FastVideo is a framework for training and running accelerated video-generation models, including real-time inference and post-training workflows. It is for researchers and developers building or deploying diffusion-based systems that generate video.
Borrowing it
Nothing to install: this file belongs to hao-ai-lab/FastVideo. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/hao-ai-lab/FastVideo/main/.agents/skills/dreamverse-deploy/SKILL.mdgit clone --depth 1 https://github.com/hao-ai-lab/FastVideoWrote 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/hao-ai-lab/fastvideo/dreamverse-deploy)<a href="https://agentmods.dev/skills/hao-ai-lab/fastvideo/dreamverse-deploy"><img src="https://agentmods.dev/badge/skills/hao-ai-lab/fastvideo/dreamverse-deploy/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/hao-ai-lab/fastvideo/dreamverse-deploy"><img src="https://agentmods.dev/badge/skills/hao-ai-lab/fastvideo/dreamverse-deploy.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00038 | $0.02586 |
| Opus 5.5 | $0.00015 | $0.01034 |
| Sonnet 5.5 | $0.00008 | $0.00517 |
| Haiku 4.5 | $0.00004 | $0.00259 |
Grade A, and why
dreamverse-deploy 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 2mo 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 — 197 lines — stays where its author put it; the contents beside it link to each section on GitHub.
dreamverse-deploy — redeploy migrated Dreamverse on a chosen GPU
Scope: project (lives in this repo at .agents/skills/dreamverse-deploy/)
When to use: you want to (re)launch the migrated apps/dreamverse/ backend
and frontend on this dev node, pinned to a specific physical GPU. Tears down
any existing deploy on the same ports first, then boots fresh and waits for
both /readyz and the FE root to return 200.
Prerequisites
- Working tree containing
apps/dreamverse/ dreamverse-serverinstalled from this checkout; if missing, runuv pip install -e ".[dreamverse]"- Local conda env at
~/miniconda3/envs/fv-main/withflashinfer-python,cerebras-cloud-sdk,openaiinstalled (override the default path withDREAMVERSE_PYTHON=/path/to/python) ~/.envexportingCEREBRAS_API_KEY,GROQ_API_KEY, etc.- npm available in
$PATH(or setNPM=/path/to/npm) gcc-13+g++-13at/usr/bin/(workaround for nvcc gcc-15 rejection)- Recommended: native ffmpeg at
$HOME/opt/ffmpeg-native/bin/ffmpeg, built viabash apps/dreamverse/scripts/install_native_ffmpeg.sh. The deploy detects that binary directly and exports it for the backend. The installer's generatedapps/dreamverse/scripts/ffmpeg-env.shis for manual launches. When the binary is missing, the deploy falls back to system ffmpeg with a warning. SetDREAMVERSE_REQUIRE_NATIVE_FFMPEG=trueto make the missing binary a hard failure.
If any required prereq is missing, the script fails fast with a clear message.
Usage
# Deploy on GPU 4 with the current web port. The legacy helper default remains
# 5274, so pass 5299 explicitly. Torch compile and warmup are both off.
./.agents/skills/dreamverse-deploy/scripts/dreamverse-deploy.sh 4 8009 5299
# Deploy on GPU 6 with custom ports
./.agents/skills/dreamverse-deploy/scripts/dreamverse-deploy.sh 6 8089 5275
# Deploy on GPU 0 with warmup enabled
./.agents/skills/dreamverse-deploy/scripts/dreamverse-deploy.sh --warmup 0 8009 5299
# Deploy with torch.compile enabled (max-autotune; first segment ~3-4min,
# subsequent segments save ~3s — only worth it for benchmarking)
./.agents/skills/dreamverse-deploy/scripts/dreamverse-deploy.sh --torch-compile 4 8009 5299
# Deploy with both warmup AND torch.compile enabled
./.agents/skills/dreamverse-deploy/scripts/dreamverse-deploy.sh --warmup --torch-compile 4 8009 5299
# Flags can appear before, between, or after positional args
./.agents/skills/dreamverse-deploy/scripts/dreamverse-deploy.sh 4 8089 5275 --warmup
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.
- 2mo ago First seen · 197 lines · 38 tokens per session scan A 2197c8215f4c
dreamverse-deploy is a skill published in the GitHub repository hao-ai-lab/FastVideo (4,755 stars, last pushed today), licensed Apache-2.0. It adds 38 tokens to every session and 2,586 once invoked, about $0.0002 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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