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Agent Skill · 研究检索

how(如何)

how 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

how 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,权限为执行命令;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 33,756。

总安装 2,305周安装 98GitHub Stars 661下载 808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:how(如何)
来源仓库:https://github.com/poteto/how
仓库路径:skills/how
安装命令:
npx skills add https://github.com/poteto/how --skill how
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/poteto/how --skill how

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。

用于查找、检索和筛选相关信息,支持根据关键词或任务场景定位内容。适合在需要信息聚合的场景下使用,可结合来源仓库和原始 README 核验具体用法。安装命令:npx skills add https://github.com/poteto/how --skill how。

适用于 Codex、Claude、Cursor、Gemini CLI,通过 GitHub 安装。建议确认权限范围和维护状态,注意是否触发联网或文件读写操作。

来源仓库内路径:skills/how

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/poteto/how --skill how;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

    它需要执行命令,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

how 怎么安装?

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

how 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。

how 需要什么权限?

权限级别是执行命令。安装流程涉及命令执行,可能通过 npx skills add https://github.com/poteto/how --skill how 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

how 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。

how 用的人多吗?

公开数据是累计安装 2,305 次,近一周安装 98 次,下载 808 次,GitHub 661 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。

how 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

  • 需要执行命令,装到常用环境前要先评估影响面。
  • 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

  • 不能接受 Skill 在你机器上执行命令的人。
  • 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
how

如何 · 本页

2,305661执行命令未明确通过

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

How

Explore the codebase to answer "how does X work?" questions. Produce clear architectural explanations at the level of a senior engineer onboarding onto a subsystem — enough to build a working mental model, not so much that it reads like annotated source code.

Two modes:

  1. Explain (default) — explore the codebase and produce a clear explanation
  2. Critique — explain first, then spawn multiple models to independently identify architectural issues

Explain Mode

Step 1 — Understand the Question and Assess Complexity

Parse what the user is asking about. They might say:

  • "How does message virtualization work?" — a subsystem
  • "How do we handle billing for on-demand usage?" — a feature flow
  • "How is the auth service structured?" — an architectural overview
  • "Walk me through what happens when a user sends a message" — a runtime trace

Identify the scope. If it's ambiguous, make your best guess and state your interpretation before exploring. Don't ask — explore and let the user redirect if you're off.

Assess complexity to decide the approach:

  • Simple (a single module, a small utility, a narrow question like "how does function X work"): Skip explorer agents entirely. The explainer agent explores and explains in a single pass. Go directly to Step 2b.
  • Complex (a subsystem spanning multiple files/services, a cross-cutting feature, a full architectural overview): Spawn parallel explorer agents first, then hand off to the explainer. Go to Step 2a.

When in doubt, lean toward the simple path — you can always spawn explorers if the explainer hits a wall.

Step 2a — Explore (complex questions only)

Decompose the question into 2-4 parallel exploration angles. Each angle should cover a distinct slice of the subsystem so the explorers aren't duplicating work. For example, if the question is "how does message virtualization work?", you might split into:

  • Explorer 1: the data model and state management
  • Explorer 2: the rendering pipeline and DOM interaction
  • Explorer 3: the scroll/measurement infrastructure

The right decomposition depends on the question — use your judgment. For narrow questions, 2 explorers is fine. For broad subsystems, use up to 4.

Spawn all explorers in a single message:

  • subagent_type: generalPurpose
  • model: gpt-5.4
  • readonly: true

Each explorer gets the same base prompt from references/explorer-prompt.md, plus a specific exploration angle telling it which slice to focus on. Each explorer should:

  • Start broad: Glob for relevant directories, Grep for key types/interfaces/class names
  • Follow the thread: once you find an entry point, trace the call chain — callers, callees, data flow, type definitions
  • Read the actual code, don't guess from file names
  • Stop when you can describe the full path from input to output (or from trigger to effect) without hand-waving any step
  • Note things that are surprising, non-obvious, or that a newcomer would get wrong

Each explorer returns structured findings: the components it found, the flow it traced, the files it read, and anything non-obvious. Overlap between explorers is fine — the explainer will reconcile.

Then proceed to Step 3.

Step 2b — Direct Explain (simple questions)

Spawn a single Task subagent that explores and explains in one pass:

  • subagent_type: generalPurpose
  • model: claude-opus-4.6
  • readonly: true

This agent does its own exploration (Glob, Grep, Read) and writes the explanation directly. Read references/explainer-prompt.md for the communication style and output format — the agent follows the same structure, it just doesn't have explorer findings as input.

Proceed to Step 4.

Step 3 — Synthesize (complex questions only)

Once all explorers have returned, spawn a single Task subagent to synthesize their findings into one coherent explanation:

  • subagent_type: generalPurpose
  • model: claude-opus-4.6
  • readonly: true

The explainer gets all explorers' findings and writes the human-facing explanation (see output format below). Read references/explainer-prompt.md for the full prompt template. The explainer reconciles overlapping findings, resolves contradictions, and weaves the separate slices into a unified picture.

Step 4 — Present

Take the explainer's output and present it to the user. You may lightly edit for clarity or add context from the conversation, but don't substantially rewrite — the explainer agent's communication is the product.

Output Format

The explanation should follow this structure, but adapt it to what makes sense for the question. Not every section is needed for every question.

Overview — 1-2 paragraphs. What is this thing, what does it do, why does it exist. Someone should be able to read this and decide whether they need to keep reading.

Key Concepts — The important types, services, or abstractions. Brief definition of each, not exhaustive — just the ones needed to understand the rest.

How It Works — The core of the explanation. Walk through the flow: what triggers it, what happens step by step, where does data go, what are the decision points. Use prose, not pseudocode. Reference specific files and functions so the reader can go look, but don't dump code blocks unless a specific snippet is genuinely necessary to understand the point.

Where Things Live — A brief map of the relevant files/directories. Not every file — just the ones someone would need to find to start working in this area.

Gotchas — Things that are non-obvious, surprising, or that would trip someone up. Historical context that explains why something looks weird. Known sharp edges.

Critique Mode

Triggered when the user asks for architectural issues, problems, or improvements — not just understanding.

Step 1 — Explain First

Run the full explain flow above (Steps 1-4). You need to understand the architecture before you can critique it.

Step 2 — Spawn Critics

After the explanation is complete, spawn architectural critics. Launch all in a single message:

SubagentModel
Critic Aclaude-opus-4.6
Critic Bcomposer-2
Critic Cgpt-5.4

For each critic:

  • subagent_type: generalPurpose
  • model: the model from the table. These are starting suggestions — escalate to a higher reasoning tier of the same model family when the architecture warrants deeper analysis.
  • readonly: true

Read references/critic-prompt.md for the prompt template. Each critic gets:

  1. The explanation from Step 1 (so they don't waste time re-exploring)
  2. The relevant file paths (so they can read the actual code)
  3. The architectural critique rubric from references/critique-rubric.md

Step 3 — Lead Judgment

You're a pragmatic lead, not an aggregator.

Categorize findings:

  • Act on — Architectural problems worth fixing now
  • Consider — Real concerns, but the cost/benefit is unclear
  • Noted — Valid observations, low priority
  • Dismissed — Wrong, missing context, or style preference

Present the explanation first (from Step 1), then the critique verdict below it. The explanation should stand on its own — someone who just wants to understand the system shouldn't have to wade through critique.

平台分布

Codex

38.64%
按下载量换算312

Claude

30.16%
按下载量换算244

Cursor

19.1%
按下载量换算154

Gemini CLI

9.91%
按下载量换算80

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

执行命令

安装流程涉及命令执行,可能通过 npx skills add https://github.com/poteto/how --skill how 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

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。

继续浏览同类 Skills