Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add Lzy599775/agent-auto-sci-skills --skill transformersgit clone --depth 1 https://github.com/Lzy599775/agent-auto-sci-skillsWrote 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/lzy599775/agent-auto-sci-skills/transformers)<a href="https://agentmods.dev/skills/lzy599775/agent-auto-sci-skills/transformers"><img src="https://agentmods.dev/badge/skills/lzy599775/agent-auto-sci-skills/transformers/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/lzy599775/agent-auto-sci-skills/transformers"><img src="https://agentmods.dev/badge/skills/lzy599775/agent-auto-sci-skills/transformers.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00058 | $0.02594 |
| Opus 5.5 | $0.00023 | $0.01038 |
| Sonnet 5.5 | $0.00012 | $0.00519 |
| Haiku 4.5 | $0.00006 | $0.00259 |
Grade A, and why
transformers 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 16d 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.
This is a copy
91% identical to transformers — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 215 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Transformers
Overview
The Hugging Face Transformers library provides access to thousands of pre-trained models for tasks across NLP, computer vision, audio, and multimodal domains. Use this skill to load models, perform inference, and fine-tune on custom data.
Installation
Targets Transformers 5.18.0, verified against its released source on 2026-10-01. Native CPU checks use Python 3.11, Torch 2.14.1, Datasets 5.0.1, Accelerate 1.15.0, PEFT 0.21.2, and Hub 1.33.0. The Torch extra requires Torch >=2.5. Install in a dedicated environment:
uv venv --python 3.11 .venv-transformers
uv pip install --python .venv-transformers/bin/python "transformers[torch]==5.18.0" "torch==2.14.1" "huggingface-hub==1.33.0" "datasets==5.0.1" "accelerate==1.15.0" "peft==0.21.2"
Use .venv-transformers/Scripts/python.exe on Windows. Select an appropriate Torch build for the target hardware before installation. Hub 2.1.1 is newer, but Datasets 5.0.1 requires Hub <2; upgrading every package independently makes this training stack unsatisfiable. The separate esm SDK currently requires Transformers <5 and belongs in another environment.
Optional dependencies (install only for the selected workflow): Pillow 12.3.0 for images; torchvision matched to Torch and timm 1.0.30 for models that require them; librosa 1.0.0 and soundfile 0.14.0 for audio preprocessing (librosa requires Python >=3.12); FFmpeg for encoded audio file inputs; pytesseract plus Tesseract for OCR document pipelines; bitsandbytes 0.50.2 for supported quantization backends. See model loading before choosing precision or quantization.
Verification used tiny random models, synthetic input, and local save/reload only. Hub pretrained examples throughout this skill are illustrative: public checkpoint metadata and configurations were reviewed, but no weights or datasets were downloaded and no hosted inference or uploads were run. Optional export/distributed/hardware paths are source-checked, not end-to-end tested. See review evidence.
What ships with it
6 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.
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.
- 16d ago Changed · +2 lines · -3 tokens per session 584d1c35d513
- 1mo ago Changed · +17 lines c1b66b110831
- 1mo ago First seen · 196 lines · 61 tokens per session scan A 3495a4df73a2
transformers is a skill published in the GitHub repository Lzy599775/agent-auto-sci-skills (2 stars, last pushed 11d ago), licensed MIT. It adds 58 tokens to every session and 2,594 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 91% identical to transformers, differing in 0 lines, and is treated as a copy.
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