A Python-powered interface to manage Loom video recordings, transcripts, and workspace folders via the official Developer API.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install loom
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install loom using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Loom?
The Loom skill for Openclaw Skills provides a robust, dependency-free Python interface designed to interact seamlessly with the Loom Developer API. It empowers developers and AI agents to automate the lifecycle of video content, from retrieving transcripts for documentation to organizing recordings into specific workspace folders. By utilizing this integration within the Openclaw Skills ecosystem, teams can bridge the gap between asynchronous video communication and automated developer workflows.
Built with a focus on simplicity and portability, this skill requires only a standard Python 3.10+ environment. It allows for high-velocity operations such as bulk metadata updates, comment retrieval, and workspace member management, making it an essential tool for scaling video-centric communication strategies.
Loom Use Cases
- Automating the extraction of video transcripts for AI-driven meeting summaries.
- Programmatically organizing Loom recordings into specific project folders for better team visibility.
- Bulk updating video titles and descriptions to maintain consistent documentation standards.
- Monitoring workspace activity by listing comments and workspace members via automated scripts.
How Loom Works
- The skill authenticates with the Loom Developer API using a securely stored LOOM_ACCESS_TOKEN.
- Commands are issued via the CLI to target specific resources like videos, folders, or transcripts.
- The Python script processes the request and communicates with Loom's backend endpoints using standard libraries.
- Data is returned in a structured JSON format, optimized for processing by other Openclaw Skills or AI agents.
- Users can optionally toggle a human-readable output mode for manual auditing and debugging.
Loom Setup
To get started with this component of your Openclaw Skills, ensure you have Python 3.10+ installed and follow these steps:
- Obtain your Loom Developer API token from the Loom dashboard.
- Set the environment variable:
export LOOM_ACCESS_TOKEN='your_token_here'
- Execute a test command to list your videos:
python3 scripts/loom.py videos --human
Loom Data Schema & Taxonomy
The skill interacts with the Loom API to return structured data. Below is the primary data organization:
| Resource | Data Points Provided |
|---|---|
| Video | ID, Title, Description, Transcript Text, URL |
| Folder | Folder ID, Name, List of contained Video IDs |
| User | Current authenticated user details and workspace ID |
| Workspace | List of members and associated metadata |
| Comments | List of text comments linked to specific video IDs |
Loom Advanced Features
- Multi-agent compatibility allowing various Openclaw Skills to trigger video management tasks.
- Full transcript retrieval support for seamless integration with LLM-based analysis pipelines.
- Workspace-wide folder traversal to manage content at an organizational scale.
- Dependency-free implementation ensuring zero conflicts with other Python-based automation tools.
SKILL.md
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