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Notion Knowledge Capture

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Prat011
notion-knowledge-capture

Transforms conversations and discussions into structured documentation pages in Notion. Captures insights, decisions, and knowledge from chat context, formats appropriately, and saves to wikis or databases with proper organization and linking for easy discovery.

Overview

PublisherPrat011
Repositoryawesome-llm-skills
Skill namenotion-knowledge-capture
Stars
1.7K
Forks
303
Bundled files
12
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 12 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by Prat011 on GitHub. Read the source before you install it.

Installation

Install the Notion Knowledge Capture AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/Prat011/awesome-llm-skills.git /tmp/awesome-llm-skills
mkdir -p .claude/skills
cp -r /tmp/awesome-llm-skills/notion-knowledge-capture .claude/skills/notion-knowledge-capture
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Notion Knowledge Capture in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Notion Knowledge Capture on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Notion Knowledge Capture is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Knowledge Capture

Transforms conversations, discussions, and insights into structured documentation in your Notion workspace. Captures knowledge from chat context, formats it appropriately, and saves it to the right location with proper organization and linking.

Quick Start

When asked to save information to Notion:

  1. Extract content: Identify key information from conversation context
  2. Structure information: Organize into appropriate documentation format
  3. Determine location: Use Notion:notion-search to find appropriate wiki page/database
  4. Create page: Use Notion:notion-create-pages to save content
  5. Make discoverable: Link from relevant hub pages, add to databases, or update wiki navigation so others can find it

Knowledge Capture Workflow

Step 1: Identify content to capture

From conversation context, extract:
- Key concepts and definitions
- Decisions made and rationale
- How-to information and procedures
- Important insights or learnings
- Q&A pairs
- Examples and use cases

Step 2: Determine content type

Classify the knowledge:
- Concept/Definition
- How-to Guide
- Decision Record
- FAQ Entry
- Meeting Summary
- Learning/Post-mortem
- Reference Documentation

Step 3: Structure the content

Format appropriately based on content type:
- Use templates for consistency
- Add clear headings and sections
- Include examples where helpful
- Add relevant metadata
- Link to related pages

Step 4: Determine destination

Where to save:
- Wiki page (general knowledge base)
- Specific project page (project-specific knowledge)
- Documentation database (structured docs)
- FAQ database (questions and answers)
- Decision log (architecture/product decisions)
- Team wiki (team-specific knowledge)

Step 5: Create the page

Use Notion:notion-create-pages:
- Set appropriate title
- Use structured content from template
- Set properties if in database
- Add tags/categories
- Link to related pages

Step 6: Make content discoverable

Link the new page so others can find it:

1. Update hub/index pages:
   - Add link to wiki table of contents page
   - Add link from relevant project page
   - Add link from category/topic page (e.g., "Engineering Docs")
   
2. If page is in a database:
   - Set appropriate tags/categories
   - Set status (e.g., "Published")
   - Add to relevant views
   
3. Optionally update parent page:
   - If saved under a project, add to project's "Documentation" section
   - If in team wiki, ensure it's linked from team homepage

Example:
Notion:notion-update-page
page_id: "team-wiki-homepage-id"
command: "insert_content_after"
selection_with_ellipsis: "## How-To Guides..."
new_str: "- <mention-page url='...'>How to Deploy to Production</mention-page>"

This step ensures the knowledge doesn't become "orphaned" - it's properly connected to your workspace's navigation structure.

Content Types

Choose appropriate structure based on content:

Concept: Overview → Definition → Characteristics → Examples → Use Cases → Related How-To: Overview → Prerequisites → Steps (numbered) → Verification → Troubleshooting → Related Decision: Context → Decision → Rationale → Options Considered → Consequences → Implementation FAQ: Short Answer → Detailed Explanation → Examples → When to Use → Related Questions Learning: What Happened → What Went Well → What Didn't → Root Causes → Learnings → Actions

Destination Patterns

General Wiki: Standalone page → add to index → tag → link from related pages

Project Wiki: Child of project page → link from project overview → tag with project name

Documentation Database: Use properties (Title, Type, Category, Tags, Last Updated, Owner)

Decision Log Database: Use properties (Decision, Date, Status, Domain, Deciders, Impact)

FAQ Database: Use properties (Question, Category, Tags, Last Reviewed, Useful Count)

See reference/database-best-practices.md for database selection guide and individual schema files.

Content Extraction from Conversations

Chat Discussion: Key points, conclusions, resources, action items, Q&A

Problem-Solving: Problem statement, approaches tried, solution, why it worked, future considerations

Knowledge Sharing: Concept explained, examples, best practices, common pitfalls, resources

Decision Discussion: Question, options, trade-offs, decision, rationale, next steps

Formatting Best Practices

Structure: Use # (title), ## (sections), ### (subsections) consistently

Writing: Start with overview, use bullets, keep paragraphs short, add examples

Linking: Link related pages, mention people, reference resources, create bidirectional links

Metadata: Include date, author, tags, status

Searchability: Clear titles, natural keywords, common search tags, image alt-text

Indexing and Organization

Wiki Index: Organize by sections (Getting Started, How-To Guides, Reference, FAQs, Decisions) with page links

Category Pages: Create landing pages with overview, doc links, and recent updates

Tagging Strategy: Use consistent tags for technology/tools, topics, audience, and status

Update Management

Create New: Content is substantive (>2 paragraphs), will be referenced multiple times, part of knowledge base, needs independent discovery

Update Existing: Adding to existing topic, correcting info, expanding concept, updating for changes

Versioning: Add update history section for significant changes (date, author, what changed, why)

Best Practices

  1. Capture promptly: Document while context is fresh
  2. Structure consistently: Use templates for similar content
  3. Link extensively: Connect related knowledge
  4. Write for discovery: Use searchable titles and tags
  5. Include context: Why this matters, when to use
  6. Add examples: Concrete examples aid understanding
  7. Maintain: Review and update periodically
  8. Get feedback: Ask if documentation is helpful

Advanced Features

Documentation databases: See reference/database-best-practices.md for database schema patterns.

Common Issues

"Not sure where to save": Default to general wiki, can move later "Content is fragmentary": Group related fragments into cohesive doc "Already exists": Search first, update existing if appropriate "Too informal": Clean up language while preserving insights

Examples

See examples/ for complete workflows:

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Notion Knowledge Capture AI skill do?

Transforms conversations and discussions into structured documentation pages in Notion. Captures insights, decisions, and knowledge from chat context, formats appropriately, and saves to wikis or databases with proper organization and linking for easy discovery.

Why use Notion Knowledge Capture on TypingMind?

Because you install it once and use it with any model. Notion Knowledge Capture is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Notion Knowledge Capture in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Prat011/awesome-llm-skills/tree/master/notion-knowledge-capture. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Notion Knowledge Capture?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Notion Knowledge Capture?

As many as you like. As long as a model supports skills, you can use Notion Knowledge Capture with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Notion Knowledge Capture AI skill free?

It is published on GitHub by Prat011. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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