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 agentmods add commands/dojocodinglabs/remotion-superpowers/analyze-footagegit clone --depth 1 https://github.com/DojoCodingLabs/remotion-superpowersWrote 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/commands/dojocodinglabs/remotion-superpowers/analyze-footage)<a href="https://agentmods.dev/commands/dojocodinglabs/remotion-superpowers/analyze-footage"><img src="https://agentmods.dev/badge/commands/dojocodinglabs/remotion-superpowers/analyze-footage.svg" alt="Measured on agentmods" 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 | $0.00036 | $0.00847 |
| Opus 5 | $0.00018 | $0.00424 |
| Sonnet 5 | $0.00007 | $0.00169 |
| Haiku 4.5 | $0.00004 | $0.00085 |
Grade A, and why
analyze-footage scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 117 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Analyze Footage — AI Video Understanding
You are helping the user understand their existing video files using TwelveLabs MCP. This gives Claude "eyes" to see what's in video footage.
Load the remotion-production skill for the video-analysis rule.
Workflow
1. Identify Video Files
Ask the user which video files to analyze, or look for them:
# Check common locations
ls public/footage/ public/raw/ public/video/ public/ 2>/dev/null | grep -E '\.(mp4|mov|avi|mkv|webm)$'
If no files found, ask the user where their footage is located.
2. Index with TwelveLabs
For each video file:
Use TwelveLabs MCP to:
1. Create an index (or use existing one)
2. Upload/index the video file
3. Wait for indexing to complete
Tell the user indexing may take a few minutes for longer videos.
3. Analyze Content
Run these analyses and present results:
Scene Breakdown:
Request: "List all distinct scenes with timestamps and descriptions"
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 Detected:
- People: 1 speaker, audience (~30 people)
- Objects: laptop, projector screen, podium, microphone
- Text on screen: company logo, product name
- Setting: conference room, professional lighting
4. Targeted Search (if user has specific needs)
Ask if they want to find specific moments:
Use TwelveLabs semantic search:
- "Find the part where the product is demonstrated"
- "Show me all outdoor scenes"
- "Find close-up shots"
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
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.
- 6d ago First seen · 117 lines · 36 tokens per session scan A 559fd212566e
analyze-footage is a command published in the GitHub repository DojoCodingLabs/remotion-superpowers (118 stars, last pushed 6mo ago), licensed MIT. It adds 36 tokens to every session and 847 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.
Other commands, from other repositories
brand-setup
Configure a new brand profile with colors, fonts, logo, visual style, platforms, and compliance rules.
status
Show current production status for the active brand and month.
index-assets
Index or re-index a brand's visual asset library.
check-approvals
Check pending approval status and send reminders if overdue.
switch-brand
Switch the active brand context.
new-month
Start a new month's social media calendar production for a brand.