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Notion Research Documentation

CommunityPopular
Prat011
notion-research-documentation

Searches across your Notion workspace, synthesizes findings from multiple pages, and creates comprehensive research documentation saved as new Notion pages. Turns scattered information into structured reports with proper citations and actionable insights.

Overview

PublisherPrat011
Repositoryawesome-llm-skills
Skill namenotion-research-documentation
Stars
1.7K
Forks
303
Bundled files
17
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.

  • 17 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 Research Documentation 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-research-documentation .claude/skills/notion-research-documentation
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Notion Research Documentation 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 Research Documentation 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 Research Documentation 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.

Research & Documentation

Enables comprehensive research workflows: search for information across your Notion workspace, fetch and analyze relevant pages, synthesize findings, and create well-structured documentation.

Quick Start

When asked to research and document a topic:

  1. Search for relevant content: Use Notion:notion-search to find pages
  2. Fetch detailed information: Use Notion:notion-fetch to read full page content
  3. Synthesize findings: Analyze and combine information from multiple sources
  4. Create structured output: Use Notion:notion-create-pages to write documentation

Research Workflow

Step 1: Search for relevant information

Use Notion:notion-search with the research topic
Filter by teamspace if scope is known
Review search results to identify most relevant pages

Step 2: Fetch page content

Use Notion:notion-fetch for each relevant page URL
Collect content from all relevant sources
Note key findings, quotes, and data points

Step 3: Synthesize findings

Analyze the collected information:

  • Identify key themes and patterns
  • Connect related concepts across sources
  • Note gaps or conflicting information
  • Organize findings logically

Step 4: Create structured documentation

Use the appropriate documentation template (see reference/format-selection-guide.md) to structure output:

  • Clear title and executive summary
  • Well-organized sections with headings
  • Citations linking back to source pages
  • Actionable conclusions or next steps

Output Formats

Choose the appropriate format based on request:

Research Summary: See reference/research-summary-format.md Comprehensive Report: See reference/comprehensive-report-format.md Quick Brief: See reference/quick-brief-format.md

Best Practices

  1. Cast a wide net first: Start with broad searches, then narrow down
  2. Cite sources: Always link back to source pages using mentions
  3. Verify recency: Check page last-edited dates for current information
  4. Cross-reference: Validate findings across multiple sources
  5. Structure clearly: Use headings, bullets, and formatting for readability

Page Placement

By default, create research documents as standalone pages. If the user specifies:

  • A parent page → use page_id parent
  • A database → fetch the database first, then use appropriate data_source_id
  • A teamspace → create in that context

Advanced Features

Search filtering: See reference/advanced-search.md Citation styles: See reference/citations.md

Common Issues

"No results found": Try broader search terms or different teamspaces "Too many results": Add filters or search within specific pages "Can't access page": User may lack permissions, ask them to verify access

Examples

See examples/ for complete workflow demonstrations:

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 Research Documentation AI skill do?

Searches across your Notion workspace, synthesizes findings from multiple pages, and creates comprehensive research documentation saved as new Notion pages. Turns scattered information into structured reports with proper citations and actionable insights.

Why use Notion Research Documentation on TypingMind?

Because you install it once and use it with any model. Notion Research Documentation 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 Research Documentation in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Prat011/awesome-llm-skills/tree/master/notion-research-documentation. 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 Research Documentation?

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 Research Documentation?

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

Is the Notion Research Documentation 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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