seedance-2.0 is an agent-driven production pipeline for creating AI films from text, images, videos, and references, including audio and platform-specific workflows. Filmmakers and creators use it to plan scenes and generate coherent prompts for Seedance-related tools, while the catalogue contains skills from that workflow.
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 Emily2040/seedance-2.0 --skill seedance-vocab-zhgit clone --depth 1 https://github.com/Emily2040/seedance-2.0Wrote 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/emily2040/seedance-2.0/seedance-vocab-zh)<a href="https://agentmods.dev/skills/emily2040/seedance-2.0/seedance-vocab-zh"><img src="https://agentmods.dev/badge/skills/emily2040/seedance-2.0/seedance-vocab-zh/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/emily2040/seedance-2.0/seedance-vocab-zh"><img src="https://agentmods.dev/badge/skills/emily2040/seedance-2.0/seedance-vocab-zh.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00055 | $0.01074 |
| Opus 5 | $0.00028 | $0.00537 |
| Sonnet 5 | $0.00011 | $0.00215 |
| Haiku 4.5 | $0.00006 | $0.00107 |
Grade A, and why
seedance-vocab-zh 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 4d 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 — 63 lines — stays where its author put it; the contents beside it link to each section on GitHub.
seedance-vocab-zh
Before producing prompt text, a prompt-ready block, a rewrite, an example, or a compiled clip, load the Director's Read, classify the brief, and complete its canonical narrative or non-narrative record. Translate that record into visible or audible carriers and keep its internal labels out of final generation prose.
Use Chinese vocabulary when the user asks for Chinese prompts, Mandarin cinematic wording, role binding, first/last-frame workflow, or maximum compactness. Chinese prompt wording is often efficient, but it must still preserve mode, reference tags, action, camera, lighting, audio, and constraints.
Intent
Chinese production wording can use compact compounds, but compression is useful only when the shot instruction remains explicit. Keep each concise phrase tied to a visible action, camera behavior, light source, sound, or preservation constraint. The shipped independent review artifact is empty, so treat these choices as working production wording pending locale-specialist review.
Usage Rule
Preserve actual reference tags exactly; their spelling is independent of the prompt language. Use short production phrases instead of abstract adjectives.
Before adapting a reference example below, load Using Reference Examples. Bind its placeholders to real assets by the requested role, then preserve the actual token, including its script, case, spacing and punctuation. A written example token does not attach a file.
Load vocab/zh for dense role-binding, first/last-frame, camera, lighting, audio, edit/extend, constraint, and safety vocabulary.
| Function | Chinese wording |
|---|---|
| Camera | 缓慢推镜, 横向跟拍, 固定中景, 低角度, 特写, 从剪影到正面四分之三角度 |
| Lighting | 侧逆光, 柔和窗光, 暖色实用灯, 冷色月光, 轮廓光, 体积光 |
| Motion | 慢慢转身, 快速掠过画面, 水珠沿表面下滑, 薄雾贴地扩散 |
| Audio | 安静环境声, 一句短对白, 轻微金属声, 无配乐, 脚步声卡点 |
| First/last frame | @图片1 为首帧, @图片2 为尾帧, 自然过渡到尾帧, 中间动作连续，不跳切 |
| Constraints | 严格保持logo、标签、形状和颜色不变 |
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.
- 4d ago Changed · +2 lines f6568a49e0c0
- 12d ago First seen · 61 lines · 55 tokens per session scan A a24b47bdcafb
seedance-vocab-zh is a skill published in the GitHub repository Emily2040/seedance-2.0 (7,279 stars, last pushed 4d ago), licensed MIT. It adds 55 tokens to every session and 1,074 once invoked, about $0.0003 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 skills, from other repositories
seedance-examples-zh
This skill should be used when the user asks for Chinese Seedance 2.0 examples, Chinese prompt patterns, example rewrites, or safe versions of working Chinese video-generation prompts.
seedance-vocab-en
This skill should be used when an English Seedance 2.0 prompt is slop-heavy, generic, padded with empty quality words, tripping false-positive filters, or needs precise English production vocabulary for camera, lighting, motion, VFX, audio, and constraints.
shortfilm-prompt
Generate cinematic AI shortfilm prompts (works with Seedance 2.0, Xiaoyunque, Sora, Kling, Jimeng, Veo) using the 5-stage structure from Mx-Shell's Zombie Scavenger. Trigger when the user wants transformation sequences, multi-shot narrative shorts, weapon-charge/combat segments, emotional family/pet/farewell…
guochao-visual-cn
A prompt-writing guide for generating Chinese-inspired illustrations and other visual designs. It distinguishes twelve Chinese artistic styles, such as ink painting, gongbi painting, blue-green landscapes, and Dunhuang murals.
seedance-prompt
This skill should be used when the user asks to write, improve, translate, compress, or debug a Seedance 2.0 video prompt; mentions T2V, I2V, V2V, R2V, camera direction, prompt quality, or provides reference assets for a production-ready prompt.
seedance-examples-ja
This skill should be used when the user asks for Japanese Seedance 2.0 examples, Japanese prompt patterns, example rewrites, or safe versions of working Japanese video-generation prompts.