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/add-model/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/add-model)<a href="https://agentmods.dev/skills/hao-ai-lab/fastvideo/add-model"><img src="https://agentmods.dev/badge/skills/hao-ai-lab/fastvideo/add-model.svg" alt="Measured on agentmods" 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 | $0.00055 | $0.04874 |
| Opus 5 | $0.00028 | $0.02437 |
| Sonnet 5 | $0.00011 | $0.00975 |
| Haiku 4.5 | $0.00006 | $0.00487 |
Grade A, and why
add-model 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 8d 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 — 439 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Add Model
Manual Invocation
This skill is for explicit /add-model use only. Do not auto-start it from a
casual model-port mention. The setup-only workflow is
../add-model-01-prep/SKILL.md.
Goal
Port a new FastVideo model family, model variant, or first-class reusable component so it can be loaded through FastVideo's native model, config, stage, registry, preset, and test infrastructure.
FastVideo has one pipeline architecture: stage-based composition via
ComposedPipelineBase. Vary the stages and modules, not the architecture.
Scope Shapes
Use this skill for either shape:
| Shape | Required output |
|---|---|
| Full model family or variant | Native components, conversion if needed, pipeline config/class, presets, registry, smoke test, local parity tests, example, quality regression. |
| First-class component contribution | Native component class/config, bucket export, component parity test, and a documented downstream pipeline that will consume it. Skip pipeline/preset/registry rows only when the contribution is intentionally component-only. |
If upstream ships many variants, lock scope before coding. "Base model" means checkpoint variant, not a modality subset. If the base checkpoint produces audio, pose, depth, masks, or other output heads, either support those outputs or get explicit user agreement to drop them.
Required Input
Start from an add-model-01-prep handoff, or equivalent fields matching
contracts/prep_handoff.md.
Before Phase 0, read the shared rules and all relevant schemas:
shared/common_rules.mdcontracts/prep_handoff.mdcontracts/port_state.mdcontracts/escape_hatch.mdcontracts/component_context.mdcontracts/parity_status.mdcontracts/conversion_request.mdcontracts/conversion_handoff.mdcontracts/component_skill_handoff.mdcontracts/pipeline_context.mdcontracts/pipeline_handoff.mdcontracts/final_handoff.md
Hard Rules
- Follow
shared/common_rules.mdfor token/auth safety, state files, escape hatches, production import boundaries, and skip/pass semantics. - If the prep handoff is missing or ambiguous, stop and run
../add-model-01-prep/SKILL.md. - If a needed component is not ported, do not ship the pipeline that needs it.
- Wan is grandfathered for missing local parity; do not copy its missing-test precedent for new work.
What ships with it
14 files 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.
- contracts/component_context.md 2.7 KB
- contracts/component_skill_handoff.md 2.0 KB
- contracts/conversion_handoff.md 1.8 KB
- contracts/conversion_request.md 2.3 KB
- contracts/escape_hatch.md 2.0 KB
- contracts/final_handoff.md 1.5 KB
- contracts/parity_status.md 1.8 KB
- contracts/pipeline_context.md 4.1 KB
- contracts/pipeline_handoff.md 2.7 KB
- contracts/port_state.md 2.4 KB
- contracts/prep_handoff.md 1.9 KB
- contracts/README.md 1.7 KB
- shared/common_rules.md 4.5 KB
- shared/component_skill_common.md 5.5 KB
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.
- 8d ago First seen · 439 lines · 55 tokens per session scan A eb62410ff894
add-model is a skill published in the GitHub repository hao-ai-lab/FastVideo (4,343 stars, last pushed yesterday), licensed Apache-2.0. It adds 55 tokens to every session and 4,874 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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sglang-diffusion-add-model
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grpo-rl-training
Expert guidance for GRPO/RL fine-tuning with TRL for reasoning and task-specific model training.
gptq
Post-training 4-bit quantization for LLMs with minimal accuracy loss. Use for deploying large models (70B, 405B) on consumer GPUs, when you need 4× memory reduction with <2% perplexity degradation, or for faster inference (3-4× speedup) vs FP16. Integrates with transformers and PEFT for QLoRA fine-tuning.