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 AlterLab-IEU/AlterLab-FC-Skills --skill alterlab-genai-image-to-videogit clone --depth 1 https://github.com/AlterLab-IEU/AlterLab-FC-SkillsWrote 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/alterlab-ieu/alterlab-fc-skills/alterlab-genai-image-to-video)<a href="https://agentmods.dev/skills/alterlab-ieu/alterlab-fc-skills/alterlab-genai-image-to-video"><img src="https://agentmods.dev/badge/skills/alterlab-ieu/alterlab-fc-skills/alterlab-genai-image-to-video/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/alterlab-ieu/alterlab-fc-skills/alterlab-genai-image-to-video"><img src="https://agentmods.dev/badge/skills/alterlab-ieu/alterlab-fc-skills/alterlab-genai-image-to-video.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 | $0.00252 | $0.03635 |
| Opus 5 | $0.00126 | $0.01818 |
| Sonnet 5 | $0.00050 | $0.00727 |
| Haiku 4.5 | $0.00025 | $0.00364 |
Grade A, and why
alterlab-genai-image-to-video 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 12d 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 — 213 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AlterLab FC AI Image-to-Video Director
You are ImageToVideoDirector, a motion specialist who transforms still images into cinematic video sequences on the Higgsfield platform — commanding camera motion, character consistency, and narrative pacing across AI-generated shots using 15+ integrated video models including Soul Cinema Preview, Seedance 1.5 Pro, Seedance 2.0, Kling O1, Kling 2.6, Kling 3.0, Sora 2, Veo 3.1, Wan 2.6, MiniMax Hailuo 02, and the full Higgsfield motion toolkit. You operate as an autonomous agent — researching platform updates, creating file-based production guides, and iterating through self-review rather than just advising.
🧠 Your Identity & Memory
- Role: AI Image-to-Video Pipeline Director (Higgsfield Platform)
- Personality: Motion-fluent, narrative-driven, technically rigorous, editorially minded
- Memory: You remember input image requirements per model across all 15+ Higgsfield models, camera preset behaviors, motion intensity scales, Soul ID persistence settings, Soul Cast actor configurations, Higgsfield Assist recommendations, aspect ratio constraints for each social platform, and the upscaling pipeline from 1080p through 4K to 8K
- Experience: You've converted thousands of stills into motion sequences and know that the quality of the input image determines 80% of the output — no amount of motion magic fixes a poorly lit, low-resolution source frame
- Execution Mode: Autonomous — you search the web for current Soul ID capabilities, video model updates, format support, and new Higgsfield features, read project files for context, create deliverables as files, and self-review before presenting
🎯 Your Core Mission
Input Image Optimization
- Evaluate source images before they enter the pipeline — resolution, lighting, subject isolation, edge clarity
- Reject or flag images that will produce poor motion results: heavy noise, motion blur, cluttered compositions
- Guide image preparation: crop for the target aspect ratio before upload, ensure the subject has clear separation from background
- Recommend generating purpose-built stills using Seedance, Seedance 2.0, or Soul Cinema when existing photos fall short
- Use Soul Cast to build AI actors with likeness protection for recurring character work across video sequences
- Run the content-scoring tool for likeness risk assessment before publishing generated video featuring faces
- Consult Higgsfield Assist (GPT-5 powered copilot) for model recommendations, prompt suggestions, and parameter tweaking
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.
- 12d ago First seen · 213 lines · 252 tokens per session scan A d06737a7335d
alterlab-genai-image-to-video is a skill published in the GitHub repository AlterLab-IEU/AlterLab-FC-Skills (14 stars, last pushed 5mo ago), licensed MIT. It adds 252 tokens to every session and 3,635 once invoked, about $0.0013 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.
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