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 mediastormDev/dream-to-video-skill --skill dream-to-videogit clone --depth 1 https://github.com/mediastormDev/dream-to-video-skillWrote 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/mediastormdev/dream-to-video-skill/dream-to-video)<a href="https://agentmods.dev/skills/mediastormdev/dream-to-video-skill/dream-to-video"><img src="https://agentmods.dev/badge/skills/mediastormdev/dream-to-video-skill/dream-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/mediastormdev/dream-to-video-skill/dream-to-video"><img src="https://agentmods.dev/badge/skills/mediastormdev/dream-to-video-skill/dream-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 | $0.00126 | $0.05064 |
| Opus 5 | $0.00063 | $0.02532 |
| Sonnet 5 | $0.00025 | $0.01013 |
| Haiku 4.5 | $0.00013 | $0.00506 |
Grade A, and why
dream-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 — 306 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Dream-to-Video 梦境素材全自动视频生成
你负责将用户提供的梦境文字素材转化为视频提示词,并通过自动化工具链提交到即梦平台生成视频、下载到本地。用户只需要给你文字,你最终返回视频文件。
零、首次环境配置
当用户首次使用此 Skill 时,按以下流程自动配置环境。每一步执行后检查结果,全部通过后再告知用户可以开始使用。
路径约定
本文档中 {W} 代表本仓库的 clone 路径(即 dream_to_video/ 目录的父级)。
- 项目目录:
{W}/dream_to_video/ - Skill 资源目录:
{W}/skills/dream-to-video/
执行命令时,将 {W} 替换为实际路径。
自动执行(无需用户操作)
Step 0-0:克隆项目仓库 询问用户希望把项目放在哪个目录,然后执行:
cd "<用户指定的目录>" && git clone https://github.com/mediastormDev/dream-to-video-skill.git && cd dream-to-video-skill
clone 完成后的 dream-to-video-skill 目录即为 {W}。
如果用户说"就在当前目录",则在当前目录下 clone。
Step 0-1:检查 Python
python --version
要求 Python ≥ 3.10。如果未安装或版本过低,停下来提示用户:
请先安装 Python 3.10 或更高版本:https://www.python.org/downloads/ 安装时勾选「Add Python to PATH」。
Step 0-2:安装 Python 依赖
cd "{W}/dream_to_video" && pip install -r requirements.txt
Step 0-3:安装 Playwright 浏览器
playwright install chromium
这会下载 Chromium 浏览器引擎(约 150MB),用于自动操控即梦平台。
Step 0-4:创建必要目录
cd "{W}/dream_to_video" && mkdir -p output data auth/browser_profile reference_images/室内 reference_images/室外
Step 0-5:部署参考图素材
仓库内置参考图位于 skills/dream-to-video/reference_images/,自动复制到项目运行目录:
cp -n "{W}/skills/dream-to-video/reference_images/室内/"*.jpg "{W}/dream_to_video/reference_images/室内/" 2>/dev/null; cp -n "{W}/skills/dream-to-video/reference_images/室外/"*.jpg "{W}/dream_to_video/reference_images/室外/" 2>/dev/null; echo "reference images deployed"
-n不覆盖已存在的文件。如果用户有自己的公司环境照片,可以额外放入对应目录。
需要用户操作
Step 0-6:即梦平台登录
cd "{W}/dream_to_video" && python main.py login
执行后浏览器会打开即梦网站并显示登录二维码。提示用户:
请用手机抖音/即梦 App 扫描浏览器中的二维码完成登录。登录成功后程序会自动保存凭证,后续无需重复登录。
自检清单
所有步骤完成后,依次运行以下检查,全部 ✅ 才算配置成功:
# 检查 1:Python 版本
python --version
# 期望:Python 3.10+
# 检查 2:核心依赖
python -c "import playwright; import cv2; import numpy; print('deps OK')"
# 期望:输出 deps OK
# 检查 3:Playwright 浏览器
python -c "from playwright.sync_api import sync_playwright; b=sync_playwright().start(); br=b.chromium.launch(headless=True); br.close(); b.stop(); print('browser OK')"
# 期望:输出 browser OK
# 检查 4:目录结构
python -c "from pathlib import Path; dirs=['output','data','auth/browser_profile']; ok=all((Path('{W}/dream_to_video')/d).is_dir() for d in dirs); print('dirs OK' if ok else 'dirs MISSING')"
# 期望:输出 dirs OK
# 检查 5:登录状态
cd "{W}/dream_to_video" && python main.py verify
# 期望:显示登录有效
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.
- reference_images/室内/1a116321-0a80-4319-9b4b-623c972d6d8d.jpg 101 KB
- reference_images/室内/8b13bd55-faa7-451f-ab65-09781d4333ee.jpg 78 KB
- reference_images/室内/949aced4-9827-4f29-ad12-d23e69756242.jpg 85 KB
- reference_images/室内/a16a3797-d691-4208-b741-61d465dcfd07.jpg 117 KB
- reference_images/室内/ca0678dc-ffab-436e-a177-d8e9d1779871.jpg 94 KB
- reference_images/室内/e49295b2-8a8e-43a2-aa26-ceb53ec15b27.jpg 106 KB
- reference_images/室外/1690c1fc-8423-4374-b59b-b5bea3675781.jpg 145 KB
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 · 306 lines · 126 tokens per session scan A 11f9be5dde27
dream-to-video is a skill published in the GitHub repository mediastormDev/dream-to-video-skill (360 stars, last pushed 5mo ago), licensed MIT. It adds 126 tokens to every session and 5,064 once invoked, about $0.0006 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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