Personal Productivity & Systems5.0 · 0 ratings

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.

Role-BasedStructured-OutputStep-by-Step

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. 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. 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. 3

    Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.

Techniques in this prompt

Role-Based

Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.

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Structured Output

Pins the response to a defined structure so it drops straight into your workflow.

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Step-by-Step

Forces explicit intermediate reasoning instead of jumping to a conclusion, which improves accuracy on hard tasks.

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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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