video-agent-visual

video-agent-visual is a skill for Claude Code, Codex from chenyuxiaojin/video-agent-skills. It costs 102 tokens per session (1,703 once invoked), scanned A, original, MIT.

A video-asset workflow that reads a storyboard and generates the required still images through an image-generation API.

In plain words
What is it for?
Use it to create PNG images from storyboard.json, apply a visual style, choose concurrency and aspect ratio, and produce visual-timeline.json and visual-report.md.
Why use it?
It turns a shot list into organized visual files and supporting timeline and report documents, while leaving text-heavy or post-production shots out.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to create PNG images from storyboard.json, apply a visual style, choose concurrency and aspect ratio, and produce visual-timeline.json and visual-report.md.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/chenyuxiaojin/video-agent-skills/video-agent-visual
Install

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.

Any agent
npx skills add chenyuxiaojin/video-agent-skills --skill video-agent-visual
Clone the repo
git clone --depth 1 https://github.com/chenyuxiaojin/video-agent-skills

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for video-agent-visual

README.md
[![agentmods](https://agentmods.dev/badge/skills/chenyuxiaojin/video-agent-skills/video-agent-visual/github.svg)](https://agentmods.dev/skills/chenyuxiaojin/video-agent-skills/video-agent-visual)
Your own site
<a href="https://agentmods.dev/skills/chenyuxiaojin/video-agent-skills/video-agent-visual"><img src="https://agentmods.dev/badge/skills/chenyuxiaojin/video-agent-skills/video-agent-visual/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.

agentmods 80×15 button for video-agent-visual

Your own site · 80×15
<a href="https://agentmods.dev/skills/chenyuxiaojin/video-agent-skills/video-agent-visual"><img src="https://agentmods.dev/badge/skills/chenyuxiaojin/video-agent-skills/video-agent-visual.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 102 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,703 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00102 $0.01703
Opus 5 $0.00051 $0.00851
Sonnet 5 $0.00020 $0.00341
Haiku 4.5 $0.00010 $0.00170

Measured 11d ago against content hash 29bfef93736a, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

video-agent-visual 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 11d ago.

The scan reads SKILL.md. This mod also ships 7 executable files (scripts/align_timestamps.py, scripts/generate_image.py, scripts/generate_images.py, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

video-agent-visual/SKILL.md · 169 lines

How it starts

The opening of the file, as written. The whole thing — 169 lines — stays where its author put it; the contents beside it link to each section on GitHub.

video-agent-visual(美术师)

职责边界

美术师通过 GPTIMG2(gpt-image-2,OpenAI 兼容图片生成 API)批量生成 2K 图片素材:

  • ✅ 读取 storyboard.json,理解每个镜头的素材需求
  • ✅ 调用 generate_images.py 批量生成图片
  • ✅ 跳过后期制作镜头(数据/文字/分屏),交给剪辑师
  • ✅ 构建 visual-timeline.json(视觉时间轴)
  • ✅ 生成 visual-report.md(素材报告)
  • ❌ 设计画面内容(分镜师负责)
  • ❌ 构建 FCPXML 时间轴(剪辑师负责)

输入 → 输出

  • 输入:storyboard.json(分镜师产出)
  • 输出:visuals/*.png + visual-timeline.json + visual-report.md

执行方式

运行脚本

python scripts/generate_images.py <project_dir> [--style <风格>] [--concurrency <并发数>] [--aspect-ratio <比例>]

参数:

  • project_dir — 项目目录(包含 storyboard.json)
  • --style — 风格名称,对应 styles/ 目录下的文件(默认:default)
  • --concurrency — 并发数(默认:5)
  • --aspect-ratio — 宽高比(默认:16:9)

脚本会:

  1. 读取 storyboard.json
  2. 过滤掉 is_post_production: true 的镜头
  3. 加载风格指令(附加到每个 prompt 前)
  4. 并发调用 GPTIMG2(gpt-image-2)/v1/images/generations 生成 2K 图片(response_format=url,拿到 url 后下载落地)
  5. 输出图片到 visuals/ 目录(001.png, 002.png...)
  6. 生成失败自动重试 1 次
  7. 输出 visual-report.mdvisual-timeline.json

尺寸说明(2K)

--aspect-ratio 按下表映射到 GPTIMG2 的 2K 尺寸(边长对齐 16 的倍数):

宽高比 尺寸
16:9(默认) 2560x1440
9:16 1440x2560
1:1 2048x2048
4:3 2048x1536
3:4 1536x2048

未列出的比例兜底为 16:9(2560x1440)。当前 storyboard.json 结构不含比例字段,比例由 --aspect-ratio 参数决定。

风格配置

预置风格文件位于 styles/ 目录:

文件 说明 适用场景
default.txt 写实、电影感、自然光 通用
tech.txt 未来感、蓝色调、科技元素 AI / 科技类视频
knowledge.txt 温暖、清晰、学术感 认知 / 知识类视频

用户可新增自定义风格文件到 styles/ 目录,脚本会自动识别。

visual-timeline.json 格式

{
  "video_specs": {
    "resolution": "1920x1080",
    "fps": 30,
    "aspect_ratio": "16:9"
  },
  "total_duration": 490.0,
  "visuals": [
    {
      "shot": "001",
      "file": "visuals/001.png",
      "description": "A person scrolling through phone...",
      "start_time": 0.0,
      "end_time": 4.0,
      "duration": 4.0,
      "asset_type": "场景",
      "acquire_method": "ai_generate",
      "mood": "焦虑、快切"
    },
    {
      "shot": "006",
      "file": null,
      "description": "数据图表动效",
      "start_time": 12.0,
      "end_time": 14.0,
      "duration": 2.0,
      "asset_type": "数据",
      "acquire_method": "post_production",
      "mood": "冲击、停顿",
      "editor_note": "数据图表动效"
    }
  ]
}

Read the full file on GitHub · 169 lines

Changes

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.

  1. 11d ago First seen · 169 lines · 102 tokens per session scan A 29bfef93736a

Subscribe to this mod's changes

video-agent-visual is a skill published in the GitHub repository chenyuxiaojin/video-agent-skills (8 stars, last pushed 3mo ago), licensed MIT. It adds 102 tokens to every session and 1,703 once invoked, about $0.0005 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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