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Notion Knowledge Capture

JetBrainsOfficialCreated by OpenAIDistributed by JetBrains

  • Skill
  • Tool integration
  • Automation and integrations
  • MIT
  • No issues found
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From the author

Capture conversations and decisions into structured Notion pages; use when turning chats/notes into wiki entries, how-tos, decisions, or FAQs with proper linking.
— 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 transforming ephemeral chat logs and notes into durable, structured knowledge within Notion. It guides the user through defining the content type, identifying the appropriate database, extracting key information, and creating or updating pages with proper metadata and cross-linking. By leveraging predefined templates and database schemas, it ensures that team wikis, decision logs, and how-to guides remain consistent and discoverable.

What it does

  • Converts unstructured conversation data into structured Notion pages.
  • Supports multiple content types including decisions, FAQs, how-tos, and wiki entries.
  • Facilitates the creation and updating of Notion pages using specific database schemas.
  • Ensures proper linking and relationship management between new and existing documentation.
  • Provides a clear setup procedure for Notion integration via MCP.

When to use it

  • When you need to document a decision made during a team chat.
  • When converting a series of troubleshooting steps into a formal how-to guide.
  • When building a team wiki from recurring questions or project notes.
  • When you need to update existing documentation with new context or status changes.

Advantages

  • Reduces knowledge loss by formalizing informal discussions.
  • Maintains consistency across documentation using standardized templates.
  • Improves discoverability through structured tagging and relational linking.
  • Integrates directly with Notion databases for seamless knowledge management.

Usage examples

“Capture our recent decision about the API migration into the decision log.”
The agent will extract the rationale and outcome from the chat, locate the decision log database, and create a structured page with the appropriate properties.
“Turn this troubleshooting conversation into a how-to guide for the team wiki.”
The agent will structure the steps, prerequisites, and edge cases into a template-based how-to page and link it to relevant project documentation.

Install

  1. Download the skill as a ZIP:

    https://mp-staging.autogineer.com/agent-skills/jetbrains/notion-knowledge-capture/download

  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip notion-knowledge-capture.zip -d .agents/skills

Documentation by OpenAI

Knowledge Capture

Convert conversations and notes into structured, linkable Notion pages for easy reuse.

Quick start

  1. Clarify what to capture (decision, how-to, FAQ, learning, documentation) and target audience.

  2. Identify the right database/template in reference/ (team wiki, how-to, FAQ, decision log, learning, documentation).

  3. Pull any prior context from Notion with Notion:notion-search → Notion:notion-fetch (existing pages to update/link).

  4. Draft the page with Notion:notion-create-pages using the database’s schema; include summary, context, source links, and tags/owners.

  5. Link from hub pages and related records; update status/owners with Notion:notion-update-page as the source evolves.

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) Define the capture

  • Ask purpose, audience, freshness, and whether this is new or an update.

  • Determine content type: decision, how-to, FAQ, concept/wiki entry, learning/note, documentation page.

2) Locate destination

  • Pick the correct database using reference/*-database.md guides; confirm required properties (title, tags, owner, status, date, relations).

  • If multiple candidate databases, ask the user which to use; otherwise, create in the primary wiki/documentation DB.

3) Extract and structure

  • Extract facts, decisions, actions, and rationale from the conversation.

  • For decisions, record alternatives, rationale, and outcomes.

  • For how-tos/docs, capture steps, pre-reqs, links to assets/code, and edge cases.

  • For FAQs, phrase as Q&A with concise answers and links to deeper docs.

4) Create/update in Notion

  • Use Notion:notion-create-pages with the correct data_source_id; set properties (title, tags, owner, status, dates, relations).

  • Use templates in reference/ to structure content (section headers, checklists).

  • If updating an existing page, fetch then edit via Notion:notion-update-page.

5) Link and surface

  • Add relations/backlinks to hub pages, related specs/docs, and teams.

  • Add a short summary/changelog for future readers.

  • If follow-up tasks exist, create tasks in the relevant database and link them.

References and examples

  • reference/ — database schemas and templates (e.g., team-wiki-database.md, how-to-guide-database.md, faq-database.md, decision-log-database.md, documentation-database.md, learning-database.md, database-best-practices.md).

  • examples/ — capture patterns in practice (e.g., decision-capture.md, how-to-guide.md, conversation-to-faq.md).

Files

evaluations/README.md

Knowledge Capture Skill Evaluations

Evaluation scenarios for testing the Knowledge Capture skill across different Codex models.

Purpose

These evaluations ensure the Knowledge Capture skill:

  • Correctly identifies content types (how-to guides, FAQs, decision records, wikis)

  • Extracts relevant information from conversations

  • Structures content appropriately for each type

  • Searches and places content in the right Notion location

  • Works consistently across Haiku, Sonnet, and Opus

Evaluation Files

conversation-to-wiki.json

Tests capturing conversation content as a how-to guide for the team wiki.

Scenario: Save deployment discussion to wiki
Key Behaviors:

  • Extracts steps, gotchas, and best practices from conversation

  • Identifies content as How-To Guide

  • Structures with proper sections (Overview, Prerequisites, Steps, Troubleshooting)

  • Searches for team wiki location

  • Preserves technical details (commands, configs)

decision-record.json

Tests capturing architectural or technical decisions with full context.

Scenario: Document database migration decision
Key Behaviors:

  • Extracts decision context, alternatives, and rationale

  • Follows decision record structure (Context, Decision, Alternatives, Consequences)

  • Captures both selected and rejected options with reasoning

  • Places in decision log or ADR database

  • Links to related technical documentation

Running Evaluations

  1. Enable the knowledge-capture skill

  2. Submit the query from the evaluation file

  3. Provide conversation context as specified

  4. Verify all expected behaviors are met

  5. Check success criteria for quality

  6. Test with Haiku, Sonnet, and Opus

Expected Skill Behaviors

Knowledge Capture evaluations should verify:

Content Extraction

  • Accurately captures key points from conversation context

  • Preserves specific technical details, not generic placeholders

  • Maintains context and nuance from discussion

Content Type Selection

  • Correctly identifies appropriate content type (how-to, FAQ, decision record, wiki page)

  • Uses matching structure from reference documentation

  • Applies proper Notion markdown formatting

Notion Integration

  • Searches for appropriate target location (wiki, decision log, etc.)

  • Creates well-structured pages with clear titles

  • Uses proper parent placement

  • Includes discoverable titles and metadata

Quality Standards

  • Content is actionable and future-reference ready

  • Technical accuracy is preserved

  • Organization aids discoverability

  • Formatting enhances readability

Creating New Evaluations

When adding Knowledge Capture evaluations:

  1. Use realistic conversation content - Include actual technical details, decisions, or processes

  2. Test different content types - How-to guides, FAQs, decision records, meeting notes, learnings

  3. Vary complexity - Simple captures vs. complex technical discussions

  4. Test discovery - Finding the right wiki section or database

  5. Include edge cases - Unclear content types, minimal context, overlapping categories

Example Success Criteria

Good (specific, testable):

  • "Structures content using How-To format with numbered steps"

  • "Preserves exact bash commands from conversation"

  • "Creates page with title format 'How to [Action]'"

  • "Places in Engineering Wiki → Deployment section"

Bad (vague, untestable):

  • "Creates good documentation"

  • "Uses appropriate structure"

  • "Saves to the right place"

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-knowledge-capture · commit 3f53782
Licence
MIT License (redistribution allowed)
  • ATTRIBUTION.md (written by AutoGineer)
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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-knowledge-capture.zip