GitHub organization
Agent skills by learningmatter-mit
- skills
- 129
- repository
- 1
Repositories by learningmatter-mit
Skills by learningmatter-mit, ranked
Ranked by score. Sort bymost stars,trending,newest,recently updated
| # | Skill | Repository | Stars | Used in | Tokens | Auto-check | Licence | Updated |
|---|---|---|---|---|---|---|---|---|
| 1 | Define a docking search box (center coordinates + box dimensions in Angstroms) from a co-crystal ligand, binding-site residues, or a saved JSON specification. | learningmatter-mit/ | 175 | — | ~2.9k | Automated safety check: Pass | MIT | yesterday |
| 2 | Build a solvated, charge-neutralized protein-ligand complex for OpenMM molecular dynamics simulation. | learningmatter-mit/ | 175 | — | ~2k | Automated safety check: Pass | MIT | yesterday |
| 3 | Identify and rank ligandable pockets on a protein structure or model using geometry (fpocket) or an ML predictor (P2Rank). | learningmatter-mit/ | 175 | — | ~4k | Automated safety check: Pass | MIT | yesterday |
| 4 | Calculate homolytic and heterolytic bond dissociation energies (BDEs) for all single bonds in a molecule using MLIPs with RDKit fragmentation. | learningmatter-mit/ | 175 | — | ~2.5k | Automated safety check: Pass | MIT | yesterday |
| 5 | Generate molecular conformers with RDKit ETKDG, relax with MLIPs, and rank by energy with Boltzmann weighting. | learningmatter-mit/ | 175 | — | ~1.3k | Automated safety check: Pass | MIT | yesterday |
| 6 | Query multiple MOF databases (QMOF via MPContribs; ARC-MOF DB7/Majumdar et al. | learningmatter-mit/ | 175 | — | ~1.9k | Automated safety check: Pass | MIT | yesterday |
| 7 | Query the Quantum MOF (QMOF) database via Materials Project's MPContribs platform for DFT-computed properties (bandgap) and optimized crystal structures of Metal-Organic Frameworks. | learningmatter-mit/ | 175 | — | ~670 | Automated safety check: Pass | MIT | yesterday |
| 8 | Search and download experimental InfraRed (IR), Mass spectra, and UV-Vis spectra data (JCAMP-DX format) for molecules. | learningmatter-mit/ | 175 | — | ~529 | Automated safety check: Pass | MIT | yesterday |
| 9 | Write and run custom ORCA input files for advanced electronic structure methods or settings not available through the SCINE wrapper, including multi-reference methods, excited states, relativistic… | learningmatter-mit/ | 175 | — | ~2.3k | Automated safety check: Pass | MIT | yesterday |
| 10 | Run DFT geometry optimization (minimization or TS search) on a molecular structure using ORCA via SCINE/ReaDuct wrapper. | learningmatter-mit/ | 175 | — | ~2k | Automated safety check: Pass | MIT | yesterday |
| 11 | Run a DFT or Coupled Cluster single-point energy calculation (with optional gradients/Hessian) on a molecular structure with ORCA through SCINE wrapper. | learningmatter-mit/ | 175 | — | ~1.8k | Automated safety check: Pass | MIT | yesterday |
| 12 | Dock small-molecule guests into a porous host material using the VOID library (Voronoi Clustering), generating multiple 3D conformers with RDKit and ranking generated complexes. | learningmatter-mit/ | 175 | — | ~918 | Automated safety check: Pass | MIT | yesterday |
| 13 | Extract explicit safety warnings, GHS classifications, LD50 toxicity profiles, and acute oral toxicity triage from PubChem PUG VIEW. | learningmatter-mit/ | 175 | — | ~702 | Automated safety check: Pass | MIT | yesterday |
| 14 | Verify non-periodic molecular TS connectivity with forward/reverse IRC using endpoint connectivity and RMSD checks. | learningmatter-mit/ | 175 | — | ~972 | Automated safety check: Pass | MIT | yesterday |
| 15 | Predict LC-MS/MS (MS2, tandem mass spectra) from SMILES via ICEBERG, a two-stage deep neural network. | learningmatter-mit/ | 175 | — | ~1.6k | Automated safety check: Pass | MIT | yesterday |
| 16 | Calculate activation barrier using Nudged Elastic Band (NEB) method with MLIPs. | learningmatter-mit/ | 175 | — | ~1.3k | Automated safety check: Pass | MIT | yesterday |
| 17 | Scripts for Wasserstein deconvolution of 1H NMR mixture spectra against reference spectra, reaction product prediction, time-series kinetics, and spectral plotting. | learningmatter-mit/ | 175 | — | ~2.3k | Automated safety check: Pass | MIT | yesterday |
| 18 | Predict 1H NMR spectra from SMILES strings via NMRdb.org SPINUS neural network prediction and nmrsim quantum mechanical spin simulation. | learningmatter-mit/ | 175 | — | ~1.8k | Automated safety check: Pass | MIT | yesterday |
| 19 | Generate transition state structures for chemical reactions using React-OT. | learningmatter-mit/ | 175 | — | ~914 | Automated safety check: Pass | MIT | yesterday |
| 20 | Find structurally similar chemical compounds using PubChem's 2D fast similarity engine via the PUG-REST API. | learningmatter-mit/ | 175 | — | ~614 | Automated safety check: Pass | MIT | yesterday |
| 21 | Set up and run molecular dynamics simulations of molecules in explicit solvent boxes using Packmol for box construction and MLIPs for dynamics. | learningmatter-mit/ | 175 | — | ~1.9k | Automated safety check: Pass | MIT | yesterday |
| 22 | Calculates gas adsorption isotherms via BVT/GCMC Monte Carlo simulations in a porous framework using MLIP. | learningmatter-mit/ | 175 | — | ~926 | Automated safety check: Pass | MIT | yesterday |
| 23 | Prepares supercells for porous frameworks based on minimum interplanar distance and relaxes them using standard MLIP relaxation tools. | learningmatter-mit/ | 175 | — | ~1.2k | Automated safety check: Pass | MIT | yesterday |
| 24 | Calculates Henry coefficient and heat of adsorption for a gas in a porous framework using Widom insertion with any supported MLIP. | learningmatter-mit/ | 175 | — | ~853 | Automated safety check: Pass | MIT | yesterday |
| 25 | Match an experimental spectrum (1H NMR, 13C NMR, IR) against predicted or database reference spectra for candidate ranking and structure confirmation. | learningmatter-mit/ | 175 | — | ~2.5k | Automated safety check: Pass | MIT | yesterday |
| 26 | Compute gas-phase thermodynamic quantities (H, S, G) and reaction thermochemistry (ΔH, ΔS, ΔG) using MLIPs with the ideal-gas/rigid-rotor/harmonic-oscillator approximation. | learningmatter-mit/ | 175 | — | ~1.4k | Automated safety check: Pass | MIT | yesterday |
| 27 | Optimize non-periodic molecular TS guesses and verify first-order saddle point from vibrational modes. | learningmatter-mit/ | 175 | — | ~876 | Automated safety check: Pass | MIT | yesterday |
| 28 | Calculate vibrational frequencies, normal modes, zero-point energy, and IR spectra of molecules and clusters using MLIPs. | learningmatter-mit/ | 175 | — | ~1.3k | Automated safety check: Pass | MIT | yesterday |
| 29 | Fetch biological assays and target proteins a chemical has been tested against via PubChem. | learningmatter-mit/ | 175 | — | ~693 | Automated safety check: Pass | MIT | yesterday |
| 30 | Query ChEMBL web services for targets, molecules, and curated bioactivity measurements (IC50, Ki, EC50, etc.). | learningmatter-mit/ | 175 | — | ~1.4k | Automated safety check: Pass | MIT | yesterday |
| 31 | 31.Drug DB Pdb Search, filter, and retrieve macromolecular structures from the RCSB Protein Data Bank (PDB), including metadata, bound ligands, and optional coordinate/validation downloads. | learningmatter-mit/ | 175 | — | ~1.1k | Automated safety check: Pass | MIT | yesterday |
| 32 | Query PubChem via PUG-REST to retrieve CIDs, computed properties, synonyms, and 2D/3D SDF structures. | learningmatter-mit/ | 175 | — | ~1.3k | Automated safety check: Pass | MIT | yesterday |
| 33 | Post-docking analysis of virtual screening results including score distributions, enrichment metrics (ROC AUC, enrichment factors), and ligand efficiency calculations. | learningmatter-mit/ | 175 | — | ~2.2k | Automated safety check: Pass | MIT | yesterday |
| 34 | Dock small-molecule ligands into a protein receptor using AutoDock Vina (Python API) and save ranked poses + docking metadata for reproducible virtual screening. | learningmatter-mit/ | 175 | — | ~2.3k | Automated safety check: Pass | MIT | yesterday |
| 35 | Prepare small-molecule ligands for docking and analysis via optional state enumeration, 3D conformer generation, MMFF/UFF minimization, and export to SDF + AutoDock PDBQT. | learningmatter-mit/ | 175 | — | ~772 | Automated safety check: Pass | MIT | yesterday |
| 36 | Compute single-trajectory MM-GBSA and / or MM-PBSA binding free energy estimates from a protein-ligand MD trajectory. | learningmatter-mit/ | 175 | — | ~4.7k | Automated safety check: Pass | MIT | yesterday |
| 37 | Validate docked or generated ligand poses for physical plausibility using PoseBusters, filtering out chemically invalid or clashing poses before downstream refinement. | learningmatter-mit/ | 175 | — | ~1.3k | Automated safety check: Pass | MIT | yesterday |
| 38 | Run a protein-ligand MD simulation in OpenMM with energy minimization, restrained equilibration, and production NPT, producing trajectory and checkpoint files for downstream analysis. | learningmatter-mit/ | 175 | — | ~1.4k | Automated safety check: Pass | MIT | yesterday |
| 39 | Prepare macromolecular receptor structures (PDB/mmCIF or RCSB PDB ID) for docking or simulation by fixing common structure issues and adding hydrogens. | learningmatter-mit/ | 175 | — | ~1.1k | Automated safety check: Pass | MIT | yesterday |
| 40 | Compute symmetry-corrected heavy-atom RMSD between docked poses and a reference crystal ligand to validate docking protocols. | learningmatter-mit/ | 175 | — | ~2.3k | Automated safety check: Pass | MIT | yesterday |
| 41 | Predict synthetic accessibility and retrosynthetic pathways for novel molecules using the IBM RXN API. | learningmatter-mit/ | 175 | — | ~647 | Automated safety check: Pass | MIT | yesterday |
| 42 | Analyze a protein-ligand MD trajectory to compute ligand RMSD, pocket RMSF, hydrogen bonds, contact occupancy, and protein-ligand interaction fingerprints over time. | learningmatter-mit/ | 175 | — | ~1.2k | Automated safety check: Pass | MIT | yesterday |
| 43 | Search and retrieve research papers from ArXiv API for scientific research. | learningmatter-mit/ | 175 | — | ~634 | Automated safety check: Pass | MIT | yesterday |
| 44 | Set up, check or troubleshoot how AtomisticSkills runs on this machine -- creating its Python environments, connecting its MCP servers, choosing uv or a container runtime, and configuring API keys. | learningmatter-mit/ | 175 | — | ~1.2k | Automated safety check: Notes | MIT | yesterday |
| 45 | Search and retrieve preprint metadata from bioRxiv and medRxiv APIs for biological and medical research. | learningmatter-mit/ | 175 | — | ~854 | Automated safety check: Pass | MIT | yesterday |
| 46 | Retrieve extensive literature (PubMed) and patent associated with a specific chemical compound via PubChem. | learningmatter-mit/ | 175 | — | ~515 | Automated safety check: Pass | MIT | yesterday |
| 47 | Retrieves averaged elemental prices and provides direct vendor purchase links for elements and precursor compounds. | learningmatter-mit/ | 175 | — | ~566 | Automated safety check: Pass | MIT | yesterday |
| 48 | Search for patents by keyword, material name, or assignee using free data sources (Google Patents). | learningmatter-mit/ | 175 | — | ~540 | Automated safety check: Pass | MIT | yesterday |
Questions, answered from the data.
What is the best skill by learningmatter-mit?
Drug Binding Site Definition from learningmatter-mit/AtomisticSkills ranks first of the 129 skills by learningmatter-mit listed here, with the highest score: its repository has 175 GitHub stars, its SKILL.md loads about 2.9k tokens and it passes the automated safety check with no findings. Next come Drug Complex System Builder and Drug Pocket Detection.
Are learningmatter-mit's skills official?
None yet. All 129 skills by learningmatter-mit listed here come from community repositories; a skill counts as official when the product's own GitHub organization publishes it.
How are these skills ranked?
By Skill Navigator score, which combines the GitHub stars of the skill's repository (shared across that repo's skills and discounted for large collections), how many other GitHub owners carry a copy of the skill, and automated SKILL.md quality checks, minus penalties for safety-check warnings and for each further skill from the same repository. Skills that fail the safety check are not listed.