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analyze-footage

analyze-footage

github.com/dojocodinglabsCreative & media16664 installsUpdated 2w ago
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About analyze-footage

analyze-footage is a Remotion Superpowers skill for understanding existing video files, aimed at video workflows that need scene analysis and clip selection. It uses TwelveLabs MCP to obtain scene and timing information and identify ranges for editing recommendations.

Key features

  1. 01Upload footage and analyze its content
  2. 02Create timestamped scene breakdowns
  3. 03Find video segments from a specific question
  4. 04Search for relevant segments across a video library
  5. 05Recommend clip ranges from analysis

Use cases

  1. 01Organize scenes and shots in long videos
  2. 02Find product-demo footage for a promo
  3. 03Locate specific content in a footage library
  4. 04Use recommended ranges in Remotion edits

MCP support

Uses TwelveLabs MCP to upload and analyze videos, and can index footage for searches across videos.

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/dojocodinglabs/remotion-superpowers --skill analyze-footage

After installing, type/analyze-footagein Claude Code to confirm it loaded.

Details

From the project README

Analyze Footage — AI Video Understanding

You are helping the user understand their existing video files using TwelveLabs MCP. This gives your agent "eyes" to see what's in video footage.

Load the remotion-production skill for the video-analysis rule.

Workflow

1. Identify Video Files

Ask the user which video files to analyze, or look for them:

# Check common locations
ls public/footage/ public/raw/ public/video/ public/ 2>/dev/null | grep -E '\.(mp4|mov|avi|mkv|webm)$'

If no files found, ask the user where their footage is located.

2. Upload to TwelveLabs

For each video file, upload it as an asset (no index needed for analysis):

create-asset:
  file: [absolute path, e.g. /path/to/project/public/footage/source-video.mp4]

Keep the returned asset ID. If the next call says the asset isn't ready yet, wait a few seconds and retry.

3. Analyze Content

Scene Breakdown — a timestamped scene map, as the video-analysis rule shows:

async-analyse-video:
  assetId: [asset ID]
  modelName: "pegasus1.5"
  analysisMode: "time_based_metadata"
  segmentDefinitions: [one "shots" definition with fields: description, shot_type, people, on_screen_text, usable]

Poll get-analyse-task every 5-10 seconds until it's ready, then parse result.data (a JSON string keyed by the segment definition id). Videos up to 2 hours work.

For a direct question ("which shots show the product?", "pick clips for a 30s promo"), use sync-analyse-video (assetId, modelName: "pegasus1.5", prompt, and a jsonSchema so you get start/end times back as numbers). Sync analysis is capped at 1 hour of video.

Always pass modelName: "pegasus1.5": the server's default model has been removed from the API.

Present as:

📹 Video Analysis: [filename]

Scenes:
  0:00 - 0:15  │ Logo animation, dark background
  0:15 - 0:45  │ Speaker at podium, introducing topic, conference setting
  0:45 - 1:20  │ Product demo on laptop screen, close-up
  1:20 - 1:45  │ Audience reaction, medium shot, clapping
  1:45 - 2:00  │ Call to action slide, company branding

Key Elements (from the segment fields):
  - People: 1 speaker, audience (~30 people)
  - Shot types: wide, medium, close-up, screen capture
  - Text on screen: company logo, product name
  - Setting: conference room, professional lighting

When the results are saved (e.g. to analysis/[filename].json), clean up with delete-asset (assetId). Assets count against the account's storage.

4. Targeted Search (if user has specific needs)

For one file, ask sync-analyse-video directly (optionally clipped with startTime/endTime). To find moments across a library of videos, index it once and search:

start-video-indexing-task:
  folderFilePath: [absolute path to the footage folder]   (or videoFilePath for one file)
→ poll get-indexed-asset until ready
search:
  query: "the part where the product is demonstrated"

search returns ranked segments with start/end times and thumbnails; it doesn't describe them. Analyse a hit if you need to know more.

5. Recommend Clips

Based on the analysis, recommend the best clips for the user's video:

🎬 Recommended Clips for a 30-second promo:

1. Opening (0:00-0:05)
   → Use 0:45-0:50 of source — product demo close-up, high energy
   
2. Main content (0:05-0:20)  
   → Use 0:50-1:05 of source — full demo walkthrough
   
3. Social proof (0:20-0:25)
   → Use 1:20-1:25 of source — audience clapping
   
4. CTA (0:25-0:30)
   → Use 1:45-1:50 of source — branded slide
6. Show Usage in Remotion

For each recommended clip, show the Remotion code. A source range becomes trimBefore (the start timestamp) plus durationInFrames (the clip length) on <Video> from @remotion/media. Cut the clips together with TransitionSeries or a Track as the official remotion-markup skill shows (video-editing.md); skills/remotion-production/rules/video-analysis.md has a full example.

import { Video } from "@remotion/media";
import { staticFile, useVideoConfig } from "remotion";

const { fps } = useVideoConfig();

// Opening (0:00-0:05) ← source 0:45-0:50
<Video
  name="Opening"
  src={staticFile("footage/source-video.mp4")}
  trimBefore={45 * fps}
  durationInFrames={5 * fps}
  objectFit="cover"
  style={{ width: "100%", height: "100%" }}
/>

Round fractional TwelveLabs timestamps to whole frames: Math.round(45.6 * fps).

Tips

  • Ensure video files are in public/ (ideally public/footage/) so Remotion can access them via staticFile() with the path relative to public/; pass the absolute path of the same file to create-asset
  • Limits: sync analysis up to 1 hour, async up to 2 hours, direct uploads up to 4 GB
  • Index only for search; an index is reusable across many searches, an asset is per analysis (delete it afterwards)
  • For anything else, see the official docs: https://docs.twelvelabs.io (and llms.txt)
  • Combine analyzed footage with stock clips from Pexels to fill gaps

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