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terminal-ops

以證據為基礎的倉庫執行工作流程,適用於 ECC。當使用者希望執行命令、檢查倉庫、除錯 CI 失敗,或推送精確修復並提供確切執行與驗證證明時使用。

terminal-ops 是什麼?

Terminal Ops 是一個專為 AI 代理(如 Claude Code、Codex)設計的技能,專注於以證據為基礎的終端機執行工作流程。它能讓代理程式直接在真實的程式碼儲存庫中執行命令、檢查 git 狀態、除錯 CI 或建置失敗,並執行精確的修復。其核心價值在於強制要求完整的可追溯性:所有動作(檢查、本地變更、驗證、提交、推送)都必須明確報告,從而避免模糊的聲明。此技能支援 inspect(檢查)、fix(修復)、verify(驗證)和 push(推送)四種模式,並內建防護機制,例如要求修改前先檢查、保持唯讀狀態直到使用者要求、以及使用最小驗證命令以減少範圍。它與其他 ECC 原生技能(如 verification-loop、tdd-workflow)無縫整合,讓開發者與 AI 代理能以高度透明且可靠的方式合作處理終端機操作任務。

相容平台~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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開啟一個已預先載入此 Agent Skill 的新對話。

說明文件

Terminal Ops

當使用者需要真實的儲存庫執行時使用此技能:執行命令、檢查 git 狀態、除錯 CI 或建置、執行精確修復,並報告實際變更與驗證內容。

此技能刻意比一般程式碼指引更狹隘。它是一個以證據為基礎的終端機執行操作流程。

技能堆疊

在相關情況下將這些 ECC 原生技能引入工作流程:

  • verification-loop:用於變更後的精確證明步驟
  • tdd-workflow:當正確修復需要回歸測試覆蓋時
  • security-review:當涉及機密、認證或外部輸入時
  • github-ops:當任務依賴 CI 執行、PR 狀態或發布狀態時
  • knowledge-ops:當需要將驗證結果擷取到持久的專案背景中時

使用時機

  • 使用者說「修復」、「除錯」、「執行這個」、「檢查倉庫」或「推送它」
  • 任務依賴命令輸出、git 狀態、測試結果或已驗證的本地修復
  • 答案必須區分:已本地變更、已本地驗證、已提交、已推送

防護機制

  • 編輯前先檢查
  • 若使用者僅要求稽核/審查,則維持唯讀
  • 優先使用儲存庫本地腳本與輔助工具,而非臨時自訂包裝
  • 除非重新執行驗證命令,否則不得聲稱已修復
  • 除非分支確實已推送至上游,否則不得聲稱已推送

工作流程

1. 確定工作表面

確認:

  • 確切的儲存庫路徑
  • 分支
  • 本地差異狀態
  • 要求的模式:
    • inspect(檢查)
    • fix(修復)
    • verify(驗證)
    • push(推送)

2. 先讀取失敗表面

在變更任何內容之前:

  • 檢查錯誤
  • 檢查檔案或測試
  • 檢查 git 狀態
  • 使用任何已提供的日誌或背景,避免盲目重新讀取

3. 保持修復範圍精簡

一次解決一個主要失敗:

  • 先使用最小的有效驗證命令
  • 僅在本地失敗解決後,才升級至較大的建置/測試通過
  • 如果命令持續以相同的特徵失敗,停止廣泛重試,縮小範圍

4. 報告確切的執行狀態

使用精確的狀態詞:

  • inspected(已檢查)
  • changed locally(已本地變更)
  • verified locally(已本地驗證)
  • committed(已提交)
  • pushed(已推送)
  • blocked(已封鎖)

輸出格式

SURFACE
- repo
- branch
- requested mode

EVIDENCE
- failing command / diff / test

ACTION
- what changed

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

陷阱

  • 當可直接讀取即時儲存庫狀態時,不要依賴過時的記憶
  • 不要將精確修復擴大為整個儲存庫的改動
  • 不要使用破壞性 git 命令
  • 不要忽略無關的本地工作

驗證

  • 回應需指明驗證命令或測試
  • 涉及 git 的工作需指明儲存庫路徑與分支
  • 任何推送聲明需包含目標分支與確切結果

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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