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 magic3007/dotfiles --skill transformersgit clone --depth 1 https://github.com/magic3007/dotfilesWrote 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/magic3007/dotfiles/transformers)<a href="https://agentmods.dev/skills/magic3007/dotfiles/transformers"><img src="https://agentmods.dev/badge/skills/magic3007/dotfiles/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/magic3007/dotfiles/transformers"><img src="https://agentmods.dev/badge/skills/magic3007/dotfiles/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 | $0.00061 | $0.01429 |
| Opus 5 | $0.00030 | $0.00714 |
| Sonnet 5 | $0.00012 | $0.00286 |
| Haiku 4.5 | $0.00006 | $0.00143 |
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 5d 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
83% identical to transformers — 38 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 — 191 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
Tested against transformers 5.9.x (stable; May 2026). Requires Python 3.10+ and PyTorch 2.4+.
uv pip install "transformers[torch]>=5.9" huggingface_hub datasets evaluate accelerate
For vision tasks, add:
uv pip install timm pillow
For audio tasks, add:
uv pip install librosa soundfile
Check your version:
import transformers
print(transformers.__version__)
Authentication
Many models on the Hugging Face Hub are gated or private. Authenticate before loading them.
Recommended: CLI login (stores token in ~/.cache/huggingface/token):
hf auth login
Python:
from huggingface_hub import login
login() # Interactive prompt; do not hardcode tokens in scripts
Servers / CI: set HF_TOKEN in the environment (never commit tokens to git or shell profiles):
export HF_TOKEN="..." # Read token from a secret manager, not source code
Get tokens at: https://huggingface.co/settings/tokens
Security: Never paste tokens into notebooks, repos, or shared configs. Prefer hf auth login over exporting tokens in .bashrc or .zshrc.
Transformers v5
Transformers v5 is PyTorch-only (TensorFlow and JAX backends were removed). For upgrades from v4, see the v5 migration guide. New projects should pair transformers 5.x with huggingface_hub 1.x.
Gated or custom architectures: accept the model license on the Hub, then load with trust_remote_code=True only when the model card requires custom code you have reviewed.
Cache location: set HF_HOME for a writable cache root (Hub files default under $HF_HOME/hub).
What ships with it
5 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.
- 5d ago First seen · 191 lines · 61 tokens per session scan A 6f082f294fd8
transformers is a skill published in the GitHub repository magic3007/dotfiles (11 stars, last pushed yesterday), licensed MIT. It adds 61 tokens to every session and 1,429 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 83% identical to transformers, differing in 38 lines, and is treated as a copy.
Other skills, from other repositories
orchestrate-agents
Orchestrate multiple agent CLIs (Claude, Codex, Antigravity) via tmux with a shared fleet store, dispatching one guardian subagent per pane. Survey-first: inspects and adopts existing tmux sessions, windows, and agent panes before creating anything new. Use when running a multi-agent session, dispatching parallel…
assess-quality
Foundational quality framework: the five questions (readable, easy to start, expands without bloat, consistent, intentional) every other dev skill is judged against, plus the dual-audience and workshop principles. Use when onboarding to a project, defining a quality bar, setting an assessment checklist, or arbitrating…
create-oss-skill
Create well-formed Agent Skills following the agentskills.io specification. Scaffold directories, write SKILL.md files, bundle scripts, and structure instructions for progressive disclosure. Use when creating a new skill, reviewing skill structure, optimizing a skill description, or setting up evals for skill quality.
extend-oss-skills-to-claude
Extend standard agentskills.io skills with Claude Code-specific features. Invocation control, subagent execution, dynamic context injection, string substitutions, model/effort overrides, and deployment scoping. Use when adapting a portable skill for Claude Code, adding Claude-specific frontmatter, setting up subagent…
merge-ready
Drive an existing pull request to a mergeable state: get CI green, resolve merge conflicts with the base branch, address and resolve review comments, trigger required bot reviews/approvals (e.g. commenting '@claude review'), link associated issues, and clean up the PR title and description. Ends with a readiness…
scaffold-project
Generates cross-language standard files (README, AGENTS.md, LICENSE, CONTRIBUTING.md, SECURITY.md, sr.yaml, .envrc, llms.txt), documentation conventions, and project structure, then dispatches to language-specific scaffolds. Use first for cross-language standard files and structure, THEN load the matching scaffold…