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-02-parity/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-02-parity)<a href="https://agentmods.dev/skills/hao-ai-lab/fastvideo/add-model-02-parity"><img src="https://agentmods.dev/badge/skills/hao-ai-lab/fastvideo/add-model-02-parity/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/add-model-02-parity"><img src="https://agentmods.dev/badge/skills/hao-ai-lab/fastvideo/add-model-02-parity.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 | $0.00053 | $0.02398 |
| Opus 5 | $0.00026 | $0.01199 |
| Sonnet 5 | $0.00011 | $0.00480 |
| Haiku 4.5 | $0.00005 | $0.00240 |
Grade A, and why
add-model-02-parity 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 11d 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 — 235 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Add Model Parity
Goal
Create parity tests as early as possible in a FastVideo port. The first pass can land before conversion or component implementation as an executable scaffold; handoff is blocked until the same tests become non-skip PASS with real weights.
When To Run
Follow ../add-model/shared/common_rules.md for token/auth safety, state files,
escape hatches, and skip/pass semantics.
Run immediately after /add-model Phase 1 has identified:
- official component classes and call signatures;
- FastVideo target component buckets/classes/configs;
- local reference clone or import path from
add-model-01-prep; - local raw or Diffusers weight path;
official_env_status=imports_ok, or private deps that will be stubbed locally in tests;local_tests_readmedocumenting setup and planned review/test commands;- expected component inputs and output tensors.
Do not wait for all FastVideo components to be implemented. Write the tests first, then let component-porting subagents make them pass.
Outputs
- One component parity test per required component, including reused components:
tests/local_tests/<bucket>/test_<family>_<component>_parity.py. - Optional helper for upstream private deps:
tests/local_tests/helpers/<family>_upstream.py. - Pipeline parity is owned later by
../add-model-09-pipeline/SKILL.mdafter all component parity tests pass non-skip. - A parity status block for the
/add-modelparity verification phase.
Early Scaffold Rules
- A scaffold may skip while the FastVideo class, converted weights, or official import is missing.
- A scaffold must already encode the real official load path, FastVideo load path, deterministic inputs, expected output extraction, and tolerance target.
- Each parity test must declare its coverage scope in the file docstring or a
module constant:
production_loader,implementation_subcomponent, orboth. Implementation/subcomponent parity may bypass production loaders deliberately, but final handoff still needs production-loader coverage somewhere before the pipeline depends on that component. - Official reference imports must run in the current FastVideo environment; do not create or assume a separate upstream venv/conda env.
- A scaffold is not evidence of correctness. It becomes evidence only after a local non-skip PASS.
- Prefer env-var path overrides with repo-relative defaults.
- Keep tests local-only under
tests/local_tests/; package/CI quality tests are added later. - Update shared state files as described in
../add-model/shared/common_rules.mdwhenever adding or activating parity tests.
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.
- 11d ago First seen · 235 lines · 53 tokens per session scan A bbfa34543fe2
add-model-02-parity is a skill published in the GitHub repository hao-ai-lab/FastVideo (4,368 stars, last pushed 2d ago), licensed Apache-2.0. It adds 53 tokens to every session and 2,398 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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