embed

embed is a skill for Claude Code from twelvelabs-io/twelve-labs-claude-code-plugin. It costs 33 tokens per session (1,175 once invoked), scanned A, original, MIT.

A tool for creating, checking, and retrieving video embeddings, which are machine-readable representations of video content.

In plain words
What is it for?
Use it to generate embeddings from a local video file or URL, check whether processing is complete, or retrieve embeddings from an indexed video.
Why use it?
It provides the video representations needed for systems that compare, search, or otherwise process video content.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Not installable: its command points at a path on the author’s own machine, so it runs nowhere else. The line is /Users/me/Videos/demo.mp4.

Part of the twelvelabs plugin — 10 skills, 11 commands, 2 hooks, 1 MCP server shipped together

Good fit Use it to generate embeddings from a local video file or URL, check whether processing is complete, or retrieve embeddings from an indexed video.

Compare 6 skills from other repositories ↓
Install

Getting it into your agent

This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.

Claude Code
/plugin marketplace add twelvelabs-io/twelve-labs-claude-code-plugin
Claude Code
/plugin install twelvelabs

Made for: Claude Code.

Or install twelvelabs, the plugin that ships this one along with the rest of its 10 skills, 11 commands, 2 hooks, 1 MCP server.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for embed

README.md
[![agentmods](https://agentmods.dev/badge/skills/twelvelabs-io/twelve-labs-claude-code-plugin/embed.svg)](https://agentmods.dev/skills/twelvelabs-io/twelve-labs-claude-code-plugin/embed)
Your own site
<a href="https://agentmods.dev/skills/twelvelabs-io/twelve-labs-claude-code-plugin/embed"><img src="https://agentmods.dev/badge/skills/twelvelabs-io/twelve-labs-claude-code-plugin/embed.svg" alt="Measured on agentmods" height="20"></a>
Per session 33 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,175 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00033 $0.01175
Opus 5 $0.00016 $0.00588
Sonnet 5 $0.00007 $0.00235
Haiku 4.5 $0.00003 $0.00118

Measured 7d ago against content hash 3bcb252425b9, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-07, from the pricing page.

Security

Grade A, and why

embed scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 7d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/embed/SKILL.md · 176 lines

How it starts

The opening of the file, as written. The whole thing — 176 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Video Embeddings

Create video embeddings, check embedding task status, and retrieve embeddings from tasks or indexed videos using TwelveLabs.

When to Use This Skill

Use this skill when the user:

  • Wants to create embeddings from a video ("embed this video", "create embeddings for this video")
  • Asks about embedding task status ("are my embeddings ready?", "check embedding status")
  • Wants to retrieve embeddings from an indexed video ("get embeddings for video abc123")
  • Mentions video embeddings in any context

Instructions

Creating Embeddings from a Video

Step 1: Identify the Video Source

Extract the video path or URL from the user's message:

  • Local file path: /path/to/video.mp4, ./video.mp4, ~/Videos/demo.mov
  • Remote URL: https://example.com/video.mp4

If no path/URL is provided, ask the user:

Please provide the video file path or URL you'd like to create embeddings for.

Examples:
- Local file: /path/to/video.mp4 or ./video.mp4
- Remote URL: https://example.com/video.mp4
Step 2: Validate the Input

For Local Files:

  1. Resolve relative paths to absolute paths
  2. Verify the file exists
  3. Check file extension is supported: .mp4, .mov, .avi, .mkv, .webm

For URLs:

  1. Verify URL starts with http:// or https://
Step 3: Start Embeddings Task

Use the mcp__twelvelabs-mcp__start-video-embeddings-task tool:

For Local Files:

Tool: mcp__twelvelabs-mcp__start-video-embeddings-task
Parameters:
  videoFilePath: "<absolute-path-to-video>"

For URLs:

Tool: mcp__twelvelabs-mcp__start-video-embeddings-task
Parameters:
  videoUrl: "<url>"
Step 4: Report Result

On success:

Video embedding started!

Source: <filename-or-url>
Task ID: <task_id>

Embedding creation runs in the background and may take several minutes.
Ask "are my embeddings ready?" to check the progress.

Checking Embedding Status

When the user asks about embedding status ("are my embeddings ready?", "check embedding status"):

Read the full file on GitHub · 176 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 7d ago First seen · 176 lines · 33 tokens per session scan A 3bcb252425b9

Subscribe to this mod's changes

embed is a skill published in the GitHub repository twelvelabs-io/twelve-labs-claude-code-plugin (23 stars, last pushed 3mo ago), licensed MIT. It adds 33 tokens to every session and 1,175 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Related

Other skills, from other repositories

agent-platform-rag-engine-management

Manage and query Agent Platform RAG Engine Corpora and retrieve grounded contexts using the Google GenAI SDK. Use when listing RAG corpora or files, inspecting a corpus, retrieving contexts, or generating content grounded in a RAG corpus. Do not use for standard database queries (use SQL/Spanner skills), Google…

google/skills · 85 tokens

agent-platform-model-registry

Agent Platform Model Registry Management. Use when you need to upload, list, describe, update, or delete machine learning models (and their versions) in the Agent Platform Model Registry. Don't use for model training, model deployment to endpoints, or managing non-Agent Platform models.

google/skills · 60 tokens

foundry-config-setup

Resolve missing setup caused by a hardcoded Foundry project endpoint or model in a sample. Use when a sample fails because it uses a placeholder/hardcoded projectendpoint (for example "https://your-project.services.ai.azure.com") or a hardcoded model instead of reading them from the environment.

microsoft/agent-framework · 65 tokens

google-cloud-solution-agentic-analytics-spark-knowledge-catalog

Discovers requirements and generates guidance to design and deploy a governed, secure agentic-analytics solution for data that's distributed across Google Cloud, other cloud providers, or on-premises. Data that's outside Google Cloud (such as data from Databricks, Snowflake, Salesforce, SAP, or Oracle systems) is…

google/skills · 138 tokens

training-check

Interactively monitor training metrics from the current Codex session, periodically checking WandB or fallback logs for NaN, divergence, plateaus, and broken runs.

wanshuiyin/Auto-claude-code-research-in-sleep · 35 tokens

nemo-automodel-launcher-config

Configure NeMo AutoModel job launches for interactive runs, Slurm clusters, and SkyPilot cloud execution.

NVIDIA/skills · 30 tokens