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 pennylanegit 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/pennylane)<a href="https://agentmods.dev/skills/k-dense-ai/scientific-agent-skills/pennylane"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-skills/pennylane/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/pennylane"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-skills/pennylane.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- 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 | $0.00074 | $0.01694 |
| Opus 5.5 | $0.00030 | $0.00678 |
| Sonnet 5.5 | $0.00015 | $0.00339 |
| Haiku 4.5 | $0.00007 | $0.00169 |
Grade A, and why
pennylane 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 25d 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
8 near-identical copies found in the catalogue:
- pennylane — 100% identical, 291 lines differ
- pennylane — 92% identical, 308 lines differ
- pennylane — 92% identical, 308 lines differ
- pennylane — 89% identical, 288 lines differ
- pennylane — 89% identical, 289 lines differ
- pennylane — 89% identical, 289 lines differ
- pennylane — 86% identical, 289 lines differ
- pennylane — 86% identical, 290 lines differ
How it starts
The opening of the file, as written. The whole thing — 142 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PennyLane
When to use
Use PennyLane to optimize parameterized circuits, build hybrid quantum-classical models, estimate molecular energies, or compare a validated circuit across devices. This skill targets stable PennyLane 0.45.1. The local examples use small synthetic systems; successful optimization does not establish quantum advantage, molecular accuracy, or hardware fidelity.
Installation
Create a dedicated environment; provider plugins and compiler dependencies should be resolved separately from unrelated scientific packages:
uv venv --python 3.13 .venv-pennylane
uv pip install --python .venv-pennylane/bin/python "pennylane==0.45.1"
PennyLane 0.45 requires NumPy 2+. Core installation includes Lightning. For ML, use the tested JAX 0.7.1/JAXlib 0.7.1 pair or PyTorch; do not assume the latest JAX is supported by this PennyLane release. See device setup for optional plugin versions and their verification limits.
Workflow
- Fix the problem convention: feature ordering and label encoding for ML; Hamiltonian sign for optimization; units, charge, multiplicity, basis, active space and mapping for chemistry.
- Build a small analytic
default.qubitcircuit. Keep the quantum function separate from the QNode when reusing it on another backend. - Check a known value and a gradient against an analytic or finite-difference
result before training. Use trainable
pennylane.numpyarrays for Autograd, framework-native tensors for Torch/JAX. - Optimize while recording objective, gradient norm, seeds, ansatz shape and
package versions.
step_and_costreturns the cost before its update; evaluate the objective again for final reporting. - Validate independently: held-out examples and classical baselines for ML; particle number and a sector-appropriate classical reference for VQE; enumerated small instances for QAOA.
- Introduce finite shots/noise, report uncertainty, inspect decomposed resources, then select a current accessible hardware backend and execution budget. Plugin portability does not guarantee identical gates, measurements or gradients.
What ships with it
7 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.
- 25d ago Changed · -121 lines · -24 tokens per session 7908ee374ed9
- 26d ago Changed · +6 lines 013974fcc6ca
- 1mo ago First seen · 257 lines · 98 tokens per session scan A 1d24f143290a
pennylane is a skill published in the GitHub repository K-Dense-AI/scientific-agent-skills (54,777 stars, last pushed yesterday), licensed MIT. It adds 74 tokens to every session and 1,694 once invoked, about $0.0003 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 2026-09-03.
Other skills, from other repositories
biopython
Comprehensive molecular biology toolkit. Use for sequence manipulation, file parsing (FASTA/GenBank/PDB), phylogenetics, and programmatic NCBI/PubMed access (Bio.Entrez). Best for batch processing, custom bioinformatics pipelines, BLAST automation. For quick lookups use gget; for multi-service integration use…
geopandas-spatial
Geospatial and climate data analysis with geopandas and xarray. Use when: (1) geographic vector data analysis, (2) climate and weather NetCDF data, (3) spatial joins and overlay operations, (4) map visualization, (5) CRS transformations and area calculations. NOT for: satellite imagery ML (use rasterio/torchgeo)…
pymatgen-materials
Materials science computation with pymatgen. Use when: (1) crystal structure creation and manipulation, (2) phase diagram construction, (3) electronic structure analysis, (4) symmetry and space group operations, (5) VASP input/output parsing. NOT for: molecular chemistry (use rdkit-chemistry), protein structure (use…
rdkit-chemistry
Molecular chemistry operations via RDKit. Use when: user asks about molecular structures, SMILES, chemical properties, or fingerprints. NOT for: reaction databases or wet lab protocols.
nanoresearch-experiment
Generate a Python code skeleton from an experiment blueprint.
alterlab-anndata
Build, slice, concatenate, read, and write AnnData annotated data matrices (obs, var, X, layers, obsm, uns) — the scverse data STRUCTURE, not an analysis pipeline. Use when creating or wrangling .h5ad/zarr files, managing cell and gene annotations, concatenating batches, or handling layers/obsm/backed-mode; for the…