From ai-dlc-skills
Download Google Docs as markdown files to external-brain folder. Use when the user provides a Google Doc URL and wants to save it locally, archive documentation, or import Google Docs content.
How this skill is triggered — by the user, by Claude, or both
Slash command
/ai-dlc-skills:gdoc-to-markdownThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Downloads a Google Doc as markdown and saves it to `~/GitHub/external-brain/`.
Downloads a Google Doc as markdown and saves it to ~/GitHub/external-brain/.
Run the download script with a Google Doc URL:
python scripts/download-gdoc.py "https://docs.google.com/document/d/DOC_ID/edit"
# Specify a category for organization
python scripts/download-gdoc.py "URL" --category meetings
# Specify a custom output directory
python scripts/download-gdoc.py "URL" --output ~/GitHub/external-brain/notes
gcloud CLI installed and authenticated~/GitHub/external-brain/ directory existsThe script uses gcloud auth with Drive API scopes. Run this once:
gcloud auth application-default login --scopes="https://www.googleapis.com/auth/cloud-platform,https://www.googleapis.com/auth/drive.readonly"
The .env file in this skill directory configures the quota project:
GCLOUD_QUOTA_PROJECT=queen-of-code
The project must have the Google Drive API enabled. Enable it at: https://console.developers.google.com/apis/api/drive.googleapis.com/overview
Files are saved with:
---
source_url: https://docs.google.com/document/d/DOC_ID/edit
doc_id: DOC_ID
downloaded_at: 2026-02-05T14:30:00Z
owner: [email protected]
category: uncategorized
---
Document titles are converted to safe filenames:
.md extension addedExample: "Q1 Planning Meeting Notes" → q1-planning-meeting-notes.md
download-gdoc.py: Main download script
python scripts/download-gdoc.py "https://docs.google.com/document/d/1ABC123/edit"
Returns the path to the saved file on success.
npx claudepluginhub nathan-oakes/ai-dlc --plugin ai-dlc-skillsGuides creation, editing, and verification of skills for AI coding agents using test-driven development with subagent scenarios. Use when authoring or debugging skills.