scribe

name: scribe
description: "Reference skill for Zoom AI Services Scribe. Use after routing to a transcription workflow when handling uploaded or stored media, Build-platform JWT auth, fast mode transcription, batch jobs, or transcript pipeline design."
user-invocable: false
triggers:
  - "scribe"
  - "ai services scribe"
  - "zoom scribe"
  - "transcribe audio file"
  - "transcribe video file"
  - "batch transcription"
  - "fast mode transcription"
  - "build platform jwt"

Zoom AI Services Scribe

Background reference for Zoom AI Services Scribe across:

• synchronous single-file transcription (POST /aiservices/scribe/transcribe)
• asynchronous batch jobs (/aiservices/scribe/jobs*)
• browser microphone pseudo-streaming via repeated short file uploads
• webhook-driven batch status updates
• Build-platform JWT generation and credential handling

Official docs:

• https://developers.zoom.us/docs/ai-services/
• https://developers.zoom.us/docs/ai-services/scribe/
• https://developers.zoom.us/docs/api/ai-services/
• https://developers.zoom.us/api-hub/ai-services/methods/endpoints.json
• Quickstart sample: https://github.com/zoom/scribe-quickstart/

Routing Guardrail

• If the user needs uploaded or stored media transcribed into text, route here first.
• If the user needs live meeting media without file-based upload/batch jobs, route to [../rtms/SKILL.md](../rtms/SKILL.md).
• If the user needs Zoom REST API inventory for AI Services paths, chain [../rest-api/SKILL.md](../rest-api/SKILL.md).
• If the user needs webhook signature patterns or generic HMAC receiver hardening, optionally chain [../webhooks/SKILL.md](../webhooks/SKILL.md).

Quick Links

1. [concepts/auth-and-processing-modes.md](concepts/auth-and-processing-modes.md)
2. [scenarios/high-level-scenarios.md](scenarios/high-level-scenarios.md)
3. [examples/fast-mode-node.md](examples/fast-mode-node.md)
4. [examples/batch-webhook-pipeline.md](examples/batch-webhook-pipeline.md)
5. [references/api-reference.md](references/api-reference.md)
6. [references/environment-variables.md](references/environment-variables.md)
7. [references/samples-validation.md](references/samples-validation.md)
8. [references/versioning-and-drift.md](references/versioning-and-drift.md)
9. [troubleshooting/common-drift-and-breaks.md](troubleshooting/common-drift-and-breaks.md)
10. [RUNBOOK.md](RUNBOOK.md)

Core Workflow

1. Get Build-platform credentials and generate an HS256 JWT.
2. Choose fast mode for one short file or batch mode for stored archives / large sets.
3. Submit the transcription request.
4. For batch jobs, poll job/file status or receive webhook notifications.
5. Persist and post-process transcript JSON.

Hosted Fast-Mode Guardrail

• The formal fast-mode API limits are 100 MB and 2 hours, but hosted browser flows can still time out before the upstream response returns.
• Current deployed-sample observations:
• ~17.2 MB MP4 completed in about 26s
• ~38.6 MB MP4 completed in about 26-37s
• ~59.2 MB MP4 completed in about 32-34s on the backend
• some ~59.2 MB browser requests still surfaced as frontend 504 while backend logs later showed 200
• Treat frontend 504 plus backend 200 as a browser/edge timeout race, not an automatic transcription failure.
• For hosted UIs, prefer an async request/polling wrapper for fast mode instead of holding the browser open for the full upstream response.
• For larger or less predictable media, prefer batch mode even when the file is still within the formal fast-mode size limit.

Browser Microphone Pattern

• scribe does not expose a documented real-time streaming API surface.
• If you want a browser microphone experience, use pseudo-streaming:

1. capture microphone audio in short chunks
2. upload each chunk through the async fast-mode wrapper
3. poll for completion
4. append chunk transcripts in sequence

• Recommended starting cadence:
• chunk size: 5 seconds
• acceptable range: 5-10 seconds
• in-flight chunk requests: 2-3
• This is a practical UI pattern for incremental transcript updates, not a substitute for rtms.
• Treat this as a fallback demo pattern, not the preferred production architecture.
• It adds repeated upload overhead, chunk-boundary drift, browser codec/container variability, and transcript stitching complexity.
• If the user asks for actual live stream ingestion, low-latency continuous media, or server-push media transport, route to [../rtms/SKILL.md](../rtms/SKILL.md) instead.

Endpoint Surface

High-Level Scenarios

• On-demand clip transcription after a user uploads one recording.
• Batch transcription of stored S3 call archives.
• Webhook-driven ETL pipeline that writes transcripts to your database/search index.
• Re-transcription of Zoom-managed recordings after exporting them to your own storage.
• Offline compliance or QA workflows that need timestamps, channel separation, and speaker hints.

Chaining

• Stored Zoom recordings -> [../rest-api/SKILL.md](../rest-api/SKILL.md) + scribe
• Webhook verification hardening -> [../webhooks/SKILL.md](../webhooks/SKILL.md)
• Real-time live transcript/media -> [../rtms/SKILL.md](../rtms/SKILL.md)
• Cross-product routing -> [../general/SKILL.md](../general/SKILL.md)

Operations

• [RUNBOOK.md](RUNBOOK.md) - 5-minute preflight and debugging checklist.