risk: low | medium | high | unknown
  verification_depth: health-only | smoke-tests | release-checklist | automated-analysis | unknown

Pick exactly one primary mode per run:

  • preview-release — shareable verification before merge or promotion
  • environment-promotion — staging → production or multi-env promotion with gates
  • container-paas-rollout — managed app/container platform release planning
  • kubernetes-rollout — rollout controller behavior, stop conditions, and rollback path
  • rollback-response — bad deploy containment and recovery
  • release-hardening — add missing preflight, approvals, or verification around an existing flow

Step 2: Gather the smallest truthful evidence set

Do not invent a release flow from vibes. Capture the minimum real facts:

  1. What artifact is actually being shipped?
  2. Which environment is in scope right now?
  3. Is the same artifact promoted across environments, or rebuilt per environment?
  4. What is the current deploy command, provider action, or controller?
  5. What health checks, smoke checks, or sign-off signals already exist?
  6. What is the real rollback method?
  7. Are there schema/data changes that make rollback asymmetric?

If any answer is unknown, state it explicitly and default to the smallest safe interpretation.

Step 3: Enforce boundaries early

Use these route-outs before generating a packet:

If the request is really about...Route to
CI YAML, job graphs, reusable release workflows, task runnersworkflow-automation
Docker/kubectl/cloud CLI installation, auth bootstrap, machine readinesssystem-environment-setup
Dashboards, alerts, traces, log pipelines, SLO monitoringmonitoring-observability
Vercel project linking, aliases, domains, env vars, Vercel deploy mechanicsvercel-deploy
Secret rotation, IAM scope, signing, attestation, compliance evidencesecurity skill
Rollout choice, promotion gates, verification, rollback packetdeployment-automation

Step 4: Load the right support packet instead of expanding the front door

After classification, use the matching reference packet:

  • preview-release → references/release-packet-templates.md#preview-release-packet
  • environment-promotion → references/release-packet-templates.md#environment-promotion-packet
  • container-paas-rollout → references/release-packet-templates.md#containerpaas-rollout-packet
  • kubernetes-rollout → references/release-packet-templates.md#kubernetes-rollout-packet
  • rollback-response → references/release-packet-templates.md#rollback-response-packet
  • release-hardening → references/release-packet-templates.md#release-hardening-packet

Use references/strategy-selection-and-stop-conditions.md when choosing between replace / rolling / blue-green / canary / progressive paths. Use references/rollout-and-rollback-checklist.md as the minimum release-safety checklist before finalizing.

Step 5: Apply release-execution rules

  • Separate build from release. A passing build does not prove the release is safe.
  • Separate deploy from exposure. Traffic shifts, promotion gates, and feature flags may matter more than the initial deploy command.
  • Separate app rollback from data rollback. Never imply they are the same.
  • Prefer the smallest rollout strategy that matches the team’s verification maturity.
  • Prefer same-artifact promotion when reproducibility matters. If the system rebuilds per env, call out the risk explicitly.
  • Verification is mandatory. Health + one core flow is the minimum acceptable finish line.
  • Stop conditions beat optimism. Name what should halt promotion or trigger rollback.

Step 6: Produce a release packet, not a DevOps lecture

Your output should always include:

# Release Packet
- Mode:
- Runtime / provider:
- Artifact:
- Environment(s):
- Promotion model:
- Rollout strategy:

## Preconditions
- Existing deploy path:
- Required auth/config:
- Known blockers / migrations:

## Steps
1. ...
2. ...
3. ...

## Verification
- Health checks:
- Smoke checks:
- Human/automatic sign-off:
- Stop conditions:

## Rollback / recovery
- Immediate rollback path:
- What rollback does not undo:

## Handoffs
- Adjacent skills or provider-specific follow-up:

Step 7: Prefer hardening over wrapper proliferation

If the request feels like “deployment is messy,” prefer:

  1. classify the release mode,
  2. name the current source of truth,
  3. add preflight / verification / rollback structure,
  4. route platform detail to the correct adjacent skill.

Do not respond by inventing another generic deployment wrapper, re-explaining all of Kubernetes, or absorbing CI authoring into this skill.

Output format

Return:

  1. chosen release mode,
  2. evidence gathered and unknowns,
  3. rollout strategy with rationale,
  4. verification plan,
  5. rollback or recovery plan,
  6. route-outs or remaining risks.

Examples

Example 1: Preview deploy confusion

Input: "Every PR gets a preview URL, but reviewers keep approving broken flows. What should the deployment process actually require before merge?"

Output shape: classify as preview-release, define what the preview proves, list smoke checks, and separate preview verification from later staging/prod promotion.

Example 2: Staging to production promotion

Input: "We deploy to staging automatically, but production promotion is still a manual Slack ritual. Give us the safest release packet."

Output shape: classify as environment-promotion, name artifact and gates, require post-promote checks, and specify rollback plus schema caveats.

Example 3: Failed production deploy

Input: "The latest release is timing out in production and it also included a migration. We need the safest rollback plan."

Output shape: classify as rollback-response, contain blast radius, separate app rollback from migration risk, and define post-recovery verification.

Best practices

  1. Keep the front door about release decisions, not platform encyclopedias.
  2. Name the real artifact, environment, and rollback path before suggesting anything.
  3. Prefer short release packets that an operator can run under pressure.
  4. Route provider-specific and adjacent concerns out early.
  5. Treat verification and stop conditions as required output, not optional polish.

References

平台分布

Codex

37.83%
按下载量换算259

Claude

32.05%
按下载量换算219

Cursor

19.09%
按下载量换算131

Gemini CLI

8.7%
按下载量换算60

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

接触敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。目前只有一个公开来源,建议同时核对源仓库。

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