Scientific Agent Skills is a collection of reusable procedures that give AI agents capabilities for scientific research across areas such as biology, chemistry, medicine, and drug discovery. It is used by researchers and by people building AI scientist workflows with compatible coding agents. The catalogue contains many of the project's skills and supporting instructions.
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 K-Dense-AI/scientific-agent-skills --skill transformersgit clone --depth 1 https://github.com/K-Dense-AI/scientific-agent-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/k-dense-ai/scientific-agent-skills/transformers)<a href="https://agentmods.dev/skills/k-dense-ai/scientific-agent-skills/transformers"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-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/k-dense-ai/scientific-agent-skills/transformers"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-skills/transformers.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk warn
- 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 Rogue Agent · line 4 Skill establishes unauthorized persistence across sessions via cron jobs, startup scripts, or state files. Session persistence allows an attacker to maintain access beyond the current interaction.Fix: Remove any persistence mechanisms (cron jobs, startup scripts, state files). Skills should not maintain state across sessions without explicit user consent.
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 | $0.01872 |
| Opus 5 | $0.00030 | $0.00936 |
| Sonnet 5 | $0.00012 | $0.00374 |
| Haiku 4.5 | $0.00006 | $0.00187 |
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 7d 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.
Copies of this mod
3 near-identical copies found in the catalogue:
- transformers — 91% identical, 0 lines differ
- transformers — 91% identical, 19 lines differ
- transformers — 83% identical, 38 lines differ
How it starts
The opening of the file, as written. The whole thing — 213 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.
- 7d ago First seen · 213 lines · 61 tokens per session scan A c1b66b110831
transformers is a skill published in the GitHub repository K-Dense-AI/scientific-agent-skills (44,220 stars, last pushed 3d ago), licensed MIT. It adds 61 tokens to every session and 1,872 once invoked, about $0.0003 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 2026-09-03.
Other skills, from other repositories
AI & LLM Security
LLM and AI application security testing — prompt injection, jailbreak resistance, OWASP LLM Top 10 (2025), RAG and agent/tool-use security, model supply chain, and AI red teaming for authorized assessments.
laravel-ai-sdk
Use when integrating AI agents, tool calling, embeddings, structured output, or streaming in Laravel 13 via the laravel/ai package.
laravel-vector-search
Use when implementing semantic/vector search in Laravel 13 with PostgreSQL + pgvector.
prompt-library
Use when looking for a ready-made agent or task template (code review, support, data analysis, translation, extraction) instead of writing one from scratch.
prompt-testing
Use when comparing two prompt variants, defining quality/efficiency/robustness metrics, or deciding whether to adopt a challenger prompt over a baseline.
prompt-creation
Use when writing a new system prompt from scratch, structuring it with Anthropic's 9-element format, or choosing emphasis levels and few-shot examples.