N8n:loom Transcript Agent Skill

by n8n-io

Fetch and display the full transcript from a Loom video URL. Use when the user wants to get or read a Loom transcript.

910 tokens

context cost

the whole folder, loaded on every use

files

instructions only

copies elsewhere

how many repositories repackaged it

199283

stars on the repo

on the repository, not the skill itself

Install

one command, takes just this skill from the repository

npx skills add https://github.com/n8n-io/n8n --skill n8n:loom-transcript

What it tells the agent to use

found in the instruction text

WebFetch fetches pages from the network

The instruction itself

9 sections, as written by the author

Loom Transcript Fetcher

Fetch the transcript from a Loom video using Loom's GraphQL API.

Instructions

Given the Loom URL: $ARGUMENTS

1. Extract the Video ID

Parse the Loom URL to extract the 32-character hex video ID. Supported URL formats:

https://www.loom.com/share/<video-id>
https://www.loom.com/embed/<video-id>
https://www.loom.com/share/<video-id>?sid=<session-id>

The video ID is the 32-character hex string after /share/ or /embed/.

2. Fetch Video Metadata

Use the WebFetch tool to POST to https://www.loom.com/graphql to get the video title and details.

Use this curl command via Bash:

curl -s 'https://www.loom.com/graphql' \
  -H 'Content-Type: application/json' \
  -H 'Accept: application/json' \
  -H 'x-loom-request-source: loom_web_45a5bd4' \
  -H 'apollographql-client-name: web' \
  -H 'apollographql-client-version: 45a5bd4' \
  -d '{
    "operationName": "GetVideoSSR",
    "variables": {"id": "<VIDEO_ID>", "password": null},
    "query": "query GetVideoSSR($id: ID!, $password: String) { getVideo(id: $id, password: $password) { ... on RegularUserVideo { id name description createdAt owner { display_name } } } }"
  }'

3. Fetch the Transcript URLs

Use curl via Bash to call the GraphQL API:

curl -s 'https://www.loom.com/graphql' \
  -H 'Content-Type: application/json' \
  -H 'Accept: application/json' \
  -H 'x-loom-request-source: loom_web_45a5bd4' \
  -H 'apollographql-client-name: web' \
  -H 'apollographql-client-version: 45a5bd4' \
  -d '{
    "operationName": "FetchVideoTranscript",
    "variables": {"videoId": "<VIDEO_ID>", "password": null},
    "query": "query FetchVideoTranscript($videoId: ID!, $password: String) { fetchVideoTranscript(videoId: $videoId, password: $password) { ... on VideoTranscriptDetails { id video_id source_url captions_source_url } ... on GenericError { message } } }"
  }'

Replace <VIDEO_ID> with the actual video ID extracted in step 1.

The response contains:

source_url — JSON transcript URL
captions_source_url — VTT (WebVTT) captions URL

4. Download and Parse the Transcript

Fetch both URLs returned from step 3 (if available):

VTT captions (captions_source_url): Download with curl -sL "<url>". This is a WebVTT file with timestamps and text.
JSON transcript (source_url): Download with curl -sL "<url>". This is a JSON file with transcript segments.

Prefer the VTT captions as the primary source since they include proper timestamps. Fall back to the JSON transcript if VTT is unavailable.

5. Present the Transcript

Format and present the full transcript to the user:

Video: [Title from metadata]

Author: [Owner name]

Date: [Created date]

0:00 - First transcript segment text...

0:14 - Second transcript segment text...

(continue for all segments)

Error Handling

If the GraphQL response contains a GenericError, report the error message to the user.
If both source_url and captions_source_url are null/missing, tell the user that no transcript is available for this video.
If the video URL is invalid or the ID cannot be extracted, ask the user for a valid Loom URL.

Notes

No authentication or cookies are required — Loom's transcript API is publicly accessible.
Only English transcripts are available through this API.
Transcripts are auto-generated and may contain minor errors.

Other skills for the same job

different authors, same section of the catalogue

Canvas Design

by anthropics

vendor ×13

Create beautiful visual art in .png and .pdf documents using design philosophy. You should use this skill when the user asks to create a poster, piece of art, design, or other static piece. Create original visual designs, never copying existing artists' work to avoid copyright violations.

Creating algorithmic art using p5.js with seeded randomness and interactive parameter exploration. Use this when users request creating art using code, generative art, algorithmic art, flow fields, or particle systems. Create original algorithmic art rather than copying existing artists' work to avoid copyright violations.

Improves the quality of images, especially screenshots, by enhancing resolution, sharpness, and clarity. Perfect for preparing images for presentations, documentation, or social media posts.

Downloads videos from YouTube and other platforms for offline viewing, editing, or archival. Handles various formats and quality options.

Lightweight WSI tile extraction and preprocessing. Use for basic slide processing tissue detection, tile extraction, stain normalization for H&E images. Best for simple pipelines, dataset preparation, quick tile-based analysis. For advanced spatial proteomics, multiplexed imaging, or deep learning pipelines use pathml.

Microscopy data management platform. Access images via Python, retrieve datasets, analyze pixels, manage ROIs/annotations, batch processing, for high-content screening and microscopy workflows.

Python library for working with DICOM (Digital Imaging and Communications in Medicine) files. Use this skill when reading, writing, or modifying medical imaging data in DICOM format, extracting pixel data from medical images (CT, MRI, X-ray, ultrasound), anonymizing DICOM files, working with DICOM metadata and tags, converting DICOM images to other formats, handling compressed DICOM data, or processing medical imaging datasets. Applies to tasks involving medical image analysis, PACS systems, radiology workflows, and healthcare imaging applications.

This skill should be used when working with pre-trained transformer models for natural language processing, computer vision, audio, or multimodal tasks. Use for text generation, classification, question answering, translation, summarization, image classification, object detection, speech recognition, and fine-tuning models on custom datasets.

13k tokens

How to use it

Copy the folder

Take n8n-io/n8n:loom-transcript from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.