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/reseed-performance-baseline/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/reseed-performance-baseline)<a href="https://agentmods.dev/skills/hao-ai-lab/fastvideo/reseed-performance-baseline"><img src="https://agentmods.dev/badge/skills/hao-ai-lab/fastvideo/reseed-performance-baseline/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/reseed-performance-baseline"><img src="https://agentmods.dev/badge/skills/hao-ai-lab/fastvideo/reseed-performance-baseline.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Agent Snooping · line 623 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
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 | $0.00162 | $0.06572 |
| Opus 5 | $0.00081 | $0.03286 |
| Sonnet 5 | $0.00032 | $0.01314 |
| Haiku 4.5 | $0.00016 | $0.00657 |
Grade A, and why
reseed-performance-baseline 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 — 643 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Re-seed Performance Baseline
Purpose
Replace or advance the rolling performance baseline in the HF dataset
FastVideo/performance-tracking. Legacy targets are scoped by
(model_id, gpu_type). V2 targets are scoped by exact comparable identity:
workload_id, variant_id, benchmark_version, hardware_profile_id,
software_profile_id, and recipe_fingerprint.
Performance comparison uses the median of up to the last 5 successful,
baseline-eligible records for the same target. Failed or calibration-only
records are useful audit history, but they do not move the future baseline
because compare_baseline.py loads records with successful_only=True and
baseline_eligible_only=True.
This skill now reseeds from a reviewed batch of one or more source performance
JSONs. It uploads one new success=true record per accepted source JSON; it
does not blindly replicate one measurement into 3 or 5 records. The effective
reseed size is therefore dynamic and equals the number of provided, validated,
internally consistent source JSONs.
For baseline shifts with existing history, if the operator provides fewer than
3 records, call out that the last-5 rolling median may not move immediately. If
the operator provides 3 consistent shifted records, the rolling median usually
moves immediately. If the operator provides 5 consistent shifted records, the
last-5 window is effectively reset to the new runtime profile. For the first
approved v2 baseline of a new exact identity, one reviewed calibration seed is
enough for the next comparable run to leave CALIBRATION_NEEDED.
These records are intentional operator-approved baseline resets, not ordinary independent main-branch persistence. Mark them clearly with provenance fields so the HF history remains auditable.
Use this skill when a performance test fails for an intentional and reviewed
reason, such as a torch/runtime/container upgrade that legitimately increases
peak memory or changes timings. This is the performance equivalent of
reseed-ssim-references: backup first, scope tightly, require explicit human
approval, then upload reviewed accepted baseline records.
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 · 643 lines · 162 tokens per session scan A 3a507a63be89
reseed-performance-baseline 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 162 tokens to every session and 6,572 once invoked, about $0.0008 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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