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 HE1780/deer-flow-by-cc --skill video-generationgit clone --depth 1 https://github.com/HE1780/deer-flow-by-ccWrote 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/he1780/deer-flow-by-cc/video-generation)<a href="https://agentmods.dev/skills/he1780/deer-flow-by-cc/video-generation"><img src="https://agentmods.dev/badge/skills/he1780/deer-flow-by-cc/video-generation/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/he1780/deer-flow-by-cc/video-generation"><img src="https://agentmods.dev/badge/skills/he1780/deer-flow-by-cc/video-generation.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 | $0.00029 | $0.01101 |
| Opus 5 | $0.00015 | $0.00550 |
| Sonnet 5 | $0.00006 | $0.00220 |
| Haiku 4.5 | $0.00003 | $0.00110 |
Grade A, and why
video-generation 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.
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.
This is a copy
91% identical to video-generation — 12 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 140 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Video Generation Skill
Overview
This skill generates high-quality videos using structured prompts and a Python script. The workflow includes creating JSON-formatted prompts and executing video generation with optional reference image.
Core Capabilities
- Create structured JSON prompts for AIGC video generation
- Support reference image as guidance or the first/last frame of the video
- Generate videos through automated Python script execution
Workflow
Step 1: Understand Requirements
When a user requests video generation, identify:
- Subject/content: What should be in the image
- Style preferences: Art style, mood, color palette
- Technical specs: Aspect ratio, composition, lighting
- Reference image: Any image to guide generation
- You don't need to check the folder under
/mnt/user-data
Step 2: Create Structured Prompt
Generate a structured JSON file in /mnt/user-data/workspace/ with naming pattern: {descriptive-name}.json
Step 3: Create Reference Image (Optional when image-generation skill is available)
Generate reference image for the video generation.
- If only 1 image is provided, use it as the guided frame of the video
Step 3: Execute Generation
Call the Python script:
python /mnt/skills/public/video-generation/scripts/generate.py \
--prompt-file /mnt/user-data/workspace/prompt-file.json \
--reference-images /path/to/ref1.jpg \
--output-file /mnt/user-data/outputs/generated-video.mp4 \
--aspect-ratio 16:9
Parameters:
--prompt-file: Absolute path to JSON prompt file (required)--reference-images: Absolute paths to reference image (optional)--output-file: Absolute path to output image file (required)--aspect-ratio: Aspect ratio of the generated image (optional, default: 16:9)
[!NOTE] Do NOT read the python file, instead just call it with the parameters.
Video Generation Example
User request: "Generate a short video clip depicting the opening scene from "The Chronicles of Narnia: The Lion, the Witch and the Wardrobe"
What ships with it
1 file 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.
- 10d ago First seen · 140 lines · 29 tokens per session scan A 2536e14c275c
video-generation is a skill published in the GitHub repository HE1780/deer-flow-by-cc (23 stars, last pushed 4mo ago), licensed MIT. It adds 29 tokens to every session and 1,101 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 91% identical to video-generation, differing in 12 lines, and is treated as a copy.
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