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 dralkh/iktinah --skill transformersgit clone --depth 1 https://github.com/dralkh/iktinahWrote 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/dralkh/iktinah/transformers)<a href="https://agentmods.dev/skills/dralkh/iktinah/transformers"><img src="https://agentmods.dev/badge/skills/dralkh/iktinah/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/dralkh/iktinah/transformers"><img src="https://agentmods.dev/badge/skills/dralkh/iktinah/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 | $0.01614 |
| Opus 5.5 | $0.00024 | $0.00646 |
| Sonnet 5.5 | $0.00012 | $0.00323 |
| Haiku 4.5 | $0.00006 | $0.00161 |
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 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.
This is a copy
91% identical to transformers — 69 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 — 196 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 5.12.0 (current PyPI release; June 2026). Requires Python 3.10+; the torch extra currently requires PyTorch 2.4+.
uv pip install "transformers[torch]==5.12.0" huggingface_hub==1.19.0 datasets==5.0.0 evaluate==0.4.6 accelerate==1.14.0
For vision tasks, add:
uv pip install timm==1.0.27 pillow==12.2.0
For audio tasks, add:
uv pip install librosa==0.11.0 soundfile==0.14.0
These pins are for reproducible examples. For exploratory work, loosen them only after checking the Transformers and Hub release notes for API changes.
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.
Use the narrowest token scope that works: read for private or gated model downloads, write only for uploads. If a long-running environment should not send the stored token on every Hub request, set HF_HUB_DISABLE_IMPLICIT_TOKEN=1 and pass a token only where authentication is required.
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.
- 1mo ago First seen · 196 lines · 61 tokens per session scan A 3495a4df73a2
transformers is a skill published in the GitHub repository dralkh/iktinah (77 stars, last pushed 3mo ago), licensed MIT. It adds 61 tokens to every session and 1,614 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 69 lines, and is treated as a copy.
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vector-and-embedding-weaknesses
Hunt vector / embedding weaknesses (OWASP LLM08:2025) — adversarial inputs against the RAG / similarity layer that cause cross-tenant leak, embedding-inversion privacy loss, semantic confusion, and retriever-driven prompt injection.