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 full-aigc-skills/jimeng-skills --skill jimeng-prompt-image2videogit clone --depth 1 https://github.com/full-aigc-skills/jimeng-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/full-aigc-skills/jimeng-skills/jimeng-prompt-image2video)<a href="https://agentmods.dev/skills/full-aigc-skills/jimeng-skills/jimeng-prompt-image2video"><img src="https://agentmods.dev/badge/skills/full-aigc-skills/jimeng-skills/jimeng-prompt-image2video/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/full-aigc-skills/jimeng-skills/jimeng-prompt-image2video"><img src="https://agentmods.dev/badge/skills/full-aigc-skills/jimeng-skills/jimeng-prompt-image2video.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 | $0.00234 | $0.02364 |
| Opus 5 | $0.00117 | $0.01182 |
| Sonnet 5 | $0.00047 | $0.00473 |
| Haiku 4.5 | $0.00023 | $0.00236 |
Grade A, and why
jimeng-prompt-image2video 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 — 166 lines — stays where its author put it; the contents beside it link to each section on GitHub.
jimeng-prompt-image2video — 即梦图生视频提示词
Craft prompts for animating reference images into videos using Dreamina's 4 video modes.
When to use this skill
Use this skill when the user:
- Provides image(s) and wants to generate a video from them ("把这张图做成视频")
- Mentions 图生视频, image2video, 图片转视频, 让图动起来
- Describes a specific sub-mode: 单图生视频, 首尾帧, 多帧故事, 全能参考
- Has multiple images and wants to create a narrative video
- Asks how to describe motion from a static reference image
Do NOT use for:
- Text-to-video (no reference images) → use jimeng-prompt-text2video
- CLI execution → use jimeng-cli-image2video
- Image-to-image editing (output is still an image) → use jimeng-prompt-image2image
Core Methodology
Image-to-video prompting is fundamentally incremental: the reference image already provides the visual content. The prompt only needs to describe what MOVES and CHANGES.
[从哪里开始] + [什么在动/怎么动] + [运镜方式] + [环境/光影变化] + [时长/节奏] + [风格一致]
The I2V Golden Rule
Don't describe what's already in the image. Only describe what happens next.
- ✗ "一个穿红裙子的女孩站在花园里，阳光灿烂..." — the image already shows this
- ✓ "女孩缓缓转身面向镜头，红裙随风飘动，镜头慢慢推近" — this is what happens NEXT
4 Sub-Modes Overview
| # | Mode | Input | Core Prompt Focus | Example File |
|---|---|---|---|---|
| 1 | 单图生视频 | 1 image | "从这张图开始，接下来发生什么运动" | examples/single-image.md |
| 2 | 首尾帧 | 2 images | "如何从图A过渡到图B" | examples/first-last-frame.md |
| 3 | 多帧故事 | 2-20 images | "帧与帧之间的因果关系和时间流逝" | examples/multi-frame.md |
| 4 | 全能参考 | images+video+audio | "综合各参考素材，生成目标视频" | examples/multimodal-ref.md |
How to use this skill
Step 1: Identify the sub-mode
Ask the user or infer from context:
- Has 1 image → Sub-mode 1 (单图生视频)
- Has 2 images described as start/end → Sub-mode 2 (首尾帧)
- Has 2+ images forming a sequence/storyboard → Sub-mode 3 (多帧故事)
- Has mixed media (images + video + audio) → Sub-mode 4 (全能参考)
Step 2: Load reference materials
Load the matched example file from examples/ for sub-mode-specific patterns.
What ships with it
7 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 166 lines · 234 tokens per session scan A df2e59072eab
jimeng-prompt-image2video is a skill published in the GitHub repository full-aigc-skills/jimeng-skills (3 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 234 tokens to every session and 2,364 once invoked, about $0.0012 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 2026-08-31.
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