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 agentmods add skills/learningmatter-mit/atomisticskills/chem-react-otnpx skills add learningmatter-mit/AtomisticSkills --skill chem-react-otgit clone --depth 1 https://github.com/learningmatter-mit/AtomisticSkillsWhat 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 | $0.00017 | $0.00832 |
| Opus 5 | $0.00009 | $0.00416 |
| Sonnet 5 | $0.00003 | $0.00166 |
| Haiku 4.5 | $0.00002 | $0.00083 |
Grade A, and why
chem-react-ot 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 2d 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 — 92 lines — stays where its author put it; the contents beside it link to each section on GitHub.
chem-react-ot — React-OT Transition State Generation
Goal
Generate transition state (TS) structures given reactant and product structures using the React-OT model (Optimal Transport). React-OT is a generative model that predicts TS geometries directly without requiring an initial guess path (like NEB).
Category: chemistry
Environment: react-ot-agent
Key Features
- Generative TS Prediction: Predicts 3D transition state structures from 3D reactants and products.
- Fast Inference: Uses an ODE solver for generation, typically much faster than DFT-based NEB.
- No Path Guess Required: Directly generates the TS structure.
Usage
1. Environment Setup
This skill requires the react-ot-agent conda environment. Ensure it is installed:
# Env: react-ot-agent
cd conda-envs/react-ot-agent
bash install.sh
2. Download Models
Before running the skill for the first time, download the pre-trained model weights:
# activate react-ot-agent first
conda activate react-ot-agent
python conda-envs/react-ot-agent/download_models.py
The checkpoint is saved to ~/.cache/react-ot/checkpoints/sb-pretrained.ckpt.
3. Generate Transition State
Run the generation script with reactant and product files (xyz, cif, pdb, etc. - anything ASE reads).
# Env: react-ot-agent
python .agents/skills/chem-react-ot/scripts/generate_ts.py \
--reactants reactant.xyz \
--products product.xyz \
--output_dir results/ts_search
Arguments:
--reactants: Path to reactant structure file(s). Can be a single file with multiple molecules or a list of files.--products: Path to product structure file(s).--output_dir: Directory to save the generated TS structure (ts_generated.xyz) and trajectory (generation_traj.xyz).--nfe: Number of function evaluations for the ODE solver (default: 10). Higher values might be more accurate but slower.--checkpoint: Path to custom model checkpoint (optional, defaults to downloaded one).
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.
- examples/oxadiazole_isomerization/output/reaction_rxn.xyz 680 B
- examples/oxadiazole_isomerization/output/ts_generated.xyz 251 B
- examples/oxadiazole_isomerization/product.xyz 232 B
- examples/oxadiazole_isomerization/reactant.xyz 235 B
- examples/oxadiazole_isomerization/README.md 1.9 KB
- examples/oxadiazole_isomerization/reference_ts.xyz 245 B
- scripts/generate_ts.py 5.1 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.
- 2d ago First seen · 92 lines · 17 tokens per session scan A fcdc748b9565
chem-react-ot is a skill published in the GitHub repository learningmatter-mit/AtomisticSkills (154 stars, last pushed 7d ago), licensed MIT. It adds 17 tokens to every session and 832 once invoked, about $0.0001 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
hypogenic
Automated LLM-driven hypothesis generation and testing on tabular datasets. Use when you want to systematically explore hypotheses about patterns in empirical data (e.g., deception detection, content analysis). Combines literature insights with data-driven hypothesis testing. For manual hypothesis formulation use…
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…
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
Hybrid ML + physics binding affinity prediction. Empirical scoring, MM/GBSA rescoring, multi-method consensus, and batch virtual screening for protein-ligand complexes.
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.
medchem
Medicinal chemistry filters. Apply drug-likeness rules (Lipinski, Veber), PAINS filters, structural alerts, complexity metrics, for compound prioritization and library filtering.