analyze-footage

analyze-footage is a skill for Claude Code from DojoCodingLabs/remotion-superpowers. It costs 47 tokens per session (1,418 once invoked), scanned A, original, MIT.

A video-analysis workflow for examining existing footage with TwelveLabs AI, a service that understands video content. It creates a timestamped map of scenes and records details such as descriptions, people, text on screen, and whether a shot is usable.

In plain words
What is it for?
Use it to organize raw video, inspect shots across a collection, ask about footage, and select clips for a Remotion project.
Why use it?
It removes the need to review footage manually just to understand what each section contains. Timestamps make useful scenes easier to find for later editing.

Skill for Claude Code

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

Part of the remotion-superpowers plugin — 15 skills, 3 agents, 2 hooks, 5 MCP servers shipped together

Good fit Use it to organize raw video, inspect shots across a collection, ask about footage, and select clips for a Remotion project.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/dojocodinglabs/remotion-superpowers/analyze-footage
Install

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.

Any agent
npx skills add DojoCodingLabs/remotion-superpowers --skill analyze-footage
Clone the repo
git clone --depth 1 https://github.com/DojoCodingLabs/remotion-superpowers

Made for: Claude Code.

Or install remotion-superpowers, the plugin that ships this one along with the rest of its 15 skills, 3 agents, 2 hooks, 5 MCP servers.

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 analyze-footage

README.md
[![agentmods](https://agentmods.dev/badge/skills/dojocodinglabs/remotion-superpowers/analyze-footage/github.svg)](https://agentmods.dev/skills/dojocodinglabs/remotion-superpowers/analyze-footage)
Your own site
<a href="https://agentmods.dev/skills/dojocodinglabs/remotion-superpowers/analyze-footage"><img src="https://agentmods.dev/badge/skills/dojocodinglabs/remotion-superpowers/analyze-footage/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.

agentmods 80×15 button for analyze-footage

Your own site · 80×15
<a href="https://agentmods.dev/skills/dojocodinglabs/remotion-superpowers/analyze-footage"><img src="https://agentmods.dev/badge/skills/dojocodinglabs/remotion-superpowers/analyze-footage.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 47 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,418 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 $0.00047 $0.01418
Opus 5.5 $0.00019 $0.00567
Sonnet 5.5 $0.00009 $0.00284
Haiku 4.5 $0.00005 $0.00142

Measured today against content hash a3356bfadc70, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

analyze-footage 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 today.

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/analyze-footage/SKILL.md · 137 lines

How it starts

The opening of the file, as written. The whole thing — 137 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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

Read the full file on GitHub · 137 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. today First seen · 137 lines · 47 tokens per session scan A a3356bfadc70

Subscribe to this mod's changes

analyze-footage is a skill published in the GitHub repository DojoCodingLabs/remotion-superpowers (145 stars, last pushed yesterday), licensed MIT. It adds 47 tokens to every session and 1,418 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-10-28.

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