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learn学习

Agent Skill

learn 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

79,872

周安装

3,225

GitHub Stars

4,227

下载量

25,856
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:learn(学习)
来源仓库:https://github.com/tw93/waza
仓库路径:skills/learn
安装命令:
npx skills add https://github.com/tw93/waza --skill learn
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/tw93/waza --skill learn

简介

用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中根据关键词、任务场景或来源线索快速定位候选结果。

  • 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。
  • 适用于需要快速获取特定领域信息的研发场景。
  • 支持主流 AI 编程工具集成使用。

SKILL.md

Learn: From Raw Materials to Published Output

Prefix your first line with 🥷 inline, not as its own paragraph.

Collect, organize, translate, explain, structure. Support the user's thinking; do not replace it.

Boundary: single URL that only needs fetching belongs in /read. A single URL that needs summary or analysis can use /read as the fetch step, but the final answer should satisfy the user's requested summary or analysis. /learn is for multi-source research that produces a new structured output.

Pre-check

Check whether /read and /write skills are installed (look for their SKILL.md in the skills directories). Warn if missing, do not block:

  • /read missing -- Phase 1 fetch falls back to native WebFetch / curl; coverage on paywalled, JS-heavy, and Chinese-platform pages degrades.
  • /write missing -- Phase 5 AI-pattern stripping falls back to manual scan. Phases 1-4 are unaffected.

Choose Mode

Ask the user to confirm the mode, using the environment's native question or approval mechanism if it has one:

ModeGoalEntryExit
Deep ResearchUnderstand a domain well enough to write about itPhase 1Phase 6: publish-ready draft
Quick ReferenceBuild a working mental model fast, no article plannedPhase 2Phase 2: notes only
Write to LearnAlready have materials, force understanding through writingPhase 3Phase 6: publish-ready draft

If unsure, suggest Quick Reference.

Phase 1: Collect

Gather primary sources only: papers that introduced key ideas, official lab/product blogs, posts from builders, canonical "build it from scratch" repositories. Not summaries. Not explainers.

Three ordered steps per source -- no shortcuts, no merging:

  1. Discover -- use an installed search plugin (e.g., PipeLLM) to map the landscape, then deep-search the 2-3 most promising sub-topics. No plugin: use the environment's native web search. Output is a URL list; do not fetch content here.
  2. Fetch -- every URL goes through /read. /read already owns the proxy cascade, paywall detection, and platform routing (WeChat, Feishu, PDF, GitHub). WebFetch and raw curl silently fail on JS-heavy or paywalled sites and skip all of that. If /read is missing (Pre-check warned), fall back to native fetch and accept reduced coverage.
  3. File -- /read saves to ~/Downloads/{title}.md when called from /learn. Move each file into a sub-topic directory under the research project after the fetch returns. Move, don't refetch.

Target: 5-10 sources for a blog post, 15-20 for a deep technical survey.

Phase 2: Digest

Work through the materials. For each piece: read it fully, keep what is good, cut what is not. At the end of this phase, cut roughly half of what was collected.

For key claims, ask before including in the outline:

  • Does this idea appear in at least two different contexts from the same source?
  • Can this framework predict what the source would say about a new problem?
  • Is this specific to this source, or would any expert in the field say the same thing?

Generic wisdom is not worth distilling. Passes two or three: belongs in the outline. Passes one: background material. Passes zero: cut it.

When two sources contradict on a factual claim, note both positions and the evidence each gives. Do not silently pick one.

Phase 3: Outline

Write the outline for the article. For each section: note the source materials it draws from. If a section has no sources, either it does not belong or a source needs to be found first.

Do not start Phase 4 until the outline is solid.

Phase 4: Fill In

Work through the outline section by section. If a section is hard to write, the mental model is still weak there: return to Phase 2 for that sub-topic. The outline may change, and that is fine.

Stall signals (any one means the mental model is incomplete for this section):

  • You have rewritten the opening sentence three or more times without settling
  • The section relies on a single source and you cannot cross-check the claim
  • You need a new source that was not collected in Phase 1
  • The paragraph makes a claim you could not explain to someone out loud

When stalled: return to Phase 2 for that sub-topic, not for the whole article.

Phase 5: Refine

Pass the draft with a specific brief:

  • Remove redundant and verbose passages without changing meaning or voice
  • Flag places where the argument does not flow
  • Identify gaps: concepts used before they are explained, claims needing sources

Do not summarize sections the user has not written. Do not draft new sections from scratch. Edits only.

Then strip AI patterns from the draft. If /write is installed, invoke it. If not, do it manually: scan for filler phrases, binary contrasts, dramatic fragmentation, and overused adverbs. Cut them without changing meaning.

Phase 6: Self-review and Publish Readiness

The user reads the entire article linearly before publishing. Not with AI. Mark everything that feels off, fix it, read again. Two passes minimum.

When it reads clean from start to finish, the draft is ready for the user to publish.

After the user confirms the article is ready to publish, stop. Do not upload, post, distribute, or perform any publish action unless explicitly asked.

Gotchas

What happenedRule
Collected 30 secondary explainers instead of primary sourcesPhase 1 targets papers, official blogs, and repos by builders. Summaries are not sources.
Used WebFetch or curl on URLs while /read was installedPhase 1 fetch is not optional. /read owns the proxy cascade, paywall detection, and platform routing. Bypassing it silently loses coverage on paywalled, JS-heavy, or Chinese-platform pages.
Treated a convincing explainer as ground truthAsk: does this appear in at least two different contexts from the same source?
Phase 2 wrote summaries instead of teaching the conceptDigest means building the mental model. Summarizing is not digesting.
AI offered to upload the article to a blog or social platform after the user said it was readyStop at confirmation. Publishing is the user's action, not yours.

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

33.54%
按下载量换算8,672

Claude

27.77%
按下载量换算7,180

Cursor

21.14%
按下载量换算5,466

Gemini CLI

10.01%
按下载量换算2,588

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

可疑

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

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。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。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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