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terminal-ops

基于证据的仓库执行工作流,适用于ECC。当用户需要运行命令、检查仓库、调试CI失败或推送精确修复并附带所执行和验证的确切证据时使用。

terminal-ops 是什么?

Terminal Ops 是一个专注于基于证据的终端执行工作流技能,旨在帮助开发者在实际仓库环境中精确执行命令、调试故障并报告变更。该技能支持多种平台如 Claude Code、Cursor 等,通过严格的流程确保每个操作都有据可查。它包含工作流步骤(如解析工作区、读取故障表面、保持修复窄化、报告执行状态)和安全护栏(如先检查再编辑、避免破坏性命令)。此外,技能集成了 validation-loop、tdd-workflow、security-review、github-ops 和 knowledge-ops 等 ECC 原生技能,以增强验证、回归测试、安全审计和结果归档能力。输出格式统一,包括表面信息、证据、操作和状态,确保透明度和可追溯性。

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

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文档

Terminal Ops

当用户需要实际的仓库执行时使用:运行命令、检查 git 状态、调试 CI 或构建、进行窄修复,并精确报告更改内容和已验证内容。

该技能有意比通用编码指导更窄。它是一个基于证据的终端执行的操作员工作流。

技能栈

在相关时将这些 ECC 原生技能拉入工作流:

  • verification-loop 用于更改后的精确证明步骤
  • tdd-workflow 当正确的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 当已验证的结果需要捕获到持久项目上下文中时

何时使用

  • 用户说“修复”、“调试”、“运行此”、“检查仓库”或“推送它”
  • 任务依赖于命令输出、git 状态、测试结果或已验证的本地修复
  • 答案必须区分:本地更改、本地验证、已提交、已推送

安全护栏

  • 编辑前先检查
  • 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,而不是临时自制的包装器
  • 在重新运行证明命令之前,不要声称已修复
  • 除非分支实际已推送到上游,否则不要声称已推送

工作流

1. 解析工作表面

确定:

  • 确切的仓库路径
  • 分支
  • 本地差异状态
  • 请求的模式:
    • inspect
    • fix
    • verify
    • push

2. 先读取故障表面

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
  • 使用任何已提供的日志或上下文,而不是盲目重新读取

3. 保持修复窄化

一次解决一个主要故障:

  • 首先使用最小的有用证明命令
  • 仅在本地故障解决后才升级到更大的构建/测试过程
  • 如果命令以相同的签名持续失败,则停止广泛的重试并缩小范围

4. 报告确切的执行状态

使用精确的状态词:

  • inspected
  • changed locally
  • verified locally
  • committed
  • pushed
  • blocked

输出格式

SURFACE
- repo
- branch
- requested mode

EVIDENCE
- failing command / diff / test

ACTION
- what changed

STATUS
- inspected / changed locally / verified locally / committed / pushed / blocked

陷阱

  • 当可以读取实时仓库状态时,不要依赖过时的记忆
  • 不要将窄修复扩展到整个仓库的混乱更改
  • 不要使用破坏性的 git 命令
  • 不要忽略不相关的本地工作

验证

  • 响应中需命名证明命令或测试
  • git 相关的工作需命名仓库路径和分支
  • 任何推送声明需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/claude-api

Anthropic Claude API patterns for Python and TypeScript. Covers Messages API, streaming, tool use, vision, extended thinking, batches, prompt caching, and Claude Agent SDK. Use when building applications with the Claude API or Anthropic SDKs.

affaan-m/everything-claude-code

End-to-end marketing campaign planning and execution. Covers audience research, positioning, campaign angle definition, landing page copy, email sequences, social posts, ad copy, short-form video scripts, and content calendars. Use as the orchestration layer for multi-channel product launches. Use when planning or executing a multi-channel product launch, or producing landing page, email, social, or ad copy.

affaan-m/everything-claude-code

Development conventions and patterns for everything-claude-code. JavaScript project with conventional commits.

affaan-m/everything-claude-code-conventions

Development conventions and patterns for everything-claude-code. JavaScript project with conventional commits.

affaan-m/frontend-design

Create distinctive, production-grade frontend interfaces with high design quality. Use when the user asks to build web components, pages, or applications and the visual direction matters as much as the code quality.

affaan-m/gget

gget CLI and Python workflow for quick genomic database queries, sequence lookup, BLAST-style searches, enrichment checks, and reproducible bioinformatics evidence logs.

affaan-m/literature-review

Systematic literature-review workflow for academic, biomedical, technical, and scientific topics, including search planning, source screening, synthesis, citation checks, and evidence logging.

affaan-m/motion-ui

Production-ready UI motion system for React/Next.js. Use when implementing animations, transitions, or motion patterns.

affaan-m/project-guidelines-example

Example project-specific skill template based on a real production application.

affaan-m/pubmed-database

Direct PubMed and NCBI E-utilities search workflows for biomedical literature, MeSH queries, PMID lookup, citation retrieval, and API-backed literature monitoring.

affaan-m/scholar-evaluation

Structured scholarly-work evaluation for papers, proposals, literature reviews, methods sections, evidence quality, citation support, and research-writing feedback.

affaan-m/uspto-database

USPTO patent and trademark data workflow for official record lookup, PatentSearch queries, TSDR checks, assignment data, and reproducible IP research logs.

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.

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