Scribe

Organization

zoom

scribe

Zoom AI Services Scribe for synchronous and batch transcription of uploaded or stored media. Use for Build-platform JWT auth, fast mode transcription, batch S3 jobs, webhook callbacks, and transcript-pipeline design.

Overview

Publisher	zoom
Repository	`skills`
Skill name	`scribe`
Stars	78
Forks	16
Bundled files	10
License	MIT
Links	Skill source Repository

Markdown instructions
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
Works with any LLM
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
10 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by zoom on GitHub. Read the source before you install it.

Installation

Install the Scribe AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
Enable the skill in any chat where you want it available.

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/zoom/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/skills/scribe .claude/skills/scribe

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Scribe in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Scribe on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Scribe is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Zoom AI Services Scribe

Current API audit: Official docs and OpenAPI were rechecked on 2026-07-10. The May 18, 2026 release added GET /aiservices/scribe/jobs/{jobId}/files/{fileId}; it is included below.

Implementation guidance 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/ai-services-quickstart/

Routing Guardrail

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

Core Workflow

Get Build-platform credentials and generate an HS256 JWT.
Choose fast mode for one short file or batch mode for stored archives / large sets.
Submit the transcription request.
For batch jobs, poll job/file status or receive webhook notifications.
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 instead.

Endpoint Surface

Mode	Method	Path	Use
Fast	`POST`	`/aiservices/scribe/transcribe`	Synchronous transcription for one file
Batch	`POST`	`/aiservices/scribe/jobs`	Submit asynchronous batch job
Batch	`GET`	`/aiservices/scribe/jobs`	List jobs
Batch	`GET`	`/aiservices/scribe/jobs/{jobId}`	Inspect job summary/state
Batch	`DELETE`	`/aiservices/scribe/jobs/{jobId}`	Cancel queued/processing job
Batch	`GET`	`/aiservices/scribe/jobs/{jobId}/files`	Inspect per-file results
Batch	`GET`	`/aiservices/scribe/jobs/{jobId}/files/{fileId}`	Inspect one per-file result

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 + scribe
Transcribe then summarize -> scribe + ../summarizer/SKILL.md
Transcribe then translate -> scribe + ../translator/SKILL.md
Webhook verification hardening -> ../webhooks/SKILL.md
Real-time live transcript/media -> ../rtms/SKILL.md
Cross-product routing -> ../general/SKILL.md

Operations

RUNBOOK.md - 5-minute preflight and debugging checklist.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Scribe AI skill do?

Why use Scribe on TypingMind?

Because you install it once and use it with any model. Scribe is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Scribe in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/zoom/skills/tree/main/skills/scribe. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Scribe?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Scribe?

As many as you like. As long as a model supports skills, you can use Scribe with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Scribe AI skill free?

Yes. It is published on GitHub by zoom under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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Scribe

Overview