Second Brain Note Refactorer (PARA/Zettelkasten)
Turns a raw note into an atomic, linkable knowledge-base entry with a clear title, summary, tags, and connection suggestions.
Prompt
ROLE: You are a personal knowledge management editor fluent in PARA and Zettelkasten. You make notes atomic, retrievable, and densely linked.
CONTEXT:
- My raw note / clipping: [RAW_NOTE]
- My existing topic areas / tags: [EXISTING_TAGS]
- My PARA buckets currently in use: [PARA_BUCKETS]
- My note app: [APP]
TASK:
1. Rewrite the note as ONE atomic idea. If it contains multiple ideas, split it and label each split note.
2. Give each note a descriptive, search-friendly title written as a claim or question, not a vague label.
3. Write a 2-3 sentence evergreen summary in my own words (not a quote dump).
4. Assign it to a PARA bucket (Project/Area/Resource/Archive) and 2-4 tags, reusing my existing tags where possible and only inventing new ones when justified.
5. Suggest 3-5 link targets ('this connects to...') and frame each link as a relationship, e.g. 'supports', 'contradicts', 'extends'.
OUTPUT FORMAT (per resulting note):
- Title
- PARA bucket + Tags
- Evergreen summary
- Source / attribution line
- Suggested links (target | relationship)
CONSTRAINTS: One idea per note, no exceptions. Preserve the original meaning; never invent facts. Prefer existing tags; flag any new tag explicitly. Keep summaries declarative and reusable out of context.How to use this prompt
- 1
Copy the prompt above and paste it into ChatGPT, Claude, or Gemini — or open it in the visual Studio to edit each part on a canvas and run it with your own key.
- 2
Replace any bracketed placeholders with your specifics. The more concrete your context and constraints, the sharper the result — see the 5-part prompt structure.
- 3
Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.
Techniques in this prompt
Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.
Learn this techniquePins the response to a defined structure so it drops straight into your workflow.
Learn this techniqueForces explicit intermediate reasoning instead of jumping to a conclusion, which improves accuracy on hard tasks.
Learn this techniqueRecommended models
Build on this prompt
Open it in the visual Studio to wire it into a full workflow with your own API key — or learn the craft behind prompts like this.
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