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 datamolgit 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/datamol)<a href="https://agentmods.dev/skills/k-dense-ai/scientific-agent-skills/datamol"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-skills/datamol/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/datamol"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-skills/datamol.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk warn
- 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 | $0.00067 | $0.02586 |
| Opus 5.5 | $0.00027 | $0.01034 |
| Sonnet 5.5 | $0.00013 | $0.00517 |
| Haiku 4.5 | $0.00007 | $0.00259 |
Grade A, and why
datamol 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 14d 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
1 near-identical copy found in the catalogue:
- datamol — 100% identical, 88 lines differ
How it starts
The opening of the file, as written. The whole thing — 233 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Datamol Cheminformatics Skill
Overview
Datamol is a Python library that provides a lightweight, Pythonic abstraction layer over RDKit for molecular cheminformatics. Simplify complex molecular operations with sensible defaults, efficient parallelization, and modern I/O capabilities. All molecular objects are native rdkit.Chem.Mol instances, ensuring full compatibility with the RDKit ecosystem.
Verified target: datamol 0.13.0, released September 9, 2026. Local checks used Python 3.13 and RDKit 2026.03.6. Python 3.11+ and RDKit 2024.09+ are required. The 0.13 distribution includes SELFIES, visualization, cloud, Excel and Parquet runtime dependencies. Current API examples and verification limits are recorded in references/review.md. Pin the environment: RDKit upgrades can change canonical representations and retained conformers. Compare chemical invariants, not a fixed conformer count or an exact version-dependent canonical string.
Key capabilities:
- Molecular format conversion (SMILES, SELFIES, InChI)
- Structure standardization and sanitization
- Molecular descriptors and fingerprints
- 3D conformer generation and analysis
- Clustering and diversity selection
- Scaffold and fragment analysis
- Chemical reaction application
- Visualization and alignment
- Batch processing with parallelization
- Cloud storage support via fsspec
Installation and Setup
Guide users to install datamol:
uv pip install "datamol==0.13.0"
RDKit and the S3/GCS, Excel/Parquet, visualization and SELFIES dependencies are installed by the current distribution; separate feature extras are not required. Use an isolated environment to avoid conflicting scientific-package requirements.
Import convention:
import datamol as dm
Core Workflows
Ten workflow areas, each with worked code, are documented in references/core_workflows.md:
| # | Area | Covers |
|---|---|---|
| 1 | Basic molecule handling | to_mol, batch conversion, error handling, canonical and isomeric SMILES, sanitization and full standardization |
| 2 | Reading and writing files | SDF, SMILES, CSV, Excel with rendered structures, the universal reader/writer, and cloud or HTTPS paths |
| 3 | Descriptors and properties | the standard descriptor set, parallel computation, aromaticity, stereochemistry, flexibility, and filtering |
| 4 | Fingerprints and similarity | ECFP4 and other types, pairwise and cross-set distances, nearest-neighbour lookup (Tanimoto distance = 1 − similarity) |
| 5 | Clustering and diversity | similarity clustering, diverse subset picking, and cluster centroids |
| 6 | Scaffold analysis | Bemis-Murcko scaffolds, grouping and counting, and scaffold-disjoint train/test splits |
| 7 | Fragmentation | fragmenting molecules, finding common fragments across a library, and fragment-based scoring |
| 8 | 3D conformers | generation, access, RMSD clustering, representative selection, and SASA |
| 9 | Visualization | grids, files, publication SVG, substructure alignment, atom and bond highlighting, conformer display |
| 10 | Chemical reactions | reaction SMARTS, applying to a molecule or a whole library |
What ships with it
9 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/conformers_module.md 4.5 KB
- references/core_api.md 4.7 KB
- references/core_workflows.md 8.8 KB
- references/descriptors_viz.md 4.7 KB
- references/fragments_scaffolds.md 4.2 KB
- references/io_module.md 4.5 KB
- references/reactions_data.md 4.3 KB
- references/review.md 3.3 KB
- references/workflow_patterns.md 5.3 KB
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.
- 14d ago Changed · +11 lines 3a0d725456f3
- 15d ago Changed · +4 lines 3207266ac7a6
- 1mo ago Changed · +17 lines 39d11469c899
- 1mo ago First seen · 201 lines · 67 tokens per session scan A 41062cea6b8a
datamol is a skill published in the GitHub repository K-Dense-AI/scientific-agent-skills (50,453 stars, last pushed today), licensed MIT. It adds 67 tokens to every session and 2,586 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 2026-08-30.
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 reciprocal hits, genomic context, and gene trees. Use for virus-host gene exchange, endogenous viral elements, or donor direction.
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.
notebooks
Author, run and convert marimo or Jupyter notebooks. Use when delivering an analysis notebook executed end to end with figures embedded.
bio-annotation
Annotate proteins with functions, domains, and homology-based taxonomy. Use when assigning functional or taxonomic labels to proteins from genomes, contigs, or MAGs.