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终端开启器

通过可重用的、无 Shell 的启动计划,在可见终端窗口中打开可执行文件及其参数数组,支持试运行、JSON 输出、能力检测、分离式备用和独立恢复模式。当 Codex 需要在新主机终端中打开交互式 CLI、SSH 会话、本地开发进程、沙箱或其他基于 argv 的命令时使用;诊断是否支持某个终端;或在请求的终端不受支持时提供可操作的计划。

终端开启器 是什么?

Terminal Opener 技能为 AI 助手(如 Codex)提供了一种可靠、安全且可审计的方式,在用户的主机终端中启动任意命令行程序。其核心价值在于避免 shell 字符串注入风险:强制将可执行文件与参数作为独立条目处理,并始终以 `shell: false` 执行 `spawn`,从而消除中间人攻击和转义错误。该技能首先默认生成一个“非启动计划”,用户确认并审核 argv 后才通过 `--launch` 标志真正打开窗口。启动进程会继承调用进程的完整环境(包括含敏感信息的变量),因此要求从环境安全的 shell 中运行。当首选终端协议(如 WezTerm mux)不可用时,技能自动降级为分离式进程(如 `wezterm start`),并通过 JSON 输出诊断失败原因。它还支持 `--recover` / `--standalone` 模式,跳过用户配置以解决配置冲突。通过 `--dry-run` 和 `--detect --json`,用户可以预先检查精确的执行计划、终端适配器可用性以及后备方案,而无需实际打开窗口。

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

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打开一个已预加载此 Agent Skill 的新对话。

文档

Terminal Opener

使用 scripts/open-terminal.js 来将可执行文件及其每个参数作为独立的进程条目保存。切勿拼接 shell 命令字符串。始终以 shell: false 执行 spawn。默认使用非启动计划。仅在用户明确请求真实窗口且 argv 经过审核后,才使用 --launch。启动的进程会继承调用进程的完整环境,包括含有敏感信息的变量。启动器不会对环境进行过滤。请从环境对目标命令安全的 shell 中运行它。

启动一个命令

在 -- 之前传递启动器选项,然后传递一个可执行文件及其参数数组:

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

首先使用普通模式。让 WezTerm 尝试其 mux 并创建一个新窗口,如果 mux 不可用,则让启动器回退到分离的 wezterm start 进程。当使用回退时,从 JSON 输出(或人类可读的失败行)中读取 muxFailure 以诊断 mux 路径失败的原因。

从终端配置中恢复

当用户配置或 mux 状态可能干扰请求的命令时,请添加 --recover 或 --standalone。使用以下命令启动一个分离的 WezTerm 进程:

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

期望恢复模式会故意跳过所有用户终端配置。

启动前检查

省略 --launch(或添加 --dry-run)并添加 --json 以检查确切的可执行文件、argv、工作目录、终端适配器、主要启动和回退,而不打开窗口。将 JSON 计划视为调用者的组合边界。

不带命令运行 --detect --json 以探测终端可用性。当适配器缺失或不受支持时,请遵循返回的 action。对当前适配器使用 WezTerm;将其他请求的终端视为不受支持的计划,而不是要执行的命令。

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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