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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

examples/how-to-guide.md

Example: How-To Guide from Discussion

User Request: "Save our discussion about deploying to production as a how-to guide"

Workflow

1. Extract Content from Chat

From conversation, identified:

  • Deployment prerequisites

  • Step-by-step procedure

  • Common issues and solutions

  • Best practices and tips

2. Structure as How-To

Organized into:

  • Overview & prerequisites

  • Numbered deployment steps

  • Verification steps

  • Troubleshooting section

  • Related resources

3. Find Location
Notion:notion-search
query: "deployment documentation"

Found: Engineering Wiki → Deployment section

4. Create Guide
Notion:notion-create-pages
parent: { page_id: "deployment-section-id" }

Output

# How to Deploy to Production

## Overview
Production deployment using GitHub Actions with zero-downtime rolling updates.
**Time Required**: 15-20 minutes | **Complexity**: Intermediate

## Prerequisites
- [ ] PR approved and merged to main
- [ ] All CI tests passing
- [ ] Database migrations reviewed
- [ ] Feature flags configured

## Deployment Steps

1. **Verify main branch is ready**
   ```bash
   git checkout main && git pull

  1. Tag release

    git tag -a v1.2.3 -m "Release v1.2.3"
git push origin v1.2.3

  2. Trigger deployment

  3. Database migrations (if needed)

    • Auto-run in GitHub Actions

    • Check logs for completion

  4. Verify deployment

    • Wait for health checks (2-3 min)

    • Test key endpoints

    • Check error rates in Datadog

Verification Checklist

  • [ ] All pods healthy in k8s dashboard

  • [ ] Error rate < 0.1% in last 10 min

  • [ ] Response time p95 < 500ms

  • [ ] Test login flow

  • [ ] Check Slack #alerts channel

Troubleshooting

Health checks failing → Check pod logs: kubectl logs -f deployment/api -n production

Migration errors → Rollback: Revert tag, migrations auto-rollback

High error rate → Emergency rollback: Previous tag auto-deploys via GitHub Actions

Best Practices

  • Deploy during low-traffic hours (2-4am PST)

  • Have 2 engineers available

  • Monitor for 30 min post-deploy

  • Update #engineering Slack with deploy notice

Related Docs

  • <mention-page url="...">Rollback Procedure</mention-page>

  • <mention-page url="...">Database Migration Guide</mention-page>


### 5. Make Discoverable

Notion:notion-update-page page_id: "engineering-wiki-homepage" command: "insert_content_after"

Added link in Engineering Wiki → How-To Guides section

## Key Success Factors
- Captured tribal knowledge from discussion
- Structured as actionable steps
- Included troubleshooting from experience
- Made discoverable by linking from wiki index
- Added metadata (time, complexity)

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)
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-knowledge-capture.zip