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 learningmatter-mit/AtomisticSkills --skill drug-molecular-fingerprintsgit clone --depth 1 https://github.com/learningmatter-mit/AtomisticSkillsWrote 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/learningmatter-mit/atomisticskills/drug-molecular-fingerprints)<a href="https://agentmods.dev/skills/learningmatter-mit/atomisticskills/drug-molecular-fingerprints"><img src="https://agentmods.dev/badge/skills/learningmatter-mit/atomisticskills/drug-molecular-fingerprints/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/learningmatter-mit/atomisticskills/drug-molecular-fingerprints"><img src="https://agentmods.dev/badge/skills/learningmatter-mit/atomisticskills/drug-molecular-fingerprints.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- 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 | $0.00033 | $0.00861 |
| Opus 5 | $0.00016 | $0.00430 |
| Sonnet 5 | $0.00007 | $0.00172 |
| Haiku 4.5 | $0.00003 | $0.00086 |
Grade A, and why
drug-molecular-fingerprints 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 10d 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 — 119 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Molecular Fingerprints
Goal
To compute circular Morgan fingerprints (ECFP-style; default ECFP4 with radius=2) for a set of compounds, then calculate pairwise Tanimoto similarity for library comparison. Optionally perform Butina clustering for diversity analysis and generate a similarity heatmap for small sets.
This skill is commonly used for hit expansion, SAR triage, compound library diversity assessment, and applicability-domain style analysis.
Instructions
The drugdisc MCP server provides a compute_molecular_fingerprints tool that can be called directly:
Basic usage with SMILES file:
mcp_drugdisc_compute_molecular_fingerprints(
smiles_file="compounds.smi",
radius=2,
fp_size=2048,
compute_similarity=True,
output_file="similarity.json"
)
With Butina clustering:
mcp_drugdisc_compute_molecular_fingerprints(
smiles_file="library.smi",
cluster=True,
cluster_cutoff=0.7,
output_file="clustered.json"
)
With similarity heatmap (small molecule sets, ≤250 compounds):
mcp_drugdisc_compute_molecular_fingerprints(
smiles_file="hits.smi",
save_heatmap="heatmap.png",
output_file="similarity.json"
)
Feature Morgan (FCFP-like) fingerprints:
mcp_drugdisc_compute_molecular_fingerprints(
smiles_file="compounds.smi",
use_features=True,
output_file="fcfp_similarity.json"
)
Chirality-aware fingerprints:
mcp_drugdisc_compute_molecular_fingerprints(
smiles_file="enantiomers.smi",
use_chirality=True,
output_file="chiral_sim.json"
)
Examples
SMILES file format
CCO ethanol
CCCO propanol
c1ccccc1 benzene
c1ccc(cc1)O phenol
Basic similarity analysis
mcp_drugdisc_compute_molecular_fingerprints(
smiles_file=".agents/skills/drug-molecular-fingerprints/examples/compounds.smi",
output_file="similarity.json"
)
Diversity-based clustering for library selection
mcp_drugdisc_compute_molecular_fingerprints(
smiles_file="screening_library.smi",
cluster=True,
cluster_cutoff=0.5,
output_file="diverse_clusters.json"
)
What ships with it
4 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.
- 10d ago First seen · 119 lines · 33 tokens per session scan A b357c50858b4
drug-molecular-fingerprints is a skill published in the GitHub repository learningmatter-mit/AtomisticSkills (163 stars, last pushed 6d ago), licensed MIT. It adds 33 tokens to every session and 861 once invoked, about $0.0002 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 2026-08-30.
Other skills, from other repositories
datamol
Pythonic wrapper around RDKit with simplified interface and sensible defaults. Preferred for standard drug discovery including SMILES parsing, standardization, descriptors, fingerprints, clustering, 3D conformers, parallel processing. Returns native rdkit.Chem.Mol objects. For advanced control or custom parameters…
rdkit
Cheminformatics toolkit for fine-grained molecular control. SMILES/SDF parsing, descriptors (MW, LogP, TPSA), fingerprints, substructure search, 2D/3D generation, similarity, reactions. For standard workflows with simpler interface, use datamol (wrapper around RDKit). Use rdkit for advanced control, custom…
deepchem
Molecular ML with diverse featurizers and pre-built datasets. Use for property prediction (ADMET, toxicity) with traditional ML or GNNs when you want extensive featurization options and MoleculeNet benchmarks. Best for quick experiments with pre-trained models, diverse molecular representations. For graph-first…
binding-affinity
Empirical affinity estimates, ligand energy inspection, docking-score consensus, and batch virtual screening. Full MM/GBSA requires a validated external workflow.
drug-design
End-to-end drug discovery pipeline orchestration. Deterministic Python script that auto-chains structure prediction, pocket detection, de novo design, docking, scoring, and ADMET filtering into reproducible workflows.
molecular-optimization
Iterative lead optimization with analyze-reason-generate-verify-evaluate loop. Paper-backed (MT-Mol, DrugR, MultiMol).