cs-auto-videl

cs-auto-videl is a skill for Codex from ChenShuo2004/cs-skills. It costs 70 tokens per session (6,303 once invoked), scanned A, original, MIT.

A workflow for making Douyin and TikTok shopping videos, including competitor analysis, storyboards, prompt images, and video-generation packages.

In plain words
What is it for?
Analyzing benchmark videos, replacing products, creating nine-frame storyboards, preparing Seedance, Gemini Omni, or Google Flow/Veo prompts, submitting eligible generation tasks, and collecting results in Feishu.
Why use it?
It organizes the many steps needed to adapt a reference product video and prepare it for different video-generation services.

Skill for Codex

Written for Codex: agents/openai.yaml present. Also seen: mentions Codex; $skill-name invocation.

Good fit Analyzing benchmark videos, replacing products, creating nine-frame storyboards, preparing Seedance, Gemini Omni, or Google Flow/Veo prompts, submitting eligible generation tasks, and collecting results in Feishu.

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Install with agentmods
npx agentmods add skills/chenshuo2004/cs-skills/cs-auto-videl
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 ChenShuo2004/cs-skills --skill cs-auto-videl
Clone the repo
git clone --depth 1 https://github.com/ChenShuo2004/cs-skills

Made for: 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 cs-auto-videl

README.md
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Your own site
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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.

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Your own site · 80×15
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Per session 70 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 6,303 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. Third-party audits
  • NVIDIA SkillSpector warn 7 Sept 2026
SkillSpector: 2 findings, up to high

These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →

  • high System Prompt Leakage · line 125
    Skill contains instructions that could directly expose system prompts, internal rules, or hidden instructions to users or external parties.
    Fix: Remove any instructions that reveal, print, or output system prompts or internal rules. System instructions should never be exposed to end users.
  • high System Prompt Leakage · line 133
    Skill contains instructions that could directly expose system prompts, internal rules, or hidden instructions to users or external parties.
    Fix: Remove any instructions that reveal, print, or output system prompts or internal rules. System instructions should never be exposed to end users.
How audits are shown
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 $0.00070 $0.06303
Opus 5 $0.00035 $0.03152
Sonnet 5 $0.00014 $0.01261
Haiku 4.5 $0.00007 $0.00630

Measured 10d ago against content hash c67751849688, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

cs-auto-videl 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 10d ago.

The scan reads SKILL.md. This mod also ships 6 executable files (scripts/extract_copy_review_frames.py, scripts/seedance_submit.py, tests/test_google_flow_contract.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.

cs-auto-videl/SKILL.md · 244 lines

How it starts

The opening of the file, as written. The whole thing — 244 lines — stays where its author put it; the contents beside it link to each section on GitHub.

auto Videl

This is the user's local CS Auto Videl skill, invoked as $cs-auto-videl.

It runs Douyin/TikTok ecommerce information-feed video workflows. Use it when the user wants to analyze a benchmark short video, replace the product, generate actual storyboard images from prompt_image.md with image2, turn user-provided nine-frame storyboards into a final video, generate Seedance 2.0 prompts, prepare Gemini Omni or Google Flow/Veo first-frame-to-video packages, optionally submit Seedance API generation tasks with their own locally configured API key, or consolidate results into Feishu.

Platform Mode Selection

Before writing final video-generation deliverables, identify the target platform mode. If the user has not named a mode, ask which mode to use before producing final prompts or images:

  • seedance: Seedance/C端2.0 prompts, storyboard references, optional API dry-run or submission.
  • gemini-omni: 8-second first-frame-to-video units with first-frame images and Gemini Omni prompts.
  • google-flow: Google Flow/Veo first-frame-to-video units, normally 8 seconds each, with first-frame images, image prompts, video prompts, and optional voice/sound direction.
  • prompt-only: no image/video generation; deliver scripts, storyboard prompts, and platform-ready text only.

If the user explicitly says $cs-auto-videl google-flow, Google Flow, Flow, Veo, Frames to Video, or 首帧转视频, load references/google-flow-mode.md and use Google Flow mode. For Google Flow mode, default to first-frame-to-video unless the user explicitly asks for another Flow feature. If the user asks for Google Flow but not the clip duration, use 8 seconds per clip unless they ask for a supported shorter duration.

Route Selection

Before starting, choose exactly one route from the user's input.

  • 复刻链路: use the benchmark video as the source of shot order, timing, scenes, actions, copy rhythm, and composition. Generate storyboard images first, then generate Seedance prompts, and only call the API when the user's private key is configured.
  • 强 Hook 九宫格生成出片链路: use when the user gives product information, selling points, target audience, product images, or says to use prompt_image.md/Codex instead of Gemini to generate nine-grid prompts. Codex reads bundled references/prompts/00-prompt-image.md or the workspace prompt_image.md when the user explicitly points to it, generates a strong-Hook nine-grid Chinese storyboard script, waits for user confirmation, generates 9 image2 prompts, calls image2 to create 9 storyboard frames, then writes the Seedance/C端2.0 prompt and optionally calls Seedance. The expected handoff is prompt_image.md → image2 → Seedance, with 9帧分镜图 + 9段提示词 retained as core artifacts.
  • 九宫格成片直投链路: use when the user uploads 9 storyboard frames and 9 prompts that were already produced from the prompt_image.md system. Skip script generation and image2. Use the provided 9帧分镜图 + 9段提示词 to write the final Seedance/C端2.0 prompt, then optionally submit it through the API.
  • If the input contains a benchmark/competitor video, route to 复刻链路. If the input lacks a benchmark video and asks Codex to create the nine-grid prompts/images, route to 强 Hook 九宫格生成出片链路. If the input already contains 9 frames plus 9 prompts, route to 九宫格成片直投链路.
  • If multiple route signals are present, ask which route to use unless the user explicitly names one route.
  • Never mix routes inside one run. A replication run may produce storyboards, a generation run creates storyboards from prompt_image.md, and a direct-submit run starts after the 9 frames and 9 prompts already exist.

Read the full file on GitHub · 244 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. 10d ago First seen · 244 lines · 70 tokens per session scan A c67751849688

Subscribe to this mod's changes

cs-auto-videl is a skill published in the GitHub repository ChenShuo2004/cs-skills (142 stars, last pushed 7d ago), licensed MIT. It adds 70 tokens to every session and 6,303 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.

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