Learn Capture
SkillDev toolsExtract 1–5 atomic facts from pasted text and save them as spaced-repetition cards in workspace/learning/facts/ with SM-2 frontmatter. Use when the user says "capture this", "save fact", "learn this", "memorize this", or pastes content they want to retain.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the Learn Capture skill
What this skill tells your AI
The instructions your AI receives, as published by evolution-foundation/evo-nexus in .claude/skills/learn-capture/SKILL.md and read by ahel’s review.
Extracts atomic facts from user-provided text and saves them as SM-2 flashcard files in workspace/learning/facts/.
Trigger
User pastes text (article, note, transcript excerpt) and wants to retain key facts for later review. Does NOT fetch URLs automatically. If the user provides a URL, ask them to paste the text content instead (v0 policy — no network dependency).
Workflow
Step 1 — Receive input
Ask the user (if not already provided):
- The text to capture (paste directly)
- Optional: deck name (default: infer from content or use
general) - Optional: source URL or description (default:
manual)
If the user provides a URL only, respond:
"Por favor, cole o texto do artigo diretamente aqui. A skill não faz fetch automático de URLs para evitar problemas de paywall e dependência de rede."
Step 2 — Extract facts
Read the pasted text carefully. Extract 1 to 5 atomic facts — each fact must be:
- Atomic: one idea per fact, not a summary paragraph
- Memorable: something worth reviewing in 1–30 days
- Retrievable: can be turned into a self-test question
Do NOT extract:
- Opinions without evidence
- Context that depends on reading the full article
- Facts already trivially known (e.g., "Python is a programming language")
Step 3 — Generate file content for each fact
For each fact, produce content in this exact format:
---
id: {YYYY-MM-DD}-{slug}
source: {source_url_or_"manual"}
deck: {deck_name}
created: {YYYY-MM-DD}
next_review: {YYYY-MM-DD+1 day}
interval: 1
ease: 2.5
reps: 0
lapses: 0
---
**Fact:** {The atomic fact stated directly, in pt-BR.}
**Why it matters:** {One sentence on why Davidson should remember this, in pt-BR.}
**Retrieval Q:** {A question whose answer is the fact above, in pt-BR.}
Slug rules:
- Kebab-case of the main topic of the fact
- Max 40 characters
- No accents, special characters, or spaces
- Example:
claude-skills-sao-arquivos-markdown
If slug collision (same date + same slug): append -2, -3, etc.
Dates (use today's actual date):
created: today in YYYY-MM-DDnext_review: tomorrow in YYYY-MM-DD (today + 1 day)
Language: fact content (Fact, Why it matters, Retrieval Q) must be in pt-BR by default (workspace.language = pt-BR), regardless of the source language.
Step 4 — Save files
For each fact:
- Create
workspace/learning/facts/directory if it does not exist - Write the file to
workspace/learning/facts/{YYYY-MM-DD}-{slug}.md - Confirm success with the file path
Step 5 — Report
After saving all files, output a summary:
✅ {N} fato(s) capturado(s) no deck "{deck}":
- workspace/learning/facts/{filename1}.md → {first 5 words of Retrieval Q}...
- workspace/learning/facts/{filename2}.md → ...
Constraints
- Max 5 facts per capture session. If the text warrants more, tell the user to split it into multiple runs.
- Do NOT create or modify any file outside
workspace/learning/facts/. - Do NOT touch
review-log.jsonlor any existing fact file. - Do NOT fetch URLs — ask user to paste text.
- All 9 frontmatter fields must be present:
id,source,deck,created,next_review,interval,ease,reps,lapses. interval=1,ease=2.5,reps=0,lapses=0are always the initial values.
Signals
- GitHub stars
- 533
- Forks
- 177
- Last commit
- May 2026
Advanced
- Catalog kind
- skill
- Gateway key
learn-capture- Source
- github.com/evolution-foundation/evo-nexus