Communitygithub.com

终端运维操作

适用于需要实际仓库执行操作的场景:运行命令、检查Git状态、调试CI/构建失败、进行精准修复,并精确报告执行和验证的每一步证据。

终端运维操作 是什么?

这是一个面向AI代码助手(如Claude Code、Coder)的专家技能,专注于以“证据优先”的方式执行仓库运维任务。其核心价值在于确保每一次命令执行、代码变更和推送都有明确的可追溯证据。它支持多种主流平台,如Claude Code和通用AI编码代理。工作流程严格遵循“先只读检查,再修改”的步骤,要求每次操作前都必须解析当前工作目录、分支和本地差异状态。技能特别强调避免使用破坏性Git命令,并严格区分“已检查”、“已本地修改”、“已验证”、“已提交”和“已推送”等不同执行阶段。输出格式固定,包含工作表面、证据、操作和状态四个部分,便于审计。此外,该技能可与其他ECC原生技能(如验证循环、测试驱动开发、安全审查)无缝集成,以处理更复杂的修复或验证场景。

兼容平台~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/affaan-m/ECC/tree/main/skills/terminal-ops

在你喜欢的 AI 中提问

打开一个已预加载此 Agent Skill 的新对话。

文档

终端运维操作 是做什么的?

当用户需要真实的仓库执行操作时使用:运行命令、检查Git状态、调试CI或构建、实施精确修复,并精确报告哪些内容已更改和哪些内容已验证。

本技能刻意比通用编码指导更为狭窄。它是一套面向证据优先终端执行的操作工作流。

技能栈

在相关场景下,将以下ECC原生技能引入工作流:

  • verification-loop 用于变更后的精确验证步骤
  • tdd-workflow 当修复需要回归覆盖时
  • security-review 当涉及密钥、认证或外部输入时
  • github-ops 当任务依赖于CI运行、PR状态或发布状态时
  • knowledge-ops 当需要将验证的结果捕获到持久的项目上下文中时

使用时机

  • 用户说“修复”、“调试”、“运行这个”、“检查仓库”或“推送它”
  • 任务依赖于命令输出、Git状态、测试结果或经过验证的本地修复
  • 答案必须区分“本地已更改”、“本地已验证”、“已提交”和“已推送”

护栏

  • 先检查再编辑
  • 如果用户仅要求审计/审查,则保持只读
  • 优先使用仓库本地脚本和辅助工具,而非临时拼凑的包装器
  • 在验证命令重新运行之前,不得声称已修复
  • 除非分支实际已推送到上游,否则不得声称已推送

工作流

1. 解析工作表面

确定:

  • 精确的仓库路径
  • 分支
  • 本地差异状态
  • 请求的模式:
    • 检查
    • 修复
    • 验证
    • 推送

2. 先读取失败表面

在更改任何内容之前:

  • 检查错误
  • 检查文件或测试
  • 检查Git状态
  • 优先使用已提供的日志或上下文,避免盲目重新读取

3. 保持修复范围狭窄

一次解决一个主要失败:

  • 首先使用最小且有效的验证命令
  • 仅当本地失败得到解决后,才升级到更大的构建/测试流程
  • 如果某个命令持续以相同特征失败,则停止广泛重试并缩小范围

4. 报告精确的执行状态

使用精确的状态词汇:

  • 已检查
  • 本地已更改
  • 本地已验证
  • 已提交
  • 已推送
  • 已阻塞

输出格式

工作表面
- 仓库
- 分支
- 请求的模式

证据
- 失败的命令 / 差异 / 测试

操作
- 更改的内容

状态
- 已检查 / 本地已更改 / 本地已验证 / 已提交 / 已推送 / 已阻塞

陷阱

  • 当可以读取实时仓库状态时,不要依赖过时的记忆
  • 不要将狭窄的修复扩大到仓库范围的更迭
  • 不要使用破坏性的Git命令
  • 不要忽略无关的本地工作

验证

  • 响应中应指明验证命令或测试
  • 与Git相关的工作应指明仓库路径和分支
  • 任何声称推送的内容必须包含目标分支和确切结果

Individual skills in this repo

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

accessibility

Design, implement, and audit inclusive digital products using WCAG 2.2 Level AA. Use when building or auditing UI that must meet WCAG 2.2 Level AA, or when reviewing a change for keyboard, contrast, or screen-reader support.

affaan-m/content-engine

Create platform-native content systems for X, LinkedIn, TikTok, YouTube, newsletters, and repurposed multi-platform campaigns. Use when the user wants social posts, threads, scripts, content calendars, or one source asset adapted cleanly across platforms.

affaan-m/ECC

Multi-platform content distribution across X, LinkedIn, Threads, and Bluesky. Adapts content per platform using content-engine patterns. Never posts identical content cross-platform. Use when the user wants to distribute content across social platforms.

affaan-m/fal-ai-media

Unified media generation via fal.ai MCP — image, video, and audio. Covers text-to-image (Nano Banana), text/image-to-video (Seedance, Kling, Veo 3), text-to-speech (CSM-1B), and video-to-audio (ThinkSound). Use when the user wants to generate images, videos, or audio with AI.

affaan-m/manim-video

日本語翻訳:このファイルは manim-video 用の日本語翻訳が必要です

affaan-m/remotion-video-creation

Remotion のベストプラクティス - React で動画を作成する。3D、アニメーション、音声、字幕、チャート、トランジションなどをカバーするドメイン固有の29のルール。

affaan-m/video-editing

AI-assisted video editing workflows for cutting, structuring, and augmenting real footage. Covers the full pipeline from raw capture through FFmpeg, Remotion, ElevenLabs, fal.ai, and final polish in Descript or CapCut. Use when the user wants to edit video, cut footage, create vlogs, or build video content.

agent-architecture-audit

Full-stack diagnostic for agent and LLM applications. Audits the 12-layer agent stack for wrapper regression, memory pollution, tool discipline failures, hidden repair loops, and rendering corruption. Produces severity-ranked findings with code-first fixes. Essential for developers building agent applications, autonomous loops, or any LLM-powered feature. Use when an agent or LLM feature misbehaves and the failing layer is unknown, or before shipping an agent stack.

agent-eval

Head-to-head comparison of coding agents (Claude Code, Aider, Codex, etc.) on custom tasks with pass rate, cost, time, and consistency metrics. Use when choosing between coding agents, or when a change to an agent setup needs measured pass rate, cost, and time rather than an impression.

agent-harness-construction

Design and optimize AI agent action spaces, tool definitions, and observation formatting for higher completion rates. Use when defining or revising an agent

agentic-engineering

Operate as an agentic engineer using eval-first execution, decomposition, and cost-aware model routing. Use when planning or executing engineering work that agents will carry out end to end.

agentic-os

Build persistent multi-agent operating systems on Claude Code. Covers kernel architecture, specialist agents, slash commands, file-based memory, scheduled automation, and state management without external databases. Use when building a persistent multi-agent system on Claude Code with its own memory, commands, and scheduling.

agent-introspection-debugging

Structured self-debugging workflow for AI agent failures using capture, diagnosis, contained recovery, and introspection reports. Use when an agent run fails and you need a reproducible diagnosis instead of a retry.

agent-payment-x402

Add x402 payment execution to AI agents with per-task budgets, spending controls, and non-custodial wallets. Supports Base through agentwallet-sdk and X Layer through OKX Payments / OKX Agent Payments Protocol. Use when an agent must pay for something itself and needs per-task budgets, spending controls, and a non-custodial wallet.

agent-self-evaluation

Use after completing any non-trivial task. The agent self-rates its output on 5 axes — accuracy, completeness, clarity, actionability, conciseness — with concrete evidence per criterion. Produces a structured 1-5 scorecard with specific improvement suggestions.

agent-sort

Build an evidence-backed ECC install plan for a specific repo by sorting skills, commands, rules, hooks, and extras into DAILY vs LIBRARY buckets using parallel repo-aware review passes. Use when ECC should be trimmed to what a project actually needs instead of loading the full bundle.

ai-first-engineering

Engineering operating model for teams where AI agents generate a large share of implementation output. Use when setting team process, review gates, or ownership rules for a codebase largely written by agents.

ai-regression-testing

Regression testing strategies for AI-assisted development. Sandbox-mode API testing without database dependencies, automated bug-check workflows, and patterns to catch AI blind spots where the same model writes and reviews code. Use when adding regression coverage to AI-assisted code, or when the same model both wrote and reviewed a change.

android-clean-architecture

Clean Architecture patterns for Android and Kotlin Multiplatform projects — module structure, dependency rules, UseCases, Repositories, and data layer patterns. Use when structuring modules, layers, or data flow in an Android or KMP project.

angular-developer

Generates Angular code and provides architectural guidance. Trigger when creating projects, components, or services, or for best practices on reactivity (signals, linkedSignal, resource), forms, dependency injection, routing, SSR, accessibility (ARIA), animations, styling (component styles, Tailwind CSS), testing, or CLI tooling.

相关技能