Claude Skills
The open format is called Agent Skills and works in Claude Code, Codex, Cursor and other agents — most people know it as Claude Skills.
Every Agent Skill we could find on GitHub, deduplicated by content. 79 870 files from 1 769 authors, of which 62 217 are unique — the rest is the same skill repackaged into someone else's repository. For each one: what it weighs in tokens, whether it ships runnable scripts, and which MCP servers it needs.
Track investment theses across their lifecycle — from screening idea to closed position with postmortem. Register theses from screener outputs, manage state transitions, attach position sizing, review due dates, and generate postmortem reports with P&L and MAE/MFE analysis. Trigger when user says "register thesis", "track this idea", "thesis status", "review due", "close position", "postmortem", or "trading journal".
Configure ASE MACE calculator adapter settings for ASE workflows. Use when ASE workflow tasks require MACE backend setup including model path/version, device/precision, stress availability, and inference controls.
Unified ASE router skill with a tree of subskills for static/relax/MD/NEB workflows and backend adapters (GPAW, MACE). Use when you need backend-agnostic workflow orchestration while keeping calculator-specific setup isolated in adapter subskills, with reproducible task preparation as output.
Configure ASE GPAW calculator adapter settings for ASE workflows. Use when ASE workflow tasks require GPAW backend setup including mode, k-point, convergence, and restart policies.
Run ReacNetGenerator on reactive MD trajectories to generate reaction networks and reports. Use when the user wants to analyze LAMMPS dump/xyz/bond trajectories with ReacNetGenerator. Handles LAMMPS dump quirks like x/y/z vs xs/ys/zs by converting to x/y/z (orthorhombic + triclinic supported via reacnet-md-tools). Can infer atomname order from a LAMMPS data file. Runs via local reacnetgenerator if available or via `uvx --from reacnetgenerator ...`. Writes outputs into `out/<input_basename>/` with logs and a summary.
> General phonon-workflow skill built around phonopy, independent of force backend. USE WHEN you need to prepare finite-displacement phonon calculations, build force constants, and analyze phonon properties (band structure, DOS, thermal quantities) while obtaining forces from different engines such as VASP, Quantum ESPRESSO, or ML force fields.
Create new Agent Skills following the agentskills.io specification. Use when the user wants to create, scaffold, or design a new skill for AI agents. Handles SKILL.md generation, directory structure setup, and validation.
> It is a specification for semantic workflows used by agents to plan, generate, formalize, summarize, and execute complex tasks, projects, experiments,and research efforts for agents, requiring explicit structure, lazy loading,scoped context, evidence-grounded routing, and human review at critical checkpoints. USE WHEN the user asks for a complex task, project, experiment, or research effort that needs to be carefully planned before execution USE WHEN the user provides a text-based plan and wants it to be made more detailed and formalized according to this specification. USE WHEN the user asks to summarize ongoing or completed work into a reusable workflow manifest. USE WHEN the user specifies the location of an existing agent workflow and wants it loaded and executed according to the specification.
Route ASE calculator-backend requests to adapter subskills based on backend choice. Use when ASE workflows need backend-specific calculator setup (for example GPAW or MACE) while keeping workflow logic backend-agnostic.
Prepare ASE geometry-optimization workflow tasks with backend-agnostic controls. Use when the user needs structural relaxation while selecting optimizer, convergence target, constraints, and output trajectory policy independently of calculator backend.
Route ASE atomistic workflow requests to task-specific subskills based on user intent. Use when the user asks for ASE-based static, relaxation, MD, or NEB workflows and you must apply consistent workflow controls independent of calculator backend.
Prepare ASE NEB workflow tasks with backend-agnostic controls. Use when the user needs reaction-path optimization between initial/final states with explicit image construction, spring settings, and convergence controls.
> A command-line utility for converting and manipulating over 50 atomic simulation data formats, including outputs from DFT and MD software (VASP, LAMMPS, Gaussian, QE, CP2K, ABACUS, etc.). USE WHEN you need to convert structural or trajectory files between different computational chemistry formats, or when parsing raw simulation outputs into structured training datasets (e.g., deepmd/raw, deepmd/npy, deepmd/hdf5) for DeePMD-kit.
Prepare ASE static (single-point) workflow tasks with backend-agnostic workflow controls. Use when the user needs one-shot energy/force/stress evaluation through an ASE calculator adapter.
> A versatile CLI tool for converting molecular file formats, generating 3D atomic coordinates from SMILES, rendering 2D chemical structure images, and preparing or extracting structures for computational workflows. USE WHEN you need to convert between chemical file formats (e.g., xyz, pdb, mol, smi, gjf), generate 3D structures from SMILES using `--gen3d`, render molecule images (PNG/SVG), or extract geometries from simulation logs to build new inputs.
Prepare ASE molecular-dynamics workflow tasks with backend-agnostic controls. Use when the user needs finite-temperature trajectories with explicit ensemble, timestep, thermostat, and output policies.
> A tool for generating initial packed molecular configurations (XYZ format) from single-molecule structures by calculating box dimensions, writing input scripts, and executing Packmol. USE WHEN you need to randomly pack a specific number of molecules into a simulation box (defined by target density or fixed lengths) to create starting geometries for molecular dynamics or related computational chemistry workflows.
Prepare, explain, validate, and run DP-GEN simplify workflows for reducing repeated or redundant DeepMD datasets. Use when the user wants to generate or modify `param.json` and `machine.json`, run `dpgen simplify param.json machine.json`, organize repeated simplify experiments, or inspect simplify outputs.
Run Python inference with DeePMD-kit models using the DeepPot API. Use when the user wants to load a trained/frozen DeePMD model (.pth or .pb) or a built-in pretrained model (e.g., DPA-3.2-5M) in Python, predict energy/force/virial for atomic configurations, evaluate descriptors, or calculate model deviation between multiple models. Also covers using `dp test` CLI for batch evaluation against labeled data.
Train DeePMD-kit models with progressive disclosure. Use when the user wants to train a DeePMD-kit potential, prepare an input.json, choose between model families such as se_e2_a/DeepPot-SE and DPA3, run `dp train`, monitor learning curves, freeze checkpoints, or test trained models. Start with model selection and read only the selected model reference under `models/` when model-specific configuration is needed.
Fine-tune a DPA3 model in DeePMD-kit using the PyTorch backend. Use when the user wants to adapt a pre-trained DPA3 model to a new downstream dataset. Supports fine-tuning from a self-trained DPA3 model (.pt checkpoint), from a multi-task pre-trained model, or from a built-in pretrained model downloaded via `dp pretrained download` (e.g., DPA-3.1-3M, DPA-3.2-5M, DPA-3.3-1M). Covers single-task and multi-task fine-tuning workflows.
> Structure manipulation and crystal analysis workflows based on pymatgen. USE WHEN you need to read/write common atomistic formats (CIF, POSCAR, XYZ), build supercells, perform site substitution/doping, inspect symmetry (space group), or compute local structure descriptors for materials tasks.
> A command-line tool in AmberTools for preparing small molecules or non-standard residues within GAFF/AMBER-compatible chemical space for molecular mechanics simulations, by automating atom/bond typing, charge generation or import, and force-field–compatible input generation. USE WHEN you are working in AMBER, dealing with molecules not covered by standard force fields, and already have a structure that can be processed (e.g., pdb, mol2, ac, gout). Typical use cases include parameterizing ligands or modified residues (assigning atom/bond types, generating or reading partial charges), converting structures from upstream tools into mol2/prepi formats, and preparing topology-ready inputs for downstream tools such as LEaP. DO NOT USE for standard residues, metal complexes, inorganic systems, or when no valid molecular structure is available (e.g., only SMILES).
> A standardized CLI wrapper for RDKit 3D/2D conformer generation that samples multiple conformers per molecule (ETKDGv3, default 10), optimizes each with a force field (MMFF94s/UFF), keeps the lowest-energy conformer, automatically falls back to 2D layout on total embedding failure with a printed warning, and writes results to SDF or XYZ format. USE WHEN you need to generate 3D (or 2D fallback) molecular geometries from SMILES datasets (.csv/.smi) for downstream tasks such as docking, visualization, or 3D-descriptor computation.
> A tool and knowledge base for running molecular dynamics (MD) simulations in LAMMPS with the DeePMD-kit plugin. It handles input script preparation, ensemble selection (NVE/NVT/NPT), and job execution via `uv` or offline binaries. USE WHEN you need to set up, write, explain, or execute a LAMMPS molecular dynamics simulation using a DeePMD machine learning potential (e.g., `graph.pb`).
Run reactive molecular dynamics simulations in LAMMPS with the ReaxFF potential, including preparing input scripts (pair_style reaxff + fix qeq/reaxff), mapping LAMMPS atom types to elements via pair_coeff, choosing ensembles (NVE/NVT/NPT), and adding common ReaxFF diagnostics such as species analysis. Use when the user wants LAMMPS+ReaxFF workflows or needs a working, annotated `input.lammps` template.
> A standardized CLI wrapper for RDKit molecular featurization workflows that handles physicochemical descriptor computation (outputs .csv) and molecular fingerprint extraction (outputs .npy or .csv), with built-in SMILES validation. USE WHEN you need to compute RDKit molecular descriptors or fingerprints from SMILES datasets (.csv/.smi), or when you want to list all available descriptor names and presets.
> A standardized CLI wrapper for Uni-Mol molecular ML workflows that handles representation extraction (embeddings), model training (regression/classification), and property prediction with built-in RDKit SMILES validation. USE WHEN you need to generate molecular embeddings, train machine learning models for chemical properties, or run predictions on SMILES datasets (.csv/.smi) using the Uni-Mol framework.
Prepare ABINIT electronic-analysis task inputs from prior converged context. Use when the user requests post-ground-state electronic analyses and needs prerequisite-aware setup.
Route ABINIT requests to task-specific subskills based on user intent. Use when the user asks for ABINIT workflows and you must decide between static, relaxation, molecular dynamics, or electronic-analysis preparation. This orchestration skill dispatches to the correct ABINIT subskill and enforces consistent handoff to submission skills.
Prepare ABINIT geometry-relaxation task inputs from a user-provided structure and optimization settings. Use when the user needs ion-only or cell-coupled structural optimization with explicit force/stress convergence controls.
Prepare ABINIT molecular-dynamics task inputs from a user-provided structure and MD controls. Use when the user needs finite-temperature trajectories with explicit ensemble, timestep, and thermostat controls.
Prepare ABINIT single-point (static) task inputs from a user-provided structure and essential DFT settings. Use when the user needs total-energy/electronic SCF evaluation with explicit ABINIT cutoff, k-point, and SCF controls.
Route CP2K requests to task-specific subskills based on user intent. Use when the user asks for CP2K workflows and you must decide between static, relaxation, molecular dynamics, or electronic-analysis preparation. This orchestration skill dispatches to the correct CP2K subskill and enforces consistent handoff to submission skills.
Prepare CP2K molecular-dynamics task inputs from a user-provided structure and MD controls. Use when the user needs finite-temperature trajectories with explicit ensemble, timestep, and thermostat/barostat settings.
Prepare CP2K electronic-analysis task inputs from prior converged context. Use when the user requests post-ground-state electronic analyses and needs prerequisite-aware setup.
Prepare CP2K geometry-relaxation task inputs from a user-provided structure and optimization settings. Use when the user needs ion-only or cell-coupled optimization with explicit optimizer and convergence controls.
Route GPAW DFT requests to task-specific subskills based on user intent. Use when the user asks for GPAW workflows and you must decide between static SCF, relaxation, DOS, or band-structure task preparation. This orchestration skill dispatches to the correct GPAW subskill and enforces consistent handoff to submission skills.
Prepare CP2K single-point (static) task inputs from a user-provided structure and essential DFT settings. Use when the user needs total-energy/electronic SCF evaluation with explicit CP2K basis/potential and SCF controls.
Prepare GPAW band-structure workflow scripts from existing ground-state context and user-specified k-path settings. Use when the user requests electronic band-structure calculations with explicit prerequisite checks and path-definition handling.
Prepare GPAW DOS workflow scripts from existing ground-state context and user-specified DOS settings. Use when the user requests total/projected DOS setup with explicit prerequisite checks against prior converged calculations.
Prepare GPAW geometry-relaxation task inputs/scripts from a user-provided structure and essential optimization settings. Use when the user needs structure optimization with explicit optimizer and force-convergence policies.
Prepare GPAW static SCF task inputs/scripts from a user-provided structure and essential DFT settings. Use when the user needs single-point total-energy/electronic calculations with explicit GPAW calculator settings and reproducible run script layout.
Prepare SIESTA electronic-analysis task inputs from prior converged context. Use when the user requests post-ground-state electronic analyses and needs prerequisite-aware setup.
Route SIESTA requests to task-specific subskills based on user intent. Use when the user asks for SIESTA workflows and you must decide between static, relaxation, molecular dynamics, or electronic-analysis preparation. This orchestration skill dispatches to the correct SIESTA subskill and enforces consistent handoff to submission skills.
Generate Quantum ESPRESSO DFT input tasks from a user-provided structure plus user-specified DFT settings. Use when the user wants to prepare QE calculations such as SCF, NSCF, relax, vc-relax, MD, bands, DOS, or phonons starting from a structure file or coordinates together with pseudopotentials, functional choice, cutoffs, k-point settings, smearing, spin/charge, and convergence parameters. This skill prepares the QE task only; use a separate submission skill such as dpdisp-submit to submit the generated task.
Prepare SIESTA geometry-relaxation task inputs from a user-provided structure and optimization settings. Use when the user needs ion-only or cell-coupled structural optimization with explicit force/stress controls.
Prepare SIESTA single-point (static) task inputs from a user-provided structure and essential DFT settings. Use when the user needs total-energy/electronic SCF evaluation with explicit SIESTA mesh, basis, and SCF controls.
Prepare SIESTA molecular-dynamics task inputs from a user-provided structure and MD controls. Use when the user needs finite-temperature trajectories with explicit ensemble, timestep, and thermostat controls.
Route VASP DFT requests to task-specific subskills based on user intent. Use when the user asks for VASP workflows and you must decide between static SCF, relaxation, DOS, or band-structure task preparation. This orchestration skill does not own detailed input generation logic; it dispatches to the correct VASP subskill and enforces consistent handoff to submission skills.
Prepare VASP DOS workflow inputs from existing SCF artifacts and user-specified DOS settings. Use when the user requests total/projected DOS setup and needs INCAR/KPOINTS preparation with explicit prerequisite checks against prior SCF runs.
Prepare VASP static SCF input tasks from a user-provided structure and essential DFT settings. Use when the user needs single-point electronic structure/total-energy calculations with INCAR generation, KSPACING-based k-point policy (or explicit KPOINTS on request), and POTCAR mapping instructions.
Prepare VASP geometry-relaxation input tasks from a user-provided structure and essential DFT settings. Use when the user needs ionic or cell-coupled relaxation and requires explicit ISIF-driven relaxation intent mapping, INCAR generation, and POTCAR mapping instructions.
Prepare VASP band-structure workflow inputs from existing SCF context and user-specified band-path settings. Use when the user requests electronic band-structure calculations and needs explicit prerequisite checks, line-mode KPOINTS path setup, and stage-specific INCAR preparation.
Prepare DFTB+ molecular-dynamics task inputs from a user-provided structure and MD controls. Use when the user needs finite-temperature trajectories using DFTB+.
Prepare DFTB+ electronic-analysis task inputs based on prior ground-state context. Use when the user requests band/DOS-style analyses and needs prerequisite-aware setup.
Route DFTB+ requests to task-specific subskills based on user intent. Use when the user asks for DFTB+ workflows and you must decide between static, relaxation, molecular dynamics, or electronic-structure post-ground-state preparation. This orchestration skill dispatches to the correct subskill and enforces consistent handoff to submission skills.
Prepare DFTB+ geometry-relaxation task inputs from a user-provided structure and optimization settings. Use when the user needs ion-only or cell-coupled structural optimization.
Prepare DFTB+ single-point (static) task inputs from a user-provided structure and essential SCC/settings choices. Use when the user needs total-energy/electronic single-point evaluation.
Prepare and explain xTB semiempirical quantum-chemistry workflows for single-point energy, forces, charges, dipole, geometry optimization, and molecular dynamics. Use when the user asks for xTB calculations directly, or wants to use xTB through Python/ASE/dpdata bridges while keeping xTB as the primary method rather than as an ASE-only backend.
Claude Skills — questions
Answers built from the skills we actually parsed.