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hyharry

Learned from AI

作者 Yi · GitHub ↗ · v0.1.0 · MIT-0
cross-platform ⚠ suspicious
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当前安装
1
版本数
在 OpenClaw 中安装
/install learned-from-ai
功能描述
Convert AI chat or drafts into structured, verified, and durable learning notes with definition, key ideas, examples, derivations, Q&A, and a cheat sheet.
使用说明 (SKILL.md)

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.
  2. 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.
  3. 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.
  4. 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/\x3Csubject>.md.
    • Always write the cheat sheet to notes/\x3Csubject>-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.
  5. 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.
  6. 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.
  7. 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.
  8. 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.

安全使用建议
This skill appears to do what it claims (turn chats into structured notes) but its runtime instructions require reading and writing a notes/ folder and spawning a specific subagent/model — none of which are declared in the manifest. Before installing, confirm: (1) where the notes/ folder lives and whether you want an automated skill to create or modify files there; (2) how the platform authorizes subagents and models (does this skill require extra API access or keys?), and whether the forced model can be changed; (3) whether 'cross-check' behavior will cause web requests that might leak sensitive content. If you proceed, consider editing the skill to (a) declare required config paths and any needed credentials, (b) remove hard-coded model requirements or document authorization, and (c) make persistent writes explicitly opt-in rather than the default.
功能分析
Type: OpenClaw Skill Name: learned-from-ai Version: 0.1.0 The 'learned-from-ai' skill is designed to transform AI chat transcripts into structured study notes and cheat sheets. The instructions in SKILL.md focus on organizational logic, such as using subagents for processing, enforcing a specific markdown structure, and preventing accidental overwrites in the 'notes/' directory. No indicators of data exfiltration, malicious execution, or harmful prompt injection were found.
能力评估
Purpose & Capability
The name and description (produce structured learning notes) align with the SKILL.md workflow (inspect source, produce main note + cheat sheet). However the instructions require searching and writing to a notes/ folder and mandate spawning a specific subagent model (openai-codex/gpt-5.4). The skill metadata declares no required config paths or credentials, so the manifest does not document the file-system and model-access behavior the skill actually needs.
Instruction Scope
The SKILL.md explicitly directs the agent to read from and write to notes/ (search existing notes, create notes/<subject>.md, notes/<subject>-cheatsheet.md). It also mandates spawning a subagent with a specific model and 'thinking' setting. These are concrete actions beyond a passive transformation: they imply filesystem access and autonomous subagent/model invocation. The instructions also say 'cross-check' facts (which may cause uncontrolled external web lookups) but do not constrain where or how verification should occur. The skill therefore instructs behavior (file I/O, network/model use) not reflected in the declared requirements.
Install Mechanism
No install spec and no code files — instruction-only skill. This is the lowest install risk: nothing is downloaded or written by an installer.
Credentials
The skill requests no environment variables or credentials in the manifest, yet it requires spawning a specific subagent model (openai-codex/gpt-5.4). Model invocation typically requires platform-managed credentials or API access; the skill does not declare any needed credentials or explain how model access is authorized. Likewise, the skill's persistent read/write to notes/ is a form of config/path requirement that is not declared. The absence of declared env/config needs is inconsistent with the instructions.
Persistence & Privilege
always is false (good). However the SKILL.md forces persistent writes to notes/ by default ('Always save outputs in notes/ unless the user explicitly asks otherwise') and requires pre-searching that folder to avoid overwrites. That grants the skill the ability to create persistent files unless the user intervenes; this persistent-write behavior is not documented in the manifest's required config paths.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install learned-from-ai
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /learned-from-ai 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v0.1.0
Initial release: convert AI chat links and drafts into reviewed notes and separate cheat sheets in notes/ using a subagent-first workflow.
元数据
Slug learned-from-ai
版本 0.1.0
许可证 MIT-0
累计安装 0
当前安装数 0
历史版本数 1
常见问题

Learned from AI 是什么?

Convert AI chat or drafts into structured, verified, and durable learning notes with definition, key ideas, examples, derivations, Q&A, and a cheat sheet. 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 84 次。

如何安装 Learned from AI?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install learned-from-ai」即可一键安装,无需额外配置。

Learned from AI 是免费的吗?

是的,Learned from AI 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。

Learned from AI 支持哪些平台?

Learned from AI 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。

谁开发了 Learned from AI?

由 Yi(@hyharry)开发并维护,当前版本 v0.1.0。

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