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 ase-frameworkgit 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/ase-framework)<a href="https://agentmods.dev/skills/sfetni/deep-matter-chem-skills/ase-framework"><img src="https://agentmods.dev/badge/skills/sfetni/deep-matter-chem-skills/ase-framework/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/ase-framework"><img src="https://agentmods.dev/badge/skills/sfetni/deep-matter-chem-skills/ase-framework.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 | $0.00002 | $0.07927 |
| Opus 5 | $0.00001 | $0.03964 |
| Sonnet 5 | $0.00000 | $0.01585 |
| Haiku 4.5 | $0.00000 | $0.00793 |
Grade A, and why
ase-framework 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 11d 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 — 627 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ASE Framework
Description
This skill covers the Atomic Simulation Environment (ASE) as a workflow hub for atomistic simulation: Atoms objects, calculators, constraints, optimizers, trajectories, file IO, structure manipulation, dataset generation, and conversion between DFT, MD, ML potentials, Phonopy, pymatgen, LAMMPS, and visualization tools. Invoke this skill when writing Python workflows that create, transform, calculate, relax, convert, or curate atomistic structures for computational materials science and chemistry.
Domain Context
ASE is a Python framework built around a small set of abstractions: an Atoms object stores symbols, positions, cell, periodic boundary conditions, arrays, constraints, and metadata; a calculator attaches an energy/force/stress backend; optimizers and molecular dynamics drivers move atoms using calculator results; IO functions convert between simulation formats. This makes ASE useful as glue code across VASP, Quantum ESPRESSO, CP2K, LAMMPS, EMT, MACE, CHGNet, and many other engines.
The convenience comes with responsibility. ASE does not know whether a converted structure is scientifically valid, whether a calculator working directory was reused incorrectly, whether a CIF lost partial occupancies, or whether a LAMMPS data file preserved element ordering. Many ASE failures are silent: cell and PBC metadata are dropped by XYZ, constraints are not supported by a target format, or an extxyz file stores forces in the wrong key. The workflow must check these invariants explicitly.
ASE sits at the boundary between representation and calculation. The same atomic configuration can be represented as a molecule with no periodic cell, a slab with pbc=[True, True, False], a fully periodic crystal, or a large supercell for phonons. The calculator interprets these choices physically: a DFT calculator with an unintended vacuum direction or missing PBC will compute a different problem, not just a differently formatted input.
For machine-learning potential work, ASE is often the canonical bridge between DFT outputs and training datasets. It reads VASP/QE/LAMMPS/XYZ trajectories, stores energies and forces in extxyz, applies constraints or filters, and evaluates MACE or CHGNet calculators during validation. This makes atom ordering, unit conventions, metadata, and provenance checks non-negotiable.
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.
- 11d ago First seen · 627 lines · 2 tokens per session scan A 3be76d347698
ase-framework is a skill published in the GitHub repository SFETNI/Deep-Matter-Chem-Skills (6 stars, last pushed 1mo ago), licensed MIT. It adds 2 tokens to every session and 7,927 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
pytdc
Use Therapeutics Data Commons through the PyTDC Python package for registry discovery, approved dataset access, task-aware splits (scaffold, cold-start, temporal, combination), evaluator metrics, benchmark groups, and bounded molecular-oracle workflows. Use this skill to find which TDC datasets exist for a therapeutic…
datamol
Pythonic wrapper around RDKit with a simplified interface and sensible defaults. Preferred for standard drug discovery work — SMILES/SELFIES/InChI conversion, molecule standardization and sanitization, descriptors, ECFP and other fingerprints, Tanimoto distance matrices, Butina clustering and diverse subset picking…
python-environment-management
Use when you are preparing to run Hi-C data normalization or read alignment filtering steps that depend on Python modules (iced, pysam, numpy, scipy) and you need to ensure consistent module versions across multiple runs or compute nodes.
python-dependency-version-resolution
Use when when setting up a new conda environment for a Python-based bioinformatics pipeline and you need to confirm that all declared dependencies (e.g., pysam >=0.15.4, bx-python >=0.8.8, numpy >=1.18.1, scipy >=1.4.
python-pandas-data-manipulation
Use when you have precomputed expected contact frequency tables (TSV format with columns like distbp, contactfrequency, nvalid) and need to apply log-binning and smoothing to group distance values into log-spaced bins, aggregate statistics within each bin, and export a cleaned, annotated output.
library-module-organization-and-accessibility
Use when you are building or extending a multi-module Python library for scientific computation (e.