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Notion Research Documentation

JetBrainsOfficialCreated by OpenAIDistributed by JetBrains

  • Skill
  • Content generator
  • Automation and integrations
  • MIT
  • No issues found

From the author

Research across Notion and synthesize into structured documentation; use when gathering info from multiple Notion sources to produce briefs, comparisons, or reports with citations.
— the description by OpenAI

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill provides a systematic approach to gathering, analyzing, and documenting information stored within Notion. It guides the agent through searching for relevant pages, fetching content, selecting an appropriate output format, and drafting structured documents such as briefs, summaries, or reports. The workflow emphasizes accuracy by requiring inline citations and direct links back to the original source material, ensuring that all synthesized findings are verifiable and well-organized.

What it does

  • Searches and retrieves relevant content across Notion workspaces.
  • Synthesizes raw information into structured formats like briefs or reports.
  • Provides templates for various documentation needs including comparisons and deep dives.
  • Ensures traceability by linking findings to their original source pages.
  • Supports the creation of actionable tasks or checklists within the generated documents.
  • Includes a setup procedure for connecting the Notion MCP client.

When to use it

  • When you need to consolidate information from multiple Notion pages into a single report.
  • When performing research that requires verifiable citations from internal documentation.
  • When creating executive summaries, competitor analyses, or technical investigations.
  • When you need to present findings in a standardized, professional format.

Advantages

  • Reduces manual effort in aggregating scattered information.
  • Improves documentation quality through standardized templates.
  • Increases transparency by maintaining clear links to source data.
  • Facilitates better decision-making by organizing findings into clear, actionable insights.

Usage examples

“Research our recent meeting notes in Notion and create a summary of all action items.”
The agent searches for relevant meeting pages, extracts action items, and compiles them into a structured summary document with links to the original meeting notes.
“Compare the two project proposals I have in Notion and create a comparison report.”
The agent fetches both proposal documents, identifies key tradeoffs, and generates a comparison report using the designated template.

Install

  1. Download the skill as a ZIP:

    https://autogineer.com/agent-skills/jetbrains/notion-research-documentation/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip notion-research-documentation.zip -d .agents/skills

Documentation by OpenAI

Research & Documentation

Pull relevant Notion pages, synthesize findings, and publish clear briefs or reports (with citations and links to sources).

Quick start

  1. Find sources with Notion:notion-search using targeted queries; confirm scope with the user.

  2. Fetch pages via Notion:notion-fetch; note key sections and capture citations (reference/citations.md).

  3. Choose output format (brief, summary, comparison, comprehensive report) using reference/format-selection-guide.md.

  4. Draft in Notion with Notion:notion-create-pages using the matching template (quick, summary, comparison, comprehensive).

  5. Link sources and add a references/citations section; update as new info arrives with Notion:notion-update-page.

Workflow

0) If any MCP call fails because Notion MCP is not connected, pause and set it up:
  1. Add the Notion MCP:

    • codex mcp add notion --url https://mcp.notion.com/mcp

  2. Enable remote MCP client:

    • Set [features].rmcp_client = true in config.toml or run codex --enable rmcp_client

  3. Log in with OAuth:

    • codex mcp login notion

After successful login, the user will have to restart codex. You should finish your answer and tell them so when they try again they can continue with Step 1.

1) Gather sources
  • Search first (Notion:notion-search); refine queries, and ask the user to confirm if multiple results appear.

  • Fetch relevant pages (Notion:notion-fetch), skim for facts, metrics, claims, constraints, and dates.

  • Track each source URL/ID for later citation; prefer direct quotes for critical facts.

2) Select the format
  • Quick readout → quick brief.

  • Single-topic dive → research summary.

  • Option tradeoffs → comparison.

  • Deep dive / exec-ready → comprehensive report.

  • See reference/format-selection-guide.md for when to pick each.

3) Synthesize
  • Outline before writing; group findings by themes/questions.

  • Note evidence with source IDs; flag gaps or contradictions.

  • Keep user goal in view (decision, summary, plan, recommendation).

4) Create the doc
  • Pick the matching template in reference/ (brief, summary, comparison, comprehensive) and adapt it.

  • Create the page with Notion:notion-create-pages; include title, summary, key findings, supporting evidence, and recommendations/next steps when relevant.

  • Add citations inline and a references section; link back to source pages.

5) Finalize & handoff
  • Add highlights, risks, and open questions.

  • If the user needs follow-ups, create tasks or a checklist in the page; link any task database entries if applicable.

  • Share a short changelog or status using Notion:notion-update-page when updating.

References and examples

  • reference/ — search tactics, format selection, templates, and citation rules (e.g., advanced-search.md, format-selection-guide.md, research-summary-template.md, comparison-template.md, citations.md).

  • examples/ — end-to-end walkthroughs (e.g., competitor-analysis.md, technical-investigation.md, market-research.md, trip-planning.md).

Files

reference/advanced-search.md

Advanced Search Techniques

Search Filtering

By Date Range

Use created_date_range to find recent content:

filters: {
  created_date_range: {
    start_date: "2024-01-01",
    end_date: "2025-01-01"
  }
}

When to use:

  • Finding recent updates on a topic

  • Focusing on current information

  • Excluding outdated content

By Creator

Use created_by_user_ids to find content from specific people:

filters: {
  created_by_user_ids: ["user-id-1", "user-id-2"]
}

When to use:

  • Research from subject matter experts

  • Team-specific information

  • Attribution tracking

Combined Filters

Stack filters for precision:

filters: {
  created_date_range: {
    start_date: "2024-10-01"
  },
  created_by_user_ids: ["expert-user-id"]
}

Scoped Searches

Teamspace Scoping

Restrict search to specific teamspace:

teamspace_id: "teamspace-uuid"

When to use:

  • Project-specific research

  • Department-focused information

  • Reducing noise from irrelevant results

Page Scoping

Search within a specific page and its subpages:

page_url: "https://notion.so/workspace/Page-Title-uuid"

When to use:

  • Research within a project hierarchy

  • Documentation updates

  • Focused investigation

Database Scoping

Search within a database's content:

data_source_url: "collection://data-source-uuid"

When to use:

  • Task/project database research

  • Structured data investigation

  • Finding specific entries

Search Strategies

Broad to Narrow
  1. Start with general search term

  2. Review results for relevant teamspaces/pages

  3. Re-search with scope filters

  4. Fetch detailed content from top results

Example:

Search 1: query="API integration" → 50 results across workspace
Search 2: query="API integration", teamspace_id="engineering" → 12 results
Fetch: Top 3-5 most relevant pages
Multi-Query Approach

Run parallel searches with related terms:

Query 1: "API integration"
Query 2: "API authentication"
Query 3: "API documentation"

Combine results to build comprehensive picture.

Temporal Research

Search across time periods to track evolution:

Search 1: created_date_range 2023 → Historical context
Search 2: created_date_range 2024 → Recent developments
Search 3: created_date_range 2025 → Current state

Result Processing

Identifying Relevant Results

Look for:

  • High semantic match: Result summary closely matches query intent

  • Recent updates: Last-edited date is recent

  • Authoritative sources: Created by known experts or in official locations

  • Comprehensive content: Result summary suggests detailed information

Prioritizing Fetches

Fetch pages in order of relevance:

  1. Primary sources: Direct documentation, official pages

  2. Recent updates: Newly edited content

  3. Related context: Supporting information

  4. Historical reference: Background and context

Don't fetch everything - be selective based on research needs.

Handling Too Many Results

If search returns 20+ results:

  1. Add filters: Narrow by date, creator, or teamspace

  2. Refine query: Use more specific terms

  3. Use page scoping: Search within relevant parent page

  4. Sample strategically: Fetch diverse results (recent, popular, authoritative)

Handling Too Few Results

If search returns < 3 results:

  1. Broaden query: Use more general terms

  2. Remove filters: Search full workspace

  3. Try synonyms: Alternative terminology

  4. Search in related areas: Adjacent teamspaces or pages

Search Quality

Effective Search Queries

Good queries (specific, semantic):

  • "Q4 product roadmap"

  • "authentication implementation guide"

  • "customer feedback themes"

Weak queries (too vague):

  • "roadmap"

  • "guide"

  • "feedback"

Over-specific queries (too narrow):

  • "Q4 2024 product roadmap for mobile app version 3.2 feature X"

User Context

Always use available user context:

  • Query should match their terminology

  • Scope to their relevant teamspaces

  • Consider their role/department

  • Reference their recent pages

Connected Sources

Notion Integrations

Search extends beyond Notion pages to:

  • Slack messages (if connected)

  • Google Drive documents (if connected)

  • GitHub issues/PRs (if connected)

  • Jira tickets (if connected)

Be aware results may come from these sources.

Source Attribution

When citing results from connected sources:

  • Note the source type in documentation

  • Use appropriate mention format

  • Verify user has access to the source system

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Security

No issues found by our static scan· No issues found

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Created by
OpenAI
Publisher
JetBrains (Official)
Distributed by
JetBrains
Source
JetBrains/skills · notion-research-documentation · commit 3f53782
Licence
MIT License (redistribution allowed)
  • ATTRIBUTION.md (written by AutoGineer)
Synced

AutoGineer is not affiliated with or endorsed by OpenAI or JetBrains.

Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download notion-research-documentation.zip