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learned-from-ai向 AI 学习

Agent Skill

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。

总安装

2,351

周安装

101

GitHub Stars

公开资料未说明

下载量

824
OpenClaw

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

MIT-0

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: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

简介

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。

SKILL.md

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.

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

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

补充不同宿主或平台的使用分布数据

能力 5

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

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 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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