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Scribe

OpenAIOfficialCreated by ZoomDistributed by OpenAI

  • Skill
  • Tool integration
  • Automation and integrations
  • MIT
  • No issues found
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From the author

Use when using Scribe.
— the description by Zoom

AutoGineer summary

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

This skill provides a structured workflow for integrating Zoom Scribe AI services into your applications. It covers the end-to-end process of setting up transcription pipelines, from credential management and media ingestion to handling batch jobs and webhook callbacks. It distinguishes between synchronous fast-mode transcription and asynchronous batch processing, ensuring developers can select the appropriate mode for their specific latency and volume requirements.

What it does

  • Guides the setup of authentication using JWT for Zoom AI services.
  • Defines the workflow for both synchronous fast-mode and asynchronous batch transcription.
  • Outlines requirements for media ingestion, chunking, and persistence.
  • Provides instructions for implementing webhook callbacks for batch job notifications.
  • Offers troubleshooting steps for common issues like credential drift or processing errors.

When to use it

  • When you need to transcribe pre-recorded media files stored in cloud storage.
  • When building automated transcription pipelines that require batch processing.
  • When you need to integrate Zoom's AI transcription capabilities into an existing application.
  • When you need to verify the status or manage the lifecycle of batch transcription jobs.

Advantages

  • Provides a clear, step-by-step procedure for complex transcription integrations.
  • Helps distinguish between different processing modes to optimize for latency.
  • Includes essential guidance on security practices like webhook signature verification.
  • Centralizes configuration requirements for S3-based batch processing.

Usage examples

“How do I set up a batch transcription job for files in S3?”
The agent will guide you through defining the input S3 URI, configuring the batch job parameters, and setting up a webhook to receive completion notifications.
“What are the requirements for using the synchronous Scribe endpoint?”
The agent will explain the POST /aiservices/scribe/transcribe endpoint, required file and config fields, and how to handle the immediate response.

Install

  1. Download the skill as a ZIP:

    https://mp-staging.autogineer.com/agent-skills/openai/scribe/download

  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip scribe.zip -d .agents/skills

Documentation by Zoom

Zoom AI Services Scribe

Use this skill for Zoom Scribe transcription pipelines over uploaded or stored media. If the user needs live meeting media, compare against RTMS or Meeting SDK bot workflows first.

Workflow

  1. Confirm the media source, size, language, expected latency, and whether fast mode or batch mode fits.

  2. Set up Build-platform credentials and JWT handling separately from application transcription logic.

  3. Design ingestion, chunking, retries, webhook callbacks, and persistence before connecting downstream AI workflows.

  4. Implement a minimal transcription request and response parser, then add batching and operational monitoring.

  5. Debug by checking credential audience, processing mode, media format, file size, backend timeout, and webhook delivery.

References

Files

scenarios/high-level-scenarios.md

High-Level Scenarios

Scenario 1: On-Demand Upload Transcription

Use fast mode when a user uploads one file and expects a transcript immediately.

Flow:

  1. Browser uploads file to your backend.

  2. Backend generates Build JWT.

  3. Backend calls POST /aiservices/scribe/transcribe.

  4. Backend returns transcript JSON to the caller.

Common downstream uses:

  • post-call summaries

  • ticket enrichment

  • searchable clip libraries

  • internal review or handoff notes

Scenario 2: Batch S3 Archive Transcription

Use batch mode when call archives or media libraries already live in S3.

Flow:

  1. Build a batch request with input prefix and output prefix.

  2. Submit POST /aiservices/scribe/jobs.

  3. Track state by webhook or polling.

  4. Read /jobs/{jobId}/files for per-file success/failure.

  5. Ingest outputs into search, analytics, or storage.

Common downstream uses:

  • compliance and audit logging

  • searchable webinar or podcast archives

  • bulk transcript backfills

  • QA scoring inputs

Scenario 3: Zoom Recording Export + Re-Transcription

Use when you must re-process Zoom-managed recordings with your own transcript settings.

Skill chain:

  • zoom-rest-api to fetch/download recordings

  • scribe to transcribe exported media

Typical reasons:

  • you need your own retention/search pipeline

  • you need different transcript settings than Zoom-managed defaults

  • you want to enrich recordings with your own summarization or tagging flow

Scenario 4: Compliance / QA Processing

Use batch mode when transcripts must be generated offline for audits, QA scoring, or archival search.

Prefer:

  • word_time_offsets=true when reviewers need precise excerpts

  • channel_separation=true for stereo call recordings

  • webhook + queue ingestion instead of synchronous polling for large volumes

Scenario 5: Customer Support Voice-to-Insights Pipeline

Use when support call recordings should feed operational analytics instead of stopping at raw transcript text.

Flow:

  1. Ingest call recordings from storage or exported meeting assets.

  2. Transcribe with scribe.

  3. Store transcript plus speaker/timing metadata.

  4. Run downstream sentiment, keyword, escalation, or QA logic in your own pipeline.

Guardrail:

  • keep scribe focused on transcription

  • do sentiment analysis, keyword detection, or scoring in downstream services after transcript generation

Scenario 6: Browser Microphone Incremental Transcript

Use when a web page should capture microphone audio and show transcript updates every few seconds without switching to RTMS.

Flow:

  1. Browser captures microphone audio with MediaRecorder.

  2. Browser flushes one chunk every 5 seconds.

  3. Backend accepts each chunk as a normal fast-mode upload through the async wrapper.

  4. Frontend polls by request ID and appends transcript chunks in order.

Guardrail:

  • this is pseudo-streaming over repeated file uploads

  • this is best kept as a lightweight demo or constrained fallback

  • do not choose it first for a true live-transcription product

  • if the requirement is truly live media stream ingestion or lower-latency continuous audio, route to rtms

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.

Needs: Needs Node.js

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
Zoom
Publisher
OpenAI (Official)
Distributed by
OpenAI
Source
openai/plugins · plugins/zoom/skills/scribe · commit d420d13
Licence
MIT License (redistribution allowed)
  • LICENSE (added by AutoGineer from plugins/zoom/LICENSE)
  • ATTRIBUTION.md (written by AutoGineer)
Synced

AutoGineer is not affiliated with or endorsed by Zoom or OpenAI.

Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download scribe.zip