onenote-performance-tuning

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Optimize OneNote Graph API performance for large notebooks, image handling, and batch operations. Use when dealing with slow API responses, large notebooks, image uploads, or HTTP 507 errors. Trigger with "onenote performance", "onenote slow", "onenote large notebook", "onenote image upload".

AI & Automation 2,266 stars 315 forks Updated today MIT

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Skill Content

# OneNote — Performance Tuning & Optimization ## Overview OneNote performance degrades predictably at scale: notebooks with 100+ sections take 3-5 seconds per API call when using `$expand`, pages with embedded images over 4MB fail silently, and sections hitting the page limit return `507 Insufficient Storage`. Image uploads are capped at 25MB per multipart part, and requesting full page content for hundreds of pages without `$select` can exhaust your rate budget in seconds. This skill provides tested patterns for every performance bottleneck: selective `$expand` and `$select` for minimal payloads, image compression before upload, batch requests via `$batch`, pagination with `$top` to avoid loading thousands of pages, and caching strategies that invalidate on change detection. Key pain points addressed: - Full `$expand=sections($expand=pages)` on large notebooks can take 10+ seconds and return multi-MB responses - Image uploads silently fail when a single multipart part exceeds 25MB — no error, just missing image - `507 Insufficient Storage` when a section hits its page limit (approximately 5,000 pages) - Page content retrieval (`GET /pages/{id}/content`) is 5-10x slower than metadata-only requests ## Prerequisites - Azure app registration with delegated permissions: `Notes.ReadWrite` - App-only auth deprecated March 31, 2025 — use delegated auth only - Python: `pip install msgraph-sdk azure-identity Pillow` (Pillow for image compression) - Node/TypeScript: `npm install ...

Details

Author
jeremylongshore
Repository
jeremylongshore/claude-code-plugins-plus-skills
Created
7 months ago
Last Updated
today
Language
Python
License
MIT

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