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 DojoCodingLabs/remotion-superpowers --skill 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/skills/dojocodinglabs/remotion-superpowers/analyze-footage)<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.
<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>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 | $0.00047 | $0.01418 |
| Opus 5.5 | $0.00019 | $0.00567 |
| Sonnet 5.5 | $0.00009 | $0.00284 |
| Haiku 4.5 | $0.00005 | $0.00142 |
Grade A, and why
analyze-footage scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured today.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 137 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Analyze Footage — AI Video Understanding
You are helping the user understand their existing video files using TwelveLabs MCP. This gives your agent "eyes" to see what's in video footage.
Load the remotion-production skill for the video-analysis rule.
Workflow
1. Identify Video Files
Ask the user which video files to analyze, or look for them:
# Check common locations
ls public/footage/ public/raw/ public/video/ public/ 2>/dev/null | grep -E '\.(mp4|mov|avi|mkv|webm)$'
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
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.
- today First seen · 137 lines · 47 tokens per session scan A a3356bfadc70
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.
Other skills, from other repositories
higgsfield-comic-to-video
Animates comic panels, manga pages, webtoons, illustrated storyboards, and sequential art into video using Higgsfield. Routes to wan26 (primary, I2V — image required) or seedance20 (fallback). Use when the user wants to animate comics, bring illustrations to life, convert manga to video, animate storyboards, or create…
higgsfield-anime-action
Generates anime-style video from a user-supplied reference frame using Higgsfield. Routes to wan27 (primary, I2V via startimage) or wan26 (fallback, I2V via image role). Use whenever the user wants anime, Japanese animation style, shonen action, seinen drama, magical girl, mecha, isekai, slice-of-life anime, or any…
higgsfield-product-360
Generates product 360° turntable, multi-angle showcase, and product reveal video prompts for Higgsfield. A product photo is always required (image-to- video). Routes to seedance20 (primary) or cinematicstudio30 (fallback). Use when the user wants a product rotation video, turntable showcase, product reveal, 360 view…
higgsfield-music-video
Generates music video and beat-synced visual content prompts for Higgsfield. An audio track is always required. Routes to veo31 (primary, native audio) or veo31lite (fallback). Use when the user wants a music video, lyric video, beat-synced visuals, performance video, concert visual, album art animation, or any…
higgsfield-cinematic
Crafts film-grade cinematic video prompts for Higgsfield and routes to the best model (cinematicstudio30 primary, veo31 fallback). Use when the user wants cinematic, film look, movie scene, dramatic lighting, depth of field, lens flare, anamorphic, letterbox, noir, epic, Steadicam, dolly, crane shot, or any…
higgsfield-3d-cgi
Crafts 3D CGI and rendered video prompts for Higgsfield and routes to the best model (seedance20 primary, wan27 fallback). Use when the user wants 3D rendered, CGI, Pixar-style, Unreal Engine, photorealistic 3D, computer- generated, or digitally rendered video. Triggers on: 3D animation, CGI, rendered, Blender, Unreal…