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Notion Knowledge Capture

OpenAIOfficialCreated by Notion Labs, IncDistributed by OpenAI

  • Skill
  • Workflow
  • 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 Notion Labs, Inc

AutoGineer summary

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

This skill provides a systematic approach for capturing information from ongoing conversations and turning it into structured, linkable content within Notion. It guides the user through defining the content type, identifying the appropriate database, extracting key information, and creating or updating pages using standardized templates. The workflow ensures that knowledge is not lost in chat logs but is instead integrated into a team's wiki, decision logs, or documentation systems.

What it does

  • Defines the purpose and audience for captured information.
  • Identifies the correct Notion database and schema for the content type.
  • Extracts key facts, decisions, and rationale from conversations.
  • Creates or updates structured pages using predefined templates.
  • Links new content to existing hub pages and related records.
  • Handles initial setup and authentication for the Notion integration.

When to use it

  • When you need to turn a brainstorming session into a formal decision log.
  • When converting technical discussions into a reusable how-to guide.
  • When summarizing team chats into FAQ entries or wiki pages.
  • When documenting learnings or project notes for future reference.

Advantages

  • Ensures consistency across documentation through standardized templates.
  • Reduces information fragmentation by linking new content to existing hubs.
  • Provides a clear, repeatable process for knowledge management.
  • Facilitates better team collaboration by making information discoverable.

Usage examples

“Can you turn our recent discussion about the API migration into a decision log entry?”
The agent will extract the alternatives, rationale, and final decision from the chat and create a structured page in your Notion decision log database.
“Create a how-to guide for setting up the local environment based on our chat.”
The agent will identify the how-to database, structure the steps and prerequisites, and create a new page in Notion with the appropriate tags and links.

Install

  1. Download the skill as a ZIP:

    https://mp-staging.autogineer.com/agent-skills/openai/notion-knowledge-capture-lh2r/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 Notion Labs, Inc

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

Written for: OpenAI Codex

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
Notion Labs, Inc
Publisher
OpenAI (Official)
Distributed by
OpenAI
Source
openai/skills · skills/.curated/notion-knowledge-capture · commit 49f948f
Licence
MIT License (redistribution allowed)
  • ATTRIBUTION.md (written by AutoGineer)
Synced

AutoGineer is not affiliated with or endorsed by Notion Labs, Inc or OpenAI.

Download ZIP

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