Transloadit Media Processing
GitHubOfficialFirst committed by kvzDistributed by GitHub
- Skill
- Tool integration
- Automation and integrations
- MIT
- Low risk
AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
Transloadit Media Processing provides an interface to manage media transformation workflows using cloud-based robots. It supports a wide range of operations including video encoding, image manipulation, audio transcoding, and document OCR. Users can define multi-step pipelines through JSON configurations, allowing for complex file processing tasks such as resizing, watermarking, concatenating, and format conversion.
What it does
- Encodes video files into formats like HLS, MP4, and WebM
- Generates thumbnails and animated GIFs from video content
- Resizes, crops, optimizes, and watermarks images
- Performs OCR on PDF documents and scanned images
- Concatenates and transcodes audio files
- Chains multiple processing steps into reusable pipelines
When to use it
- When you need to automate media format conversions in your application
- When building multi-step pipelines for image or video processing
- When you require OCR capabilities for document processing
- When you need to integrate media transformation directly into an AI agent workflow
Advantages
- Access to over 86 specialized processing robots
- Supports complex, chained multi-step media pipelines
- Flexible integration via MCP server or command-line interface
- Reusable template system for consistent processing logic
Usage examples
- “Convert this video to HLS format using Transloadit.”
- The agent triggers a Transloadit assembly using the /video/encode robot with an HLS preset.
- “Resize this image to 1200x800 and add a watermark.”
- The agent executes a two-step pipeline: first resizing the image, then applying a watermark via the /image/resize robot.
- “Extract text from this PDF document.”
- The agent runs the /document/ocr robot to process the file and return the extracted text.
- “Create a thumbnail for my video.”
- The agent uses the /video/thumbs robot to generate the requested number of thumbnail images.
Install
Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/github/transloadit-media-processing/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip transloadit-media-processing.zip -d .agents/skills
Documentation by kvz
Transloadit Media Processing
Process, transform, and encode media files using Transloadit's cloud infrastructure. Supports video, audio, images, and documents with 86+ specialized processing robots.
When to Use This Skill
Use this skill when you need to:
Encode video to HLS, MP4, WebM, or other formats
Generate thumbnails or animated GIFs from video
Resize, crop, watermark, or optimize images
Convert between image formats (JPEG, PNG, WebP, AVIF, HEIF)
Extract or transcode audio (MP3, AAC, FLAC, WAV)
Concatenate video or audio clips
Add subtitles or overlay text on video
OCR documents (PDF, scanned images)
Run speech-to-text or text-to-speech
Apply AI-based content moderation or object detection
Build multi-step media pipelines that chain operations together
Setup
Option A: MCP Server (recommended for Copilot)
Add the Transloadit MCP server to your IDE config. This gives the agent direct access
to Transloadit tools (create_template, create_assembly, list_assembly_notifications, etc.).
VS Code / GitHub Copilot (.vscode/mcp.json or user settings):
{
"servers": {
"transloadit": {
"command": "npx",
"args": ["-y", "@transloadit/mcp-server", "stdio"],
"env": {
"TRANSLOADIT_KEY": "YOUR_AUTH_KEY",
"TRANSLOADIT_SECRET": "YOUR_AUTH_SECRET"
}
}
}
}Get your API credentials at https://transloadit.com/c/-/api-credentials
Option B: CLI
If you prefer running commands directly:
npx -y @transloadit/node assemblies create \
--steps '{"encoded": {"robot": "/video/encode", "use": ":original", "preset": "hls-1080p"}}' \
--wait \
--input ./my-video.mp4Core Workflows
Encode Video to HLS (Adaptive Streaming)
{
"steps": {
"encoded": {
"robot": "/video/encode",
"use": ":original",
"preset": "hls-1080p"
}
}
}Generate Thumbnails from Video
{
"steps": {
"thumbnails": {
"robot": "/video/thumbs",
"use": ":original",
"count": 8,
"width": 320,
"height": 240
}
}
}Resize and Watermark Images
{
"steps": {
"resized": {
"robot": "/image/resize",
"use": ":original",
"width": 1200,
"height": 800,
"resize_strategy": "fit"
},
"watermarked": {
"robot": "/image/resize",
"use": "resized",
"watermark_url": "https://example.com/logo.png",
"watermark_position": "bottom-right",
"watermark_size": "15%"
}
}
}OCR a Document
{
"steps": {
"recognized": {
"robot": "/document/ocr",
"use": ":original",
"provider": "aws",
"format": "text"
}
}
}Concatenate Audio Clips
{
"steps": {
"imported": {
"robot": "/http/import",
"url": ["https://example.com/clip1.mp3", "https://example.com/clip2.mp3"]
},
"concatenated": {
"robot": "/audio/concat",
"use": "imported",
"preset": "mp3"
}
}
}Multi-Step Pipelines
Steps can be chained using the "use" field. Each step references a previous step's output:
{
"steps": {
"resized": {
"robot": "/image/resize",
"use": ":original",
"width": 1920
},
"optimized": {
"robot": "/image/optimize",
"use": "resized"
},
"exported": {
"robot": "/s3/store",
"use": "optimized",
"bucket": "my-bucket",
"path": "processed/${file.name}"
}
}
}Key Concepts
Assembly: A single processing job. Created via
create_assembly(MCP) orassemblies create(CLI).Template: A reusable set of steps stored on Transloadit. Created via
create_template(MCP) ortemplates create(CLI).Robot: A processing unit (e.g.,
/video/encode,/image/resize). See full list at https://transloadit.com/docs/transcoding/Steps: JSON object defining the pipeline. Each key is a step name, each value configures a robot.
:original: Refers to the uploaded input file.
Tips
Use
--waitwith the CLI to block until processing completes.Use
presetvalues (e.g.,"hls-1080p","mp3","webp") for common format targets instead of specifying every parameter.Chain
"use": "step_name"to build multi-step pipelines without intermediate downloads.For batch processing, use
/http/importto pull files from URLs, S3, GCS, Azure, FTP, or Dropbox.Templates can include
${variables}for dynamic values passed at assembly creation time.
Files
- ATTRIBUTION.mdAdded by AutoGineer836 B
- LICENSELicence file added by AutoGineer1.0 KiB
- SKILL.md5.0 KiB
Requirements and compatibility
Written for: GitHub Copilot
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 an MCP server, Needs Node.js
The author says: Requires a free Transloadit account (https://transloadit.com/signup). Uses the @transloadit/mcp-server MCP server or the @transloadit/node CLI.
Security
What our scanner noticed· Low risk
- Instructions that use the network
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
- First committed by
kvz
- Publisher
- GitHub (Official)
- Distributed by
- GitHub
- Source
- github/awesome-copilot · skills/transloadit-media-processing · commit 797337f
- Licence
- MIT License (redistribution allowed)
- LICENSE (added by AutoGineer from LICENSE)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
- Contributors
- kvz
AutoGineer is not affiliated with or endorsed by kvz or GitHub.
Download ZIP
The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download transloadit-media-processing.zip