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Production Readiness Sre

Organization
devcodex-labs
production-readiness-sre

生产可用性 / SRE 专家 Owner — 当任务涉及发布风险、运行稳定性、可观测性、容量、资源生命周期、内存泄漏、回滚、故障恢复、运行手册、长连接、队列、缓存或生产验收时使用；要求把实现映射到可运行、可监控、可恢复、可回滚。

Overview

Publisherdevcodex-labs
Repositorydevcodex
Skill nameproduction-readiness-sre
Stars
433
Forks
44
Bundled files
1
LicenseAGPL-3.0
Links
Skill sourceRepositoryHomepage
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by devcodex-labs on GitHub. Read the source before you install it.

Installation

Install the Production Readiness Sre AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/devcodex-labs/devcodex/tree/main/content/skills/production-readiness-sre
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/devcodex-labs/devcodex.git /tmp/devcodex
mkdir -p .claude/skills
cp -r /tmp/devcodex/content/skills/production-readiness-sre .claude/skills/production-readiness-sre
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Production Readiness Sre in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Production Readiness Sre on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Production Readiness Sre is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Production Readiness SRE Skill

定位

本 Skill 负责生产可用性与 SRE Owner 视角。它把“代码能跑”提升为“生产可运行、可观察、可扩展、可恢复、可回滚”，尤其关注长运行服务、资源生命周期、泄漏风险、容量边界和发布失败后的恢复。

触发条件

场景是否触发
服务、CLI、Hook、长连接、队列、缓存、数据库、文件句柄、定时任务或后台进程变化必须
发布、回滚、生产验收、稳定性、压测、内存泄漏、资源清理或故障恢复必须
用户要求“全面验证”“发版前检查”“生产可用”“压测/泄漏风险”必须
一次性本地文档修改且无运行面N/A + skipReason

核心门禁

Gate要求证据
ProductionReadinessSreGate变更必须说明可观测性、容量假设、故障模式、回滚和运行证据logs、metrics、tests、runbook、release evidence
ObservabilityPlanGate关键路径有日志、指标、trace 或可诊断输出observabilityPlan
CapacityAssumptionGate容量、并发、数据量、超时、重试和限流假设明确capacityAssumption
LeakRiskStabilityGate长生命周期资源必须检查释放、泄漏风险和必要压测路线resource lifecycle、pressure test
FailureModeRecoveryGate超时、下游失败、部分失败、重试风暴和数据不一致有恢复策略failureModes
RollbackRunbookGate发布前明确回滚条件、回滚步骤、验证和负责人/触发器rollbackPlan、runbookEntry

执行步骤

  1. 判断运行面：进程、端口、连接、队列、缓存、文件、定时任务、Hook 或发布包。
  2. 建立可观测性：日志字段、错误码、指标、trace、命令输出或报告证据。
  3. 写容量假设：数据规模、并发、超时、重试、批量大小、内存和 CPU 预算。
  4. 检查资源生命周期：创建、复用、关闭、异常路径释放和泄漏探针。
  5. 枚举故障模式：网络、权限、磁盘、配置、依赖、并发、重复执行和部分失败。
  6. 定义回滚和运行手册：发布前检查、失败判断、回滚命令、验证方式和后续观察。

输出字段

markdown
## ProductionReadinessSreGate

| 字段 | 内容 |
|------|------|
| observabilityPlan | 日志、指标、trace、错误码、报告或命令输出 |
| capacityAssumption | 并发、数据量、超时、重试、限流、资源预算 |
| failureModes | 下游失败、超时、部分失败、重复执行、数据不一致等 |
| rollbackPlan | 回滚条件、步骤、验证和影响范围 |
| runbookEntry | 运行手册、排障入口、监控和恢复动作 |
| releaseRisk | 发布风险、兼容风险、依赖风险和缓解 |
| operationalEvidence | 测试、压测、日志、构建、pack、install smoke 或人工证据 |

反模式

反模式修正
只跑单元测试就宣称可发版补 release-verification、pack/install smoke、回滚和发布后验收
长连接或 watcher 无清理路径记录生命周期并测试 close / cleanup / timeout
压测只看吞吐，不看内存或句柄增长增加 leak-risk stability 指标和采样窗口
本地启动服务后不记录 PID/端口或不清理遵守 ServiceLifecycleCleanup，收尾核验端口释放

与其他 Skill 的关系

  • release-verification / audit-release：发布执行和发布审查分别承接 R0~R7 与 release readiness。
  • test-router：将 SRE 风险映射为单元、集成、负载、稳定性、pack、install smoke 或 manual evidence。
  • audit-project：资源生命周期、内存泄漏和工程稳定性审查叠加本 Skill。
  • expert-output-quality：报告必须用生产可用性证据支撑，不只写“测试通过”。

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Production Readiness Sre AI skill do?

生产可用性 / SRE 专家 Owner — 当任务涉及发布风险、运行稳定性、可观测性、容量、资源生命周期、内存泄漏、回滚、故障恢复、运行手册、长连接、队列、缓存或生产验收时使用；要求把实现映射到可运行、可监控、可恢复、可回滚。

Why use Production Readiness Sre on TypingMind?

Because you install it once and use it with any model. Production Readiness Sre is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Production Readiness Sre in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/devcodex-labs/devcodex/tree/main/content/skills/production-readiness-sre. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Production Readiness Sre?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Production Readiness Sre?

As many as you like. As long as a model supports skills, you can use Production Readiness Sre with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Production Readiness Sre AI skill free?

Yes. It is published on GitHub by devcodex-labs under the AGPL-3.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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