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 oyi77/1ai-skills --skill gengit clone --depth 1 https://github.com/oyi77/1ai-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/oyi77/1ai-skills/gen)<a href="https://agentmods.dev/skills/oyi77/1ai-skills/gen"><img src="https://agentmods.dev/badge/skills/oyi77/1ai-skills/gen/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/oyi77/1ai-skills/gen"><img src="https://agentmods.dev/badge/skills/oyi77/1ai-skills/gen.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 | $0.00069 | $0.02956 |
| Opus 5.5 | $0.00028 | $0.01182 |
| Sonnet 5.5 | $0.00014 | $0.00591 |
| Haiku 4.5 | $0.00007 | $0.00296 |
Grade B, and why
video-gen scanned grade B with 2 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 yesterday.
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.
Sends data to an external URLmediumData exfiltration
A POST to an outside endpoint may be telemetry or may be exfiltration; either way the mod talks to somewhere, and you should know where.
response = requests.post( "https://api.dev.runwayml.com/v1/image_to_video", Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
response = requests.post( How it starts
The opening of the file, as written. The whole thing — 319 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Video Generation
Generate videos from text prompts or images using diffusion-based models. This is the umbrella skill for all AI video generation providers.
Companion skills: video-editor · remotion · faceless-youtube · auto-clipper · b-roll-finder
When to Use
Trigger phrases:
- "generate a video" · "make a video from text" · "text to video" · "image to video"
- "AI video" · "animate this image" · "create a video clip" · "video generation"
- "seedance" · "即梦" · "runway" · "kling video" · "sora" · "grok video" · "pika"
- "extend this video" · "continue this video" · "video from prompt"
Use cases:
- Generate short video clips (4-60s) from text descriptions
- Animate still images into dynamic video
- Extend existing videos with AI-generated continuation
- Create product demos, ads, and social media clips
- Generate B-roll footage for longer productions
- Multi-modal generation (image + video + audio references)
When NOT to use:
- For programmatic/data-driven video (use remotion)
- For post-production editing of existing footage (use video-editor)
- For building YouTube channels at scale (use faceless-youtube)
- When you need frame-precise control (use remotion)
Overview
AI video generation creates video content from text prompts or images using diffusion-based models. Different providers excel at different tasks — this skill routes to the right one.
Model Selection Guide
| Model | Best For | Duration | Audio | Install |
|---|---|---|---|---|
| Seedance 2.0 | Multi-modal cinematic, lip-sync, Chinese prompts | 4-15s | ✅ Native | jimeng.jianying.com |
| Kling 3.0 | 4K quality, multi-shot identity consistency | 5-10s | ❌ | api.klingai.com |
| Runway Gen-3 | General purpose, fast iteration | 5-10s | ❌ | runwayml.com |
| Grok Imagine | Quick social content, synchronized audio | 6-10s | ✅ Native | Grok app (Super Grok) |
| Veo 3-1 | Physics-respecting, video extension | 5-8s | ✅ | RunComfy CLI |
| Wan 2.7 | Audio-driven lip-sync, multi-language | 5-10s | ✅ | RunComfy CLI |
| Pika | Creative, stylized short clips | 3-5s | ❌ | pika.art |
| Sora | High-fidelity, complex scenes | 5-20s | ❌ | OpenAI API |
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.
- yesterday Changed 942b578e4b22
- 1mo ago First seen · 319 lines · 69 tokens per session scan B f8e1141ce894
video-gen is a skill published in the GitHub repository oyi77/1ai-skills (17 stars, last pushed yesterday), licensed MIT. It adds 69 tokens to every session and 2,956 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it B with 2 findings (sends data to an external url, makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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video-podcast-maker-nano
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