Communitygithub.com

終端操作

針對 ECC 的證據優先儲存庫執行工作流程。當使用者希望執行指令、檢查儲存庫、除錯 CI 失敗,或是推送具有精確執行與驗證證明的狹義修正時使用。

終端操作 是什麼?

終端操作(Terminal Ops)是一種針對 ECC 平台的技能,專注於證據優先的終端執行工作流程。它的核心目標是確保在儲存庫上進行的所有操作(如執行指令、檢查 Git 狀態、除錯 CI 或建置問題、進行狹義修正)都有明確、可追溯的執行與驗證證明。此技能比一般的程式碼輔導更為狹義,它定義了一套嚴格的工作流程,包括:解析工作環境(儲存庫路徑、分支、差異狀態)、先檢查失敗表面再執行修改、保持修正範圍狹窄、並使用精確的狀態詞彙(檢查過、本地修改、本地驗證、已提交、已推送、受阻)回報執行狀態。它支援與 verification-loop、tdd-workflow、security-review、github-ops、knowledge-ops 等 ECC 原生技能整合,以應對更複雜的場景。其主要優勢在於防止盲目修改、強制基於證據的驗證、避免對儲存庫造成不必要的破壞,最終提供可稽核的、可靠的終端操作結果。

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

在你喜歡的 AI 中提問

開啟一個已預先載入此 Agent Skill 的新對話。

說明文件

終端操作 是做什麼的?

當使用者想要在真實儲存庫中執行操作時使用此技能:執行指令、檢查 Git 狀態、除錯 CI 或建置、進行狹義修正,並精確回報哪些內容已變更及經過驗證。

此技能特意比一般程式碼輔導更為狹義。它是一種用於證據優先終端執行的操作員工作流程。

技能組合

在相關時,將這些 ECC 原生技能拉入工作流程:

  • verification-loop 用於在變更後進行精確的證明步驟
  • tdd-workflow 當正確的修正需要回歸測試覆蓋率時
  • security-review 當涉及機密、驗證或外部輸入時
  • github-ops 當任務依賴於 CI 執行、PR 狀態或發佈狀態時
  • 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「修正」、「除錯」、「執行此項」、「檢查儲存庫」或「推送它」
  • 任務依賴於指令輸出、Git 狀態、測試結果或經過驗證的本機修正
  • 答案必須區分「本機變更」、「本機驗證」、「已提交」和「已推送」

護欄

  • 編輯前先檢查
  • 如果使用者僅要求稽核/審查,則保持唯讀模式
  • 優先使用儲存庫本機的腳本和輔助工具,而非臨時自製的包裝腳本
  • 在重新執行證明指令之前,不要聲稱修正完成
  • 除非分支實際上已推送到上游,否則不要聲稱推送完成

工作流程

1. 解析工作介面

確定:

  • 確切的儲存庫路徑
  • 分支
  • 本機差異狀態
  • 請求的模式:
    • 檢查
    • 修正
    • 驗證
    • 推送

2. 先讀取失敗介面

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
  • 先使用任何已提供的日誌或背景,再盲目地重新讀取

3. 保持修正範圍狹窄

一次解決一個主要失敗:

  • 首先使用最小的有用證明指令
  • 只有在解決本機失敗之後,才升級到更大的建置/測試通過
  • 如果某個指令持續以相同的特徵失敗,則停止廣泛的重試並縮小範圍

4. 回報精確的執行狀態

使用精確的狀態詞彙:

  • 已檢查
  • 已在本機變更
  • 已在本機驗證
  • 已提交
  • 已推送
  • 已封鎖

輸出格式

SURFACE
- 儲存庫
- 分支
- 請求的模式

EVIDENCE
- 失敗的指令 / 差異 / 測試

ACTION
- 變更了什麼

STATUS
- 已檢查 / 已在本機變更 / 已在本機驗證 / 已提交 / 已推送 / 已封鎖

陷阱

  • 當可以讀取即時儲存庫狀態時,不要根據過時的記憶進行操作
  • 不要將狹義的修正擴大為儲存庫範圍的翻新
  • 不要使用破壞性的 Git 指令
  • 不要忽略無關的本機工作

驗證

  • 回應中需命名證明指令或測試
  • 與 Git 相關的工作需命名儲存庫路徑和分支
  • 任何推送聲明都需包含目標分支和確切結果

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/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.

相關技能