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Agent Skill · 研究检索

agentic-cli-design(agentic CLI 设计)

用于辅助界面设计、视觉规范、排版、配色、布局和交互体验优化。它适合让 Agent 根据产品场景整理页面结构、生成 UI 方案、检查视觉一致性或改进组件层级。使用时需要结合现有品牌、设计系统和用户任务,不应只堆装饰元素;涉及真实页面改动时,应通过截图或浏览器预览检查文本溢出、对齐和响应式表现。

agentic-cli-design 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为操作浏览器;许可证信息还不明确,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 54,235。

总安装 514周安装 21GitHub Stars 3下载 166核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:agentic-cli-design(agentic CLI 设计)
来源仓库:https://github.com/tumf/skills
仓库路径:skills/agentic-cli-design
安装命令:
npx skills add https://github.com/tumf/skills --skill agentic-cli-design
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/tumf/skills --skill agentic-cli-design

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

agentic-cli-design 提出 CLI 作为 LLM 可安全调用的协议/API 设计理念。适合设计无歧义、幂等且自描述的终端工具。强调选项显式、默认安全与非交互式重试机制。

安装命令:npx skills add https://github.com/tumf/skills --skill agentic-cli-design。可用于评估和改进现有 CLI 工具的可用性。

来源仓库内路径:skills/agentic-cli-design

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/tumf/skills --skill agentic-cli-design;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要操作浏览器,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

agentic-cli-design 怎么安装?

在终端执行 npx skills add https://github.com/tumf/skills --skill agentic-cli-design。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

agentic-cli-design 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

agentic-cli-design 需要什么权限?

权限级别是操作浏览器。该 Skill 可能涉及浏览器控制能力,使用时可能读取或操作网页内容,需要在受控环境中确认权限边界。

agentic-cli-design 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

agentic-cli-design 用的人多吗?

公开数据是累计安装 514 次,近一周安装 21 次,下载 166 次,GitHub 3 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

agentic-cli-design 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
agentic-cli-design

agentic CLI 设计 · 本页

5143操作浏览器未明确通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

Agentic CLI Design

Overview

Agentic CLI Design is a design philosophy that treats CLIs not as "tools for humans to operate in terminals," but as protocols/APIs that LLMs/agents can safely, reliably, and repeatably invoke.

The success criteria is that agents can operate the CLI:

  • Without confusion (options are explicit)
  • Without breaking things (safe-by-default)
  • Without getting stuck (non-interactive, timeout/retry strategies)
  • Repeatedly (idempotent, re-execution tolerant)
  • With self-healing (observable, self-describing)

This skill provides a comprehensive framework for designing and evaluating CLI tools for agent use.

Core Definition

Agentic CLI Design = *Design principles for CLIs that enable LLM/agents to execute commands safely and reliably in non-interactive, repetitive, failure-prone environments.*

Rather than optimizing for human "feel," we optimize for machines to read, decide, re-execute, and recover.

The 7 Principles

P1. Machine-readable (Primary Output Format)

Machine-readable output is the primary interface, not human-readable text.

  • Provide --json / --output json|yaml|text as a first-class option
  • Strict separation: stdout=results / stderr=logs/progress (never mix)
  • Errors are also structured (preferably JSON)
  • Schema stability (manage breaking changes with schemaVersion)

When to use: Every CLI command should support structured output.

P2. Non-interactive by Default

Interactive prompts should not be the default behavior.

  • Don't assume interactive prompts
  • Provide --yes / --force / --no-confirm / --non-interactive flags
  • Must complete successfully in TTY-less environments (CI, job runners)

When to use: Any command that might prompt for user input.

P3. Idempotent & Replayable

Commands should be safe to run multiple times with the same result.

  • Send/create operations accept dedupe-key / client-request-id
  • "Already exists" behavior is configurable: --if-exists skip|update|error
  • Retrieval uses explicit pagination: --limit --cursor --all

When to use: Any write operation or data retrieval command.

P4. Safe-by-default

Destructive operations require explicit confirmation.

  • Destructive operations support --dry-run / --confirm <id>
  • Deletion requires --force or similar (default prevents accidents)
  • Permissions/scopes are minimized; insufficient permissions return "next steps"

When to use: Any command that modifies or deletes data.

P5. Observable & Debuggable

Operations must be traceable and debuggable.

  • Provide --verbose / --debug / --log-format json
  • Accept --trace-id for correlation IDs
  • Exit codes are categorized for automatic recovery:

- 0: success - 2: invalid arguments - 3: authentication/permission - 4: retryable (rate limit, transient errors)

When to use: All commands should support observability flags.

P6. Context-efficient

Minimize token/context consumption for LLM agents.

  • --fields/--select (projection)
  • --output ndjson (streaming)
  • Default is summary; details via get/--include-* explicitly
  • Rich server-side filtering (since/until/query/type...)

When to use: Commands that return large datasets or detailed information.

P7. Introspectable (Self-describing CLI)

The CLI itself can emit its specification in machine-readable format.

MCP tools have schema definitions, but CLIs are often black boxes. Solution: CLI emits its own specification.

  • commands --json (list commands and arguments)
  • schema --command... --output json-schema (per-command JSON Schema)
  • --help --json (examples, exit codes, error vocabulary)
  • install-skills (emit/install the CLI's own agent skill from <project dir>/skills)
  • --output json top-level fixed fields:

- schemaVersion, type, ok

Recommended install-skills behavior:

  • Installs the CLI's own usage skill, not arbitrary third-party skills
  • Reads skill source from <project dir>/skills
  • Default destination: ./.agents/skills
  • install-skills --global: ~/.agents/skills
  • install-skills --claude: ./.claude/skills
  • install-skills --claude --global: ~/.claude/skills

When to use: For CLIs that need to be discoverable and self-documenting to agents.

Reference Documentation

This skill includes detailed reference materials organized by topic:

references/principles.md

Detailed explanation of each of the 7 principles with concrete examples, implementation guidance, and best practices.

Use when: You need in-depth understanding of a specific principle or implementation examples.

references/scorecard.md

Complete checklist for reviewing CLI design against the 7 principles. Each principle has specific checkboxes for evaluation.

Use when: Reviewing existing CLIs for agent-friendliness or validating new CLI designs.

references/templates.md

Concrete templates for:

  • JSON response formats (success/failure)
  • Exit code conventions
  • Introspection command patterns
  • Authentication status responses

Use when: Implementing specific features like JSON output, error handling, or self-description.

references/anti-patterns.md

Common failure patterns that break agent compatibility, including:

  • Mixed stdout/stderr
  • Inconsistent JSON schemas
  • Interactive-by-default behavior
  • Unsafe destructive operations
  • Context explosion
  • Browser-only authentication

Use when: Troubleshooting agent failures or avoiding common pitfalls.

Quick Start

For New CLI Design

  1. Review the 7 principles above
  2. Use references/scorecard.md as a design checklist
  3. Implement using patterns from references/templates.md
  4. Avoid patterns in references/anti-patterns.md

For Existing CLI Review

  1. Run through references/scorecard.md
  2. Identify gaps in principle coverage
  3. Check references/anti-patterns.md for current issues
  4. Prioritize fixes based on agent use cases

For Agent Skill Creation

When creating an Agent Skill for a CLI tool, include:

  • Task-specific recipes (minimal command sequences)
  • Guardrails (dry-run → confirm → execute patterns)
  • Input/output types (typical success/failure JSON)
  • Error recovery procedures (rate limit, auth_required, etc.)
  • Recommended defaults (--json/--non-interactive/--limit)
  • An install-skills flow so the CLI can install its own skill from <project dir>/skills

Authentication Considerations

For OAuth/headless authentication:

  • Prefer Device Authorization Grant (RFC 8628) as first choice
  • Provide auth status --json for agents to check prerequisites
  • Support auth export / auth import for headless environment migration
  • In --non-interactive mode: return error + next steps (don't prompt)

See references/templates.md for authentication response formats.

Scoring Your CLI

Each principle can be scored 0/1/2 points for easy comparison:

  • 0: Not implemented
  • 1: Partially implemented
  • 2: Fully implemented

Maximum score: 14 points (2 points × 7 principles)

Use references/scorecard.md for detailed scoring criteria.

Related Concepts

  • MCP (Model Context Protocol): Provides tool definitions with schemas; Agentic CLI Design makes standalone CLIs equally discoverable
  • Agent Skills: Documentation layer that teaches agents safe CLI usage patterns
  • Infrastructure as Code: Shares idempotency and declarative principles
  • 12-Factor Apps: Shares configuration and observability principles

Examples in the Wild

Well-designed CLIs that demonstrate these principles:

  • GitHub CLI (gh): Excellent --json support, non-interactive flags
  • kubectl: Strong idempotency, declarative apply, dry-run support
  • AWS CLI: Comprehensive --output json, pagination, filtering

When to Use This Skill

Use this skill when:

  1. Designing a new CLI intended for agent/automation use
  2. Improving an existing CLI to be more agent-friendly
  3. Creating an Agent Skill for a CLI tool
  4. Debugging agent failures with CLI tools
  5. Reviewing CLI design for production readiness
  6. Choosing between CLI tools for agent workflows

When NOT to Use This Skill

This skill may be overkill for:

  • One-off scripts for personal use
  • CLIs exclusively for human interactive use
  • Prototypes not intended for production
  • Tools with inherently interactive workflows (text editors, REPLs)

Further Reading

平台分布

Codex

38.58%
按下载量换算64

Claude

28.26%
按下载量换算47

Cursor

18.12%
按下载量换算30

Gemini CLI

8.74%
按下载量换算15

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

操作浏览器

该 Skill 可能涉及浏览器控制能力,使用时可能读取或操作网页内容,需要在受控环境中确认权限边界。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。目前只有一个公开来源,建议同时核对源仓库。

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