DeerFlow is an open-source super-agent harness that coordinates sub-agents, memory, tools, sandboxes, and extensible skills to handle research, coding, and content-creation tasks that may run for minutes or hours. It is intended for long-running, multi-step work performed by AI agents. The catalogue entries are skills, agents, and instructions that support its workflows.
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 bytedance/deer-flow --skill video-generationgit clone --depth 1 https://github.com/bytedance/deer-flowWrote 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/bytedance/deer-flow/video-generation)<a href="https://agentmods.dev/skills/bytedance/deer-flow/video-generation"><img src="https://agentmods.dev/badge/skills/bytedance/deer-flow/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/bytedance/deer-flow/video-generation"><img src="https://agentmods.dev/badge/skills/bytedance/deer-flow/video-generation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Snyk pass
- NVIDIA SkillSpector pass
- Socket warn
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 | $0.01282 |
| Opus 5.5 | $0.00012 | $0.00513 |
| Sonnet 5.5 | $0.00006 | $0.00256 |
| Haiku 4.5 | $0.00003 | $0.00128 |
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 1mo 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.
Copies of this mod
6 near-identical copies found in the catalogue:
- video-generation — 100% identical, 3 lines differ
- video-generation — 91% identical, 15 lines differ
- video-generation — 91% identical, 15 lines differ
- video-generation — 91% identical, 15 lines differ
- video-generation — 91% identical, 15 lines differ
- video-generation — 91% identical, 15 lines differ
How it starts
The opening of the file, as written. The whole thing — 155 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.
- 1mo ago Changed · +3 lines cbd6a3ef76c4
- 1mo ago First seen · 152 lines · 29 tokens per session scan A 7a5116069ffa
video-generation is a skill published in the GitHub repository bytedance/deer-flow (84,291 stars, last pushed today), licensed MIT. It adds 29 tokens to every session and 1,282 once invoked, about $0.0001 per session on Opus 5.5. 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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