/install learned-from-ai
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
- 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.
- Use the preferred subagent settings by default:
runtime: subagent,model: openai-codex/gpt-5.4,thinking: medium. - Always save outputs in
notes/unless the user explicitly asks for a different location. - Always keep the original shared/source link in the main summary note when a link exists, so the source can be traced easily.
- Before writing, search the
notes/folder for existing related notes by subject/project so you do not overwrite durable knowledge accidentally. - For boundary cases on the same project/topic, do not rewrite the existing note by default. Create a new summary and cheat sheet instead.
- Name new boundary-case files intelligently: use either a more specific sub-subject name or the existing knowledge name plus an incremented suffix.
- Always generate a cheat sheet based on the reviewed main note.
- Do not violate the preferred structure unless the user explicitly asks for a different one.
- Strongly remove AI slop, repetition, weak filler, and hallucinated claims.
- Cross-check questionable facts, formulas, standards, and numbers when needed.
- Keep the main note and cheat sheet separate.
Preferred structure
Use this exact structure unless the user explicitly overrides it:
- Definition
- Essential ideas / engineering practice
- Worked examples and calculations
- Important theoretical derivations
- Q&A from the discussion
- Further reading / viewing
Always create a separate cheat sheet file based on the reviewed main note.
Workflow
-
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: subagentmodel: openai-codex/gpt-5.4thinking: medium
- Give the subagent the link or source material and the required output structure.
-
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.
-
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.
- Before naming or writing files, inspect existing note filenames in
-
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.
-
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.
-
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.
-
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.
-
Finalize and organize
- Ensure files are in
notes/. - Use short, practical names.
- Avoid redundant filenames like
-study-noteunless the user explicitly wants them.
- Ensure files are in
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.
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install learned-from-ai - After installation, invoke the skill by name or use
/learned-from-ai - Provide required inputs per the skill's parameter spec and get structured output
What is Learned from AI?
Convert AI chat or drafts into structured, verified, and durable learning notes with definition, key ideas, examples, derivations, Q&A, and a cheat sheet. It is an AI Agent Skill for Claude Code / OpenClaw, with 84 downloads so far.
How do I install Learned from AI?
Run "/install learned-from-ai" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is Learned from AI free?
Yes, Learned from AI is completely free, licensed under MIT-0. You can download, install and use it at no cost.
Which platforms does Learned from AI support?
Learned from AI is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created Learned from AI?
It is built and maintained by Yi (@hyharry); the current version is v0.1.0.