Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add twelvelabs-io/twelve-labs-claude-code-plugin --skill image-searchgit clone --depth 1 https://github.com/twelvelabs-io/twelve-labs-claude-code-pluginWrote 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.
[](https://agentmods.dev/skills/twelvelabs-io/twelve-labs-claude-code-plugin/image-search)<a href="https://agentmods.dev/skills/twelvelabs-io/twelve-labs-claude-code-plugin/image-search"><img src="https://agentmods.dev/badge/skills/twelvelabs-io/twelve-labs-claude-code-plugin/image-search/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/twelvelabs-io/twelve-labs-claude-code-plugin/image-search"><img src="https://agentmods.dev/badge/skills/twelvelabs-io/twelve-labs-claude-code-plugin/image-search.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00051 | $0.00987 |
| Opus 5 | $0.00026 | $0.00494 |
| Sonnet 5 | $0.00010 | $0.00197 |
| Haiku 4.5 | $0.00005 | $0.00099 |
Grade A, and why
image-search 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 10d 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.
How it starts
The opening of the file, as written. The whole thing — 127 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Composed Text + Image Search
Search indexed videos using an image combined with optional text for more precise results. The text narrows image-based results — for example, an image of a car plus "red color" finds only red versions of that car model.
When to Use This Skill
Use this skill when the user:
- Provides a reference image and wants to find similar content in videos
- Wants to combine an image with text to refine results ("find this but in blue")
- Asks to search with a picture or screenshot
- Wants to locate specific objects/scenes matching a visual reference
Prerequisites
- Videos must be indexed with Marengo 3.0 (composed search requires Marengo 3.0)
- A reference image: either a publicly accessible URL or a local file path
Instructions
Step 1: Get the Image and Query
From the user's request, extract:
- Image: A publicly accessible URL or an absolute path to a local image file
- Text query (optional): Additional text to refine the image search
Step 2: Call the Search Tool
Image-only search (find content similar to the image):
With a URL:
Tool: mcp__twelvelabs-mcp__search
Parameters:
queryMediaUrl: "<image URL>"
queryMediaType: "image"
With a local file:
Tool: mcp__twelvelabs-mcp__search
Parameters:
queryMediaFile: "<absolute path to image file>"
queryMediaType: "image"
Composed search (image + text refinement):
With a URL:
Tool: mcp__twelvelabs-mcp__search
Parameters:
query: "<text description>"
queryMediaUrl: "<image URL>"
queryMediaType: "image"
With a local file:
Tool: mcp__twelvelabs-mcp__search
Parameters:
query: "<text description>"
queryMediaFile: "<absolute path to image file>"
queryMediaType: "image"
To search a specific index, add indexId.
Step 3: Display Results
Format results the same way as regular text search:
For each video with matching segments, display:
- Filename of the video (if available)
- URL of the video stream (if available)
- Bulleted list of start and end times for matching segments
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.
- 10d ago First seen · 127 lines · 51 tokens per session scan A e44941aeb026
image-search 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 51 tokens to every session and 987 once invoked, about $0.0003 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.
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