Diffusers is a PyTorch library for running and building diffusion systems that generate images, audio, and 3D molecular structures. Developers use its pretrained models, generation pipelines, and interchangeable noise schedulers for inference or model training. The catalogue add-ons support workflows around the library.
Borrowing it
Nothing to install: this file belongs to huggingface/diffusers. 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/huggingface/diffusers/main/.ai/skills/self-review/SKILL.mdgit clone --depth 1 https://github.com/huggingface/diffusersWrote 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/huggingface/diffusers/self-review)<a href="https://agentmods.dev/skills/huggingface/diffusers/self-review"><img src="https://agentmods.dev/badge/skills/huggingface/diffusers/self-review/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/huggingface/diffusers/self-review"><img src="https://agentmods.dev/badge/skills/huggingface/diffusers/self-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Snyk pass
- 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 | $0.00088 | $0.00701 |
| Opus 5.5 | $0.00035 | $0.00280 |
| Sonnet 5.5 | $0.00018 | $0.00140 |
| Haiku 4.5 | $0.00009 | $0.00070 |
Grade A, and why
self-review 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 1mo 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 — 62 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Self-review
Runs the same rubric as the @claude CI reviewer, so you catch issues before a
maintainer does — but over your whole PR diff. (The CI scopes itself to
src/diffusers/, tests/, and .ai/; for your own PR, also review your docs
and scripts.) You're already on the branch with the conventions loaded, so: get
the
diff → review it against the rubric → report → iterate with the contributor
until it's ready, then remind them to share the final notes on the PR.
1. Get the diff
git diff main...HEAD # use your target branch if not main
If the branch trails main and the diff looks polluted with unrelated merged
files, scope to your own commits: git log main..HEAD --oneline, then
git show <commit>.
2. Read the rubric
references/review-rules.md is the canonical rubric (the CI pins it from main) — read
it and review against it; don't rely on a remembered copy. For the areas you
touched, also read references/code_style.md, references/models.md, references/pipelines.md,
references/modular.md, references/testing.md, or references/pitfalls.md.
3. Report
- Blocking issues — numbered. Each: title → explanation →
file.py:line→ impact. Cite the rule, e.g. Perreferences/models.md: "…only keep the inference path." - Non-blocking issues — same format, lower severity.
- Dead code (advisory) — a table:
path:line· Likely-dead / Used · reason. - Summary — short synthesis and a verdict (READY / NEEDS CHANGES),
spelling out:
- Fix before submitting — all blocking issues, and remove the flagged dead code.
- Leave for the actual review — non-blocking issues that aren't obviously correct; raise these with the reviewer rather than guessing at them now.
Report only — do not edit files. Be concrete, cite the rule, review the whole diff, and don't invent issues or flag pure style.
4. Iterate until ready, then share
Expect several rounds: the contributor addresses findings, you review again. Keep working with them to fix as much as possible until the verdict is READY — the Leave for the actual review items are the only ones that should reach the reviewer unresolved. End the final round's report by reminding the contributor to share it on the PR (description or a comment) — it saves the reviewer a few rounds of back-and-forth. Never commit the notes as part of the diff.
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.
- 1mo ago First seen · 62 lines · 88 tokens per session scan A 3b6e2670c3dd
self-review is a skill published in the GitHub repository huggingface/diffusers (34,728 stars, last pushed today), licensed Apache-2.0. It adds 88 tokens to every session and 701 once invoked, about $0.0004 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.
Other skills, from other repositories
ml-training-recipes
Battle-tested PyTorch training recipes for all domains — LLMs, vision, diffusion, medical imaging, protein/drug discovery, spatial omics, genomics. Covers training loops, optimizer selection (AdamW, Muon), LR scheduling, mixed precision, debugging, and systematic experimentation. Use when training or fine-tuning…
physicsnemo-shard-tensor
Official NVIDIA-authored guidance for PhysicsNeMo ShardTensor domain parallelism — integrate domain parallelism into training/inference scripts (new or existing) with DDP or FSDP2, write and register shard patches to enable new layers/ops, and bootstrap multi-GPU correctness tests. Use when working with ShardTensor…
physicsnemo-discover
Official NVIDIA-authored guidance for navigating PhysicsNeMo — pick the model, datapipe, or example for a SciML/AI4Science task (surrogates, forecasting, downscaling, physics-informed, inverse, generative). Points at existing files via live repo search; never writes code. Do NOT use for installation or environment…
github
GitHub operations via gh CLI: issues, PRs, CI runs, code review, API queries. Use when: (1) checking PR status or CI, (2) creating/commenting on issues, (3) listing/filtering PRs or issues, (4) viewing run logs. NOT for: complex web UI interactions requiring manual browser flows (use browser tooling when available)…
meta-codereview-current-diff
Read the current uncommitted diff, run three independent reviewers (safety + tests-coverage + style) in parallel, then arbitrate a single BLOCK / BLOCKWITHOVERRIDE / PASSWITHNOTES verdict. Use before commit when you want a multi-perspective second-opinion instead of a single-reviewer agent loop.
review-trtmc-pr
Review a TensorRT-Model-Connect GitHub PR or local contributor branch against the current post-#1093 model-family isolation architecture, repository rules, behavioral correctness, and exact-head validation evidence. Use for contributor self-review before marking a PR ready, or when deciding whether a community PR is…