Agent skill
learn-capture
Extract 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.
Install this agent skill to your Project
npx add-skill https://github.com/EvolutionAPI/evo-nexus/tree/main/.claude/skills/learn-capture
SKILL.md
Learn Capture
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.
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