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/decompose-pipeline-pr/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/decompose-pipeline-pr)<a href="https://agentmods.dev/skills/hao-ai-lab/fastvideo/decompose-pipeline-pr"><img src="https://agentmods.dev/badge/skills/hao-ai-lab/fastvideo/decompose-pipeline-pr/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/decompose-pipeline-pr"><img src="https://agentmods.dev/badge/skills/hao-ai-lab/fastvideo/decompose-pipeline-pr.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 335 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 | $0.00079 | $0.03492 |
| Opus 5 | $0.00039 | $0.01746 |
| Sonnet 5 | $0.00016 | $0.00698 |
| Haiku 4.5 | $0.00008 | $0.00349 |
Grade A, and why
decompose-pipeline-pr 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 12d 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 — 339 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Decompose Pipeline PR
Purpose
When a PR adds a new pipeline (or first-class component port) and crosses
~3,000 LOC, single-shot review converges to rubber-stamping. This skill
decomposes such a PR into a stack of independently-reviewable PRs without
disturbing main.
It is the inverse of add-model: where add-model walks adding a new
pipeline as a fresh PR, this skill walks decomposing an existing oversized
pipeline PR.
Worked example: PR #1280 (daVinci-MagiHuman, 9,812 LOC, 56 files) → 2 prerequisite PRs off main + 8-PR stack:
- #1293
will/activation-trace(prerequisite) - #1294
will/loader-infra(prerequisite) - #1295 (1/8) housekeeping
- #1296 (2/8) t5gemma encoder
- #1297 (3/8) DiT
- #1298 (4/8) pipeline stages
- #1299 (5/8) pipeline orchestrator
- #1300 (6/8) provenance (AGENTS.md, JOURNAL.md, lessons)
- #1301 (7/8) conversion scripts
- #1302 (8/8) registry activation
Prerequisites
- Open PR number on
hao-ai-lab/FastVideo(or any FastVideo fork) ghCLI authenticated against the target remote- Local git worktree support (
git worktree) - Git config
user.name/user.emailset - Pre-commit installed (
pre-commit install --hook-type pre-commit --hook-type commit-msg) - The target PR's branch fetched locally as
origin/<feature-branch>
Inputs
| Parameter | Required | Description |
|---|---|---|
| PR number or URL | Yes | E.g. 1280 or https://github.com/hao-ai-lab/FastVideo/pull/1280 |
| Max desired PR size | No | Defaults to ~2,500 LOC of code per stack PR (excluding generated/journal files) |
| Output directory | No | Defaults to .agents/tmp/decompose-<pr-number>/ (gitignored) |
Steps
1. Verify ground truth (do not trust gh pr diff --name-only)
gh pr diff <N> --name-only has been observed to emit phantom file entries.
Always cross-check against the authoritative git diff:
mkdir -p .agents/tmp/decompose-<N>
git fetch origin pull/<N>/head:<feature-branch>
git diff origin/main..origin/<feature-branch> --name-status \
> .agents/tmp/decompose-<N>/files.txt
git diff origin/main..origin/<feature-branch> --stat
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.
- 12d ago First seen · 339 lines · 79 tokens per session scan A d11e32baa966
decompose-pipeline-pr is a skill published in the GitHub repository hao-ai-lab/FastVideo (4,376 stars, last pushed 3d ago), licensed Apache-2.0. It adds 79 tokens to every session and 3,492 once invoked, about $0.0004 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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