From twelvelabs
Analyzes a video synchronously using TwelveLabs AI to return a summary or answer questions about its content. Accepts video URLs, file paths, asset IDs, or indexed video IDs.
How this skill is triggered — by the user, by Claude, or both
Slash command
/twelvelabs:sync-analyzeThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Analyse a video using TwelveLabs AI and return the result inline. Works on:
Analyse a video using TwelveLabs AI and return the result inline. Works on:
videoId (legacy, Pegasus 1.2 only).assetId (Pegasus 1.5).For videos longer than 1 hour, or for time-based metadata extraction, use the async-analyze skill instead.
The user wants a quick text/JSON answer about a single video and:
For multi-segment structured extraction (e.g. "find every product mention with timestamps"), use the async-analyze skill — that supports analysisMode: "time_based_metadata".
| Input type | modelName |
|---|---|
videoId | pegasus1.2 (forced — the MCP rejects 1.5 + videoId) |
| Anything else | pegasus1.5 |
Only override to pegasus1.2 when the user explicitly says so ("use 1.2", "legacy model").
Look at what the user gave you:
videoId. Force modelName: "pegasus1.2".http(s)://... URL → videoUrl. Use modelName: "pegasus1.5".asset_* referred to as an "asset" or "uploaded thing" → assetId. modelName: "pegasus1.5".Tool: mcp__twelvelabs-mcp__create-asset
Parameters:
file: "<absolute-path>"
Capture the returned assetId. Tell the user the asset was created and that they can reuse or delete it via /twelvelabs:assets. Then continue as if they had given you an assetId.If the user gave a question, use it as prompt. Otherwise default to:
"Provide a comprehensive summary of this video including key moments, topics covered, and important details."
Set exactly one source (videoId, videoUrl, assetId, or base64Video) plus modelName and prompt:
Tool: mcp__twelvelabs-mcp__sync-analyse-video
Parameters:
<one of: videoId | videoUrl | assetId | base64Video>: "..."
modelName: "pegasus1.5" # or "pegasus1.2" only for videoId / explicit user override
prompt: "..."
Add these only when the user asks for the corresponding behavior:
temperature (0-1, default 0.2) — "more creative" / "more deterministic"maxTokens — long outputsjsonSchema — "give me JSON"; provide a JSON Schema (Draft 2020-12); cannot combine with promptV2startTime / endTime (Pegasus 1.5 only) — clip to a sub-range; end - start ≥ 4promptV2 (Pegasus 1.5 only, mutually exclusive with prompt) — structured prompt with image references via <@name> and a mediaSources array (each: { name, mediaType: "image", url | assetId | base64String })Video Analysis
<analysis output>
Video: <best identifier — filename, URL, asset ID, or videoId>
Model: pegasus1.5 (or pegasus1.2)
videoId is not accepted with modelName='pegasus1.5' → you misclassified; either drop modelName (server will fall back) or switch to pegasus1.2.Provide exactly one of videoId, videoUrl, assetId, or base64Video → you set zero or multiple sources; re-classify.create-asset returns status pending initially)."What is this video about?" (after listing videos and the user picked one)
→ sync-analyse-video with videoId + Pegasus 1.2, generic summary prompt.
"Analyse https://example.com/clip.mp4 and tell me what happens"
→ sync-analyse-video with videoUrl + Pegasus 1.5.
"Summarise /Users/me/keynote.mp4"
→ create-asset with file: "/Users/me/keynote.mp4" → sync-analyse-video with the new assetId + Pegasus 1.5.
"What products are mentioned in this video? Return JSON: { product, timestamp_sec }[]"
→ sync-analyse-video with the appropriate source + jsonSchema set to an array schema.
videoId but is capped at Pegasus 1.2 capabilities.npx claudepluginhub twelvelabs-io/twelve-labs-claude-code-plugin --plugin twelvelabsAsynchronously analyzes up to 2-hour videos by URL, file upload, or asset ID using Pegasus 1.5. Supports time-based metadata extraction, multimodal reference images and structured JSON output.
Analyzes video files or YouTube URLs: extracts frames/audio, detects scenes/motion/silence/transitions via ffmpeg tools with structured workflow.
Imports, searches, and analyzes videos from YouTube, TikTok, Instagram using Memories.ai LVMM for persistent intelligence, summarization, knowledge bases, and social trends research.