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Agent Skill · 研究检索

learned-from-ai(向 AI 学习)

learned-from-ai 用于记录任务执行中的错误、用户纠正、经验和能力缺口,适合在 OpenClaw 中希望让 Agent 持续沉淀问题、修正和最佳实践时使用。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。

learned-from-ai 是研究检索类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为执行命令;许可证为 MIT-0,第三方扫描有提醒项,按安装量在研究检索类的 73,675 个 Skill 里排第 33,477。

总安装 2,351周安装 101GitHub Stars 公开资料未说明下载 824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
MIT-0
版本
0.1.0

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:learned-from-ai(向 AI 学习)
来源仓库:https://github.com/hyharry/learned-from-ai
安装命令:
openclaw skills install learned-from-ai
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 OpenClaw 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。

ClawHubOpenClaw
openclaw skills install learned-from-ai

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。

将 AI 聊天或草稿转换为结构化、经过验证且持久的学习笔记,其中包含定义、关键思想、示例、推导、问答和备忘单。

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 OpenClaw;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 openclaw skills install learned-from-ai;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

learned-from-ai 怎么安装?

在终端执行 openclaw skills install learned-from-ai。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

learned-from-ai 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。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。

learned-from-ai 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 openclaw skills install learned-from-ai 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。

learned-from-ai 是免费的吗?

来源标注的许可证是 MIT-0。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

learned-from-ai 用的人多吗?

公开数据是累计安装 2,351 次,近一周安装 101 次,下载 824 次。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。

learned-from-ai 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

值得选的地方和要先想清楚的地方

值得选的地方

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。

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。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 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 OpenClaw,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
learned-from-ai

向 AI 学习 · 本页

2,351—执行命令MIT-0有提醒

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

name
learned-from-ai
description
Turn chat interactions with AI into durable learning materials for humans. Use when the user provides a chat/share link, pasted AI conversation, or rough AI-generated draft and wants it converted into structured, long-lived notes for review and memory. Especially use when the user wants the fixed structure: (1) definition, (2) essential ideas or engineering practice, (3) worked examples and calculations, (4) important derivations, (5) Q&A, (6) further reading/viewing, plus a separate cheat sheet. For tasks under this skill, always use a subagent with model openai-codex/gpt-5.4 and thinking medium by default so the main session stays responsive, unless the user explicitly asks otherwise. Always save outputs in notes/ unless the user explicitly asks otherwise.

learned-from-ai

Turn transient AI chat output into structured, reviewed, long-lived learning material that is easy for a human to study, remember, and revisit.

Non-negotiable rules

  1. Always handle tasks under this skill through a subagent by default so the main session does not get blocked, unless the user explicitly asks otherwise.
  2. Use the preferred subagent settings by default: runtime: subagent, model: openai-codex/gpt-5.4, thinking: medium.
  3. Always save outputs in notes/ unless the user explicitly asks for a different location.
  4. Always keep the original shared/source link in the main summary note when a link exists, so the source can be traced easily.
  5. Before writing, search the notes/ folder for existing related notes by subject/project so you do not overwrite durable knowledge accidentally.
  6. For boundary cases on the same project/topic, do not rewrite the existing note by default. Create a new summary and cheat sheet instead.
  7. Name new boundary-case files intelligently: use either a more specific sub-subject name or the existing knowledge name plus an incremented suffix.
  8. Always generate a cheat sheet based on the reviewed main note.
  9. Do not violate the preferred structure unless the user explicitly asks for a different one.
  10. Strongly remove AI slop, repetition, weak filler, and hallucinated claims.
  11. Cross-check questionable facts, formulas, standards, and numbers when needed.
  12. Keep the main note and cheat sheet separate.

Preferred structure

Use this exact structure unless the user explicitly overrides it:

  1. Definition
  2. Essential ideas / engineering practice
  3. Worked examples and calculations
  4. Important theoretical derivations
  5. Q&A from the discussion
  6. Further reading / viewing

Always create a separate cheat sheet file based on the reviewed main note.


Workflow

  1. Start by spawning the working subagent

- For tasks under this skill, start with a subagent by default so the main session stays responsive. - When this skill is activated with a slash command and the user appends a chat/share link, immediately spawn the subagent. - Use the default settings unless the user explicitly overrides them: - runtime: subagent - model: openai-codex/gpt-5.4 - thinking: medium - Give the subagent the link or source material and the required output structure.

  1. Inspect the source

- Read the shared link, pasted chat, file, or notes. - Extract the real technical content. - Ignore UI noise, fluff, and repeated AI phrasing.

  1. Pre-search the knowledge base in notes/

- Before naming or writing files, inspect existing note filenames in notes/ for the same subject, project, or nearby topic. - Use this step to avoid overwriting durable notes. - If the new source is clearly a new subtopic or a separate chat on the same project, plan a new note instead of rewriting the old one.

  1. Identify the subject and output files

- Pick a short subject-based filename. - By default, write a new note rather than overwriting an existing one when the source is a new chat, new link, or new subtopic. - Write the main note to notes/<subject>.md. - Always write the cheat sheet to notes/<subject>-cheatsheet.md. - If needed, use either: - a more specific sub-subject name, or - the existing knowledge name plus an incremented suffix. - If the source came from a shared/public link, record that original link near the top of the main note so the summary can be traced back to its source easily.

  1. Review and verify before polishing

- During review, use strong reasoning and factual discipline. - Catch factual errors. - Remove hallucinations. - Strip AI slop. - Cross-check formulas, standards, fit values, and calculations when needed. - Distinguish exact statements from approximations. - Preserve useful approximations, but label them honestly as approximations, first-pass checks, or worst-case bounds.

  1. Write the main note

- Follow the preferred structure exactly. - Do not reorder or silently replace it with a different teaching flow. - Make definitions crisp, logic coherent, and examples numerically consistent. - The preferred structure must not be violated. - Do not overwrite an existing durable note unless the user explicitly asks for revision of that specific file.

  1. Write the cheat sheet

- Base it on the reviewed main note. - Keep it separate from the main note. - Distill, do not duplicate. - The preferred main-note structure must still remain intact and must not be violated.

  1. Finalize and organize

- Ensure files are in notes/. - Use short, practical names. - Avoid redundant filenames like -study-note unless the user explicitly wants them.


Writing standards

Keep

  • precise definitions
  • practical engineering or domain logic
  • worked numerical examples
  • short derivations that reveal the principle
  • explicit assumptions and limitations
  • Q&A clearly separated from exposition

Remove

  • AI filler
  • repetitive hype
  • vague certainty
  • unsupported claims
  • long padding that does not improve learning

Prefer

  • short sections
  • bullets over bloated prose
  • equations when they clarify reasoning
  • ASCII sketches when a simple drawing helps
  • concise filenames

Review checklist

Before finishing, check:

  • Are the files in notes/?
  • Does the main note keep the original shared/source link when one exists?
  • Does the main note follow the preferred structure exactly?
  • Is the cheat sheet separate and genuinely distilled?
  • Were suspicious claims cross-checked?
  • Were hallucinations and AI slop removed?
  • Are examples and calculations internally consistent?
  • Are approximations labeled clearly?
  • Are filenames short and subject-based?

Example file layout

notes/
  gdt.md
  gdt-cheatsheet.md

Scope

This skill is for turning AI chat interactions into durable human learning materials.

It is not mainly for:

  • writing full textbooks from scratch
  • doing exhaustive literature reviews
  • dumping raw chat transcripts into files without review

If the source is rough, correct it. If it is verbose, compress it. If it is uncertain, verify it.

平台分布

OpenClaw

90.07%
按下载量换算742

安全审计

VirusTotal

通过

ClawScan

可疑

Static analysis

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 openclaw skills install learned-from-ai 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。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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