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 rdkitgit 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/rdkit)<a href="https://agentmods.dev/skills/k-dense-ai/scientific-agent-skills/rdkit"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-skills/rdkit/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/rdkit"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-skills/rdkit.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 | $0.00080 | $0.01530 |
| Opus 5 | $0.00040 | $0.00765 |
| Sonnet 5 | $0.00016 | $0.00306 |
| Haiku 4.5 | $0.00008 | $0.00153 |
Grade A, and why
rdkit 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 9d 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.
How it starts
The opening of the file, as written. The whole thing — 112 lines — stays where its author put it; the contents beside it link to each section on GitHub.
RDKit Cheminformatics Toolkit
Overview
RDKit is a comprehensive cheminformatics library providing Python APIs for molecular analysis and manipulation. This skill provides guidance for reading/writing molecular structures, calculating descriptors, fingerprinting, substructure searching, chemical reactions, 2D/3D coordinate generation, and molecular visualization. Use this skill for drug discovery, computational chemistry, and cheminformatics research tasks.
Current baseline (checked 2026-06-07): RDKit 2026.03.3 is the latest GitHub/PyPI release (rdkit 2026.3.3 on PyPI). Official installation docs continue to recommend conda-forge for most users, while cross-platform PyPI wheels are published under the rdkit package name. rdkit-pypi is the old PyPI package name and should only appear when maintaining legacy environments.
Installation and Setup
Use uv when installing into an existing Python environment:
uv pip install rdkit
For reproducible chemistry environments, especially when mixing compiled scientific packages, conda-forge remains the upstream recommendation:
conda create -c conda-forge -n my-rdkit-env rdkit
conda activate my-rdkit-env
Avoid installing both conda rdkit and PyPI rdkit/rdkit-pypi into the same environment unless you are deliberately debugging packaging behavior. Mixed installs can make it unclear which binary extension is being imported.
Core Capabilities
Twelve capability areas, each with worked code, are documented in references/core_capabilities.md:
| # | Area | Covers |
|---|---|---|
| 1 | Molecular I/O and creation | SMILES, MOL files and blocks, InChI, SDF and SMILES suppliers, multithreaded reading, writers |
| 2 | Sanitization and validation | disabling automatic sanitization, manual and partial sanitization, detecting problems first |
| 3 | Analysis and properties | atom and bond iteration, ring information and SSSR, chirality and stereochemistry, fragments |
| 4 | Descriptors | MW, LogP, TPSA, H-bond donors/acceptors, rotatable bonds, aromatic rings, bulk calculation, drug-likeness |
| 5 | Fingerprints and similarity | topological, Morgan/ECFP via rdFingerprintGenerator, MACCS, atom pair, torsion, Avalon; Tanimoto and other metrics; Butina clustering |
| 6 | Substructure searching | SMARTS queries, match retrieval, and a library of common patterns |
| 7 | Chemical reactions | reaction SMARTS, applying reactions, reaction fingerprints |
| 8 | 2D and 3D coordinates | depiction, template alignment, ETKDG embedding, force-field optimization, RMSD, constrained embedding |
| 9 | Visualization | single and grid images, substructure highlighting, custom drawer options, Jupyter integration, fingerprint bit environments |
| 10 | Molecular modification | explicit hydrogens, Kekulization, aromaticity, substructure replacement, charge neutralization |
| 11 | Hashes and standardization | Murcko scaffold and canonical hashes, regioisomer hashes, randomized SMILES for augmentation |
| 12 | Pharmacophore and 3D features | feature factories and feature extraction |
What ships with it
8 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.
- references/api_reference.md 17 KB
- references/core_capabilities.md 15 KB
- references/descriptors_reference.md 12 KB
- references/smarts_patterns.md 8.0 KB
- references/workflows_and_best_practices.md 5.2 KB
- scripts/molecular_properties.py 7.2 KB runs code
- scripts/similarity_search.py 9.3 KB runs code
- scripts/substructure_filter.py 12 KB runs code
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.
- 9d ago First seen · 112 lines · 80 tokens per session scan A d87943f6d751
rdkit is a skill published in the GitHub repository K-Dense-AI/scientific-agent-skills (44,469 stars, last pushed today), licensed MIT. It adds 80 tokens to every session and 1,530 once invoked, about $0.0004 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
discovery-toolbox
A routed repertoire of 90 scientific thinking operators for biological research agents - visual reasoning, detectability and information budgets, search reframing, causal identification, competing explanations, observation and selection processes, pipeline artifact diagnosis, effort allocation, and confirmation…
discovery-director
Operate as a research director making original discoveries from a given biological question and dataset. Use when the task is open-ended scientific research, exploring omics or experimental data for findings, hypothesis generation and testing, screening a large candidate space of genes, variants, features or…
bio-interdomain-hgt
Detect and polarize interdomain horizontal gene transfer with homology, context, and phylogenetic checks. Use when studying lateral gene transfer, virus-host gene exchange, endogenous viral elements, or donor direction.
polars-dovmed
Search PMC Open Access and bioRxiv corpora with polars-dovmed. Use when structured, reproducible literature queries should run through the hosted API or local parquet indexes.
csag-extraction
Extract a Conditional Scientific Argumentation Graph and grounded Q&A from a manuscript. Use when representing assertions, contexts, evidence links, and inference steps in machine-readable form.
exploratory-data-analysis
Inspect scientific data and generate a Markdown structure-and-quality report. Use when triaging tabular, array, sequence, HDF5, JSON, or raster files before downstream analysis.