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eli12eli12 搜索

Agent Skill

eli12 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。

总安装

315

周安装

13

GitHub Stars

13

下载量

103
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

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:eli12(eli12 搜索)
来源仓库:https://github.com/jpcaparas/skills
仓库路径:skills/eli12
安装命令:
npx skills add https://github.com/jpcaparas/skills --skill eli12
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/jpcaparas/skills --skill eli12

简介

eli12 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。

  • 它通过简化语言、结构化输出和类比解释,帮助理解代码逻辑与系统行为,尤其适合新手构建正确心智模型。
  • 使用时应先明确关注的功能路径或子系统,再通过提问缩小搜索范围,确保聚焦具体需求。
  • 安装前需确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写操作。
  • eli12 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

eli12

Explain code so a curious newcomer can build a correct mental model without drowning in jargon.

Inspired by poteto/how, but tuned for accessibility: simpler language, tighter structure, and real-world analogies that stay anchored to the actual code.

Decision Tree

  1. If the user is asking how a subsystem, feature flow, runtime path, or file cluster works, use this skill.
  2. If the request is very vague and the likely search space is large, ask 1-3 short narrowing questions before exploring. Default to scope-first questions such as "Which feature or path do you care about?", "Do you want runtime flow, architecture, or file tour?", and "Should I stay focused on frontend, backend, or data?".
  3. If the question is narrow and local to one function, class, hook, or file, do a direct explain pass after a focused code search.
  4. If the question spans multiple modules, services, or an end-to-end flow, split it into 2-4 exploration angles, gather findings in parallel when the harness allows it, then synthesize.
  5. If the user mainly wants bugs, risks, or architectural critique, explain only enough to ground the discussion, then switch to normal review mode instead of staying inside the teaching frame.
  6. If a real-world analogy would make the explanation fuzzier, use fewer analogies and stay closer to the code.

Quick Reference

SituationOpen / doWhy
User asks something broad like "Explain this repo" with no targetAsk a few short scope questions before reading deeplyPrevents token waste and irrelevant architecture tours
Explain one file, class, or helper simplyRead references/explainer-prompt.mdKeeps the answer direct and digestible
Explain a broad subsystem or runtime flowRead references/explorer-prompt.md, then references/explainer-prompt.mdGather evidence first, simplify second
Need better analogies without getting sloppyRead references/analogy-patterns.mdMaps abstract code ideas to grounded everyday systems
Unsure where simplification goes wrongRead references/gotchas.mdAvoids patronizing tone and misleading shortcuts
Need a ready-made output shapeCopy templates/explanation-outline.mdGives a stable structure for the final explanation
Sanity-check prompt routing locallyRun python3 scripts/probe_eli12.py --prompt "How does auth work?"Verifies trigger and complexity heuristics

What This Skill Optimizes For

  • building the smallest correct mental model first
  • asking for scope before doing an expensive repo tour
  • defining jargon right when it appears
  • using short, concrete paragraphs instead of wall-of-text explainers
  • using friendly ASCII sketches when a flow or boundary is easier to see than to read
  • tying every analogy back to real files, functions, and data flow
  • helping the reader know where to look next in the codebase

Default Operating Mode

  1. If the request is too vague to answer efficiently, ask 1-3 short narrowing questions before opening a large slice of the repo.
  2. State your interpretation of the question once the target is clear.
  3. Search for entry points, key types, and the files that actually move data or decisions.
  4. Trace the real path from trigger to effect. Do not explain from filenames alone.
  5. Collapse the findings into plain language.
  6. Use one grounded analogy per important concept when it helps, then tie it back to exact code names.
  7. Add a small ASCII sketch when topology, control flow, or data movement is easier to grasp visually than in prose.
  8. End with a short map of where the important pieces live.

Output Shape

Use the sections that help. Skip the ones that would add noise.

  • Big Picture - What this thing does and why it exists.
  • Main Pieces - The handful of concepts or files the reader needs in order to follow the rest.
  • The Story - The step-by-step flow from input to output, trigger to effect, or request to response.
  • ASCII Sketch - A compact text diagram for flow, boundaries, or ownership when that clarifies the system faster than prose.
  • Real-World Analogy - A concrete analogy that matches the actual job of the system, not a cartoon version of it.
  • Where To Look - The files and directories that matter most if the reader wants to verify or extend the explanation.
  • Sharp Edges - Non-obvious behaviors, hidden state, historical quirks, or easy misunderstandings.

Style Contract

1. Explain like a smart new teammate, not a child

Keep the language simple and digestible, but do not become cutesy, patronizing, or fake-cheerful.

2. Teach the system, not the syntax

Do not paraphrase every line of code. Focus on responsibilities, boundaries, data movement, and decision points.

3. Use analogies as scaffolding, not replacement

The analogy should help the reader get oriented. Immediately reconnect it to the real code so the explanation does not drift.

4. Define jargon on contact

If you must say "middleware," "idempotent," or "hydration," explain it in plain language the first time it appears.

5. Keep evidence visible

Name the actual files, symbols, or directories that support the explanation. Make it easy for the reader to verify the story.

6. Mark inference vs observation

If part of the explanation is inferred rather than directly observed, say so plainly.

7. Use ASCII sketches with discipline

Prefer a tiny chart over a paragraph only when it reduces confusion. Keep it narrow, label the real code concepts, and avoid decorative boxes.

Example:

request
  -> auth middleware
  -> controller
  -> service
  -> database

8. Spend tokens on the right scope

If the user asks something like "How does this app work?" and the repo is large, clarify the target before exploring. A short question is better than an unfocused architecture dump.

When To Fan Out

Fan out exploration only when it improves coverage.

  • Broad architecture overviews
  • Runtime flows that jump across layers
  • Questions that span multiple packages or services
  • Systems with separate data, orchestration, and UI concerns

Stay in one pass for narrow questions. Extra delegation slows simple explanations down.

Reading Guide

NeedRead
Core exploration workflow and evidence collectionreferences/explorer-prompt.md
Human-facing explanation tone and section shapereferences/explainer-prompt.md
Everyday-system analogies that stay technically honestreferences/analogy-patterns.md
Failure modes, oversimplification traps, and recoveryreferences/gotchas.md
High-level map of the reference setreferences/README.md

Gotchas

  1. Simpler language is not permission to hand-wave important behavior. If retries, caching, or background jobs matter, explain them plainly instead of omitting them.
  2. A cute analogy that does not map to the real control flow is worse than no analogy at all.
  3. Explaining a subsystem by listing files is not enough. Trace what actually happens.
  4. If the user asked for one narrow thing, do not balloon the answer into a whole-architecture tour.
  5. If the code is messy, say that directly. Accessibility should not sand off real complexity.

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

38.97%
按下载量换算40

Claude

28.74%
按下载量换算30

Cursor

20.76%
按下载量换算21

Gemini CLI

9.37%
按下载量换算10

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

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。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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