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終端機啟動器

在可見的終端機視窗中,透過可重複使用的無 Shell 啟動計畫(包含乾執行、JSON、功能偵測、分離後備與獨立恢復模式)來啟動一個可執行檔及其引數陣列。當 Codex 需要在新主機終端機中開啟互動式 CLI、SSH 工作階段、本地開發程序、沙箱或其他以 argv 為基礎的命令時使用;診斷支援的終端機是否可用;或在請求的終端機不受支援時提供可行計畫。

終端機啟動器 是什麼?

Terminal Opener 是一項 AI 助手技能,旨在透過結構化且安全的「啟動計畫」來啟動終端機視窗與命令,繞過傳統的 Shell 字串內插。它強制使用 `shell: false` 來調用子程序,從而降低命令注入風險,並保留環境變數(包括秘密資訊)而不進行過濾。該技能支援 WezTerm 作為首選適配器,提供多種模式:標準啟動(嘗試替代終端機多工器後,若無法使用則分離回退)、恢復模式(透過 `--skip-config start --always-new-process` 跳過使用者組態),以及僅檢查模式(使用 `--dry-run --json` 來檢查確切的 argv、工作目錄及故障路徑,無需實際開啟視窗)。使用者也可以執行 `--detect--json` 來檢查終端機的可用性,當無法取得支援的終端機時,會傳回未支援的計畫而非執行命令。主要的實作腳本位於 `scripts/open-terminal.js`,適用於 Codex、Cursor 或任何需要安全啟動互動式終端機工作階段的 AI 編碼代理程式。

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

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說明文件

終端機啟動器 是做什麼的?

使用 scripts/open-terminal.js 將可執行檔與每個引數保留為獨立的程序項目。請勿內插 Shell 命令字串。所有 spawn 都保持 shell: false。預設為非啟動計畫。僅在使用者明確要求實際視窗且 argv 已審查後才使用 --launch。啟動的程序會繼承呼叫程序的完整環境,包括包含秘密的變數。啟動器不會過濾環境。請在一個環境對目標命令安全的 Shell 中執行它。

啟動命令

在 -- 之前傳遞啟動器選項,然後傳遞一個可執行檔及其引數陣列:

node skills/terminal-opener/scripts/open-terminal.js \
  --launch \
  --cwd /absolute/host/path \
  -- ssh -t example.test command-with-arguments

先執行普通模式。讓 WezTerm 嘗試其多工器並開啟新視窗,如果多工器不可用,則讓啟動器回退到分離的 wezterm start 程序。當使用回退時,從 JSON 輸出(或可讀的失敗行)讀取 muxFailure,以診斷多工器路徑失敗的原因。

從終端機組態恢復

當使用者組態或多工器狀態可能干擾請求的命令時,加入 --recover 或 --standalone。使用以下指令啟動分離的 WezTerm 程序:

--skip-config start --always-new-process

預期恢復模式會有意跳過所有使用者終端機組態。

啟動前檢查

省略 --launch(或加入 --dry-run)並加入 --json,以檢查確切的可執行檔、argv、工作目錄、終端機適配器、主要啟動與回退,而不開啟視窗。將 JSON 計畫視為呼叫者的組合邊界。

在沒有命令的情況下執行 --detect --json 以探查終端機可用性。當適配器缺失或不受支援時,請遵循傳回的 action。對目前適配器使用 WezTerm;將其他請求的終端機視為不受支援的計畫,而不是要執行的命令。

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.

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