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 SFETNI/Deep-Matter-Chem-Skills --skill reactive-mdgit clone --depth 1 https://github.com/SFETNI/Deep-Matter-Chem-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/sfetni/deep-matter-chem-skills/reactive-md)<a href="https://agentmods.dev/skills/sfetni/deep-matter-chem-skills/reactive-md"><img src="https://agentmods.dev/badge/skills/sfetni/deep-matter-chem-skills/reactive-md/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/sfetni/deep-matter-chem-skills/reactive-md"><img src="https://agentmods.dev/badge/skills/sfetni/deep-matter-chem-skills/reactive-md.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00003 | $0.19265 |
| Opus 5 | $0.00002 | $0.09633 |
| Sonnet 5 | $0.00001 | $0.03853 |
| Haiku 4.5 | $0.00000 | $0.01927 |
Grade A, and why
reactive-md 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 12d 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 — 1,182 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Reactive Molecular Dynamics
Description
This skill covers reactive molecular dynamics (reactive MD) using LAMMPS with the ReaxFF and COMB force fields: force-field selection and parameter provenance, charge equilibration (QEq) setup and diagnostics, timestep and thermostat selection for reactive systems, thermal protocols for reaction sampling, reaction event detection and postprocessing, validation of reactive trajectories against DFT, and coupling reactive MD to active-learning ML potential workflows. Invoke this skill when bond-breaking or bond-forming chemistry must be modeled explicitly — combustion, oxidation, pyrolysis, stress corrosion cracking, catalyst surface chemistry, or shock-induced reactions — and a fixed-topology force field is physically inappropriate.
Domain Context
Reactive MD differs from classical fixed-topology MD in one fundamental respect: the connectivity of the bonding network is not fixed at the start of the simulation but emerges from the interatomic potential as atoms approach or recede. This requires a potential energy function that varies continuously from a bonded to a non-bonded description as a function of local geometry.
ReaxFF (reactive force field) achieves this through a bond-order formalism. The key physical approximations are:
- Bond order: Each pair interaction carries a continuous bond order that depends on the interatomic distance and the local coordination environment. The bond order BO_ij decays smoothly from 1 (full bond) to 0 (no bond) as the interatomic distance increases. Bond order is not a quantum-mechanical quantity — it is a scalar empirical function parameterized to reproduce DFT bond-dissociation curves and reaction barriers.
- Charge equilibration (QEq): At each timestep, atomic partial charges are self-consistently minimized by the electronegativity equalization principle (Rappé & Goddard, 1991). Every atom's charge responds to its instantaneous bonding environment. QEq adds a per-timestep iterative solver that dominates the computational cost of ReaxFF and can fail to converge for systems with large charge separation.
- Coupled energy terms: Bond energy, overcoordination penalty, undercoordination energy, valence angle energy, torsion energy, and van der Waals and Coulomb interactions are all functions of bond order. These terms are globally coupled through a single parameter file (
ffield.reax). Partial substitution of parameters from different publications into one parameter file is physically invalid — all ReaxFF parameters for all elements in the system must come from the same source and fitting procedure. - Transferability limits: A ReaxFF parameterization is fitted to a specific set of elements and a specific set of reference reactions, geometries, and bond energies. The parameterization domain is not universal. Using ReaxFF outside the element set or chemistry it was fitted for — even for elements present in the parameter file — is extrapolation with unbounded errors.
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.
- 12d ago First seen · 1,182 lines · 3 tokens per session scan A f45fafcefbfc
reactive-md is a skill published in the GitHub repository SFETNI/Deep-Matter-Chem-Skills (6 stars, last pushed 1mo ago), licensed MIT. It adds 3 tokens to every session and 19,265 once invoked, about $0.0000 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 2026-08-31.
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…
smiles-validation
Strict SMILES validation, structural comparison, and modification verification. Catches invalid LLM-generated molecules.
patsnap-biological-modality
Biological sequence and modality intelligence via Patsnap MCP.
patsnap-scientific-translational-evidence
Patsnap Scientific & Translational Evidence MCP for AI agents. Retrieval platform focusing on scientific literature and translational outcomes, covering academic publication queries and translational medicine record tracking.
patsnap-target-disease
Patsnap Target & Disease MCP for AI agents. Target and disease profiling tool, covering target characterization, disease profiling, and epidemiology evidence retrieval.
patsnap-solution-engine
Patsnap TRIZ Concept Solution Engine MCP for AI agents. Generates innovation or product cost-reduction concepts through asynchronous TRIZ and TRIZ/DFMA workflows. Use for engineering problem solving, concept alternatives, cost-reduction analysis, task-progress retrieval, and selected-solution details.