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概要と使いどころ
PostgreSQL-specific development assistant focusing on unique PostgreSQL features, advanced data types, and PostgreSQL-exclusive capabilities. Covers JSONB operations, array types, custom types, range/geometric types, full-text search, window functions, and PostgreSQL extensions ecosystem.
日本語の概要は準備中です。原文の説明を表示しています。
Use when writing Playwright tests, fixing flaky tests, debugging failures, implementing Page Object Model, configuring CI/CD, optimizing performance, mocking APIs, handling authentication or OAuth, testing accessibility (axe-core), file uploads/downloads, date/time mocking, WebSockets, geolocation, permissions, multi-tab/popup flows, mobile/responsive layouts, touch gestures, GraphQL, error handling, offline mode, multi-user collaboration, third-party services (payments, email verification), console error monitoring, global setup/teardown, test annotations (skip, fixme, slow), test tags (@smoke, @fast, @critical, filtering with --grep), project dependencies, security testing (XSS, CSRF, auth), performance budgets (Web Vitals, Lighthouse), iframes, component testing, canvas/WebGL, service workers/PWA, test coverage, i18n/localization, Electron apps, or browser extension testing. Covers E2E, component, API, visual, accessibility, security, Electron, and extension testing.
日本語の概要は準備中です。原文の説明を表示しています。
Interactive visualization with Plotly. 40+ chart types (scatter, line, heatmap, 3D, geographic) with hover, zoom, pan. Two APIs: Plotly Express (DataFrame) and Graph Objects (fine control). For static publication figures use matplotlib; for statistical grammar use seaborn.
日本語の概要は準備中です。原文の説明を表示しています。
NCBI Gene via E-utilities: curated records across 1M+ taxa. Official symbols, aliases, RefSeq IDs, summaries, coordinates, GO, interactions. Use for gene ID resolution and cross-species function queries. For sequences use Ensembl; for expression use geo-database.
日本語の概要は準備中です。原文の説明を表示しています。
Cloud quantum chemistry platform with Python SDK. Run geometry optimization, conformer generation, torsional scans, and energy minimization (DFT/semiempirical), and retrieve properties (dipole, partial charges, frontier orbitals) — no local QC software or HPC needed.
日本語の概要は準備中です。原文の説明を表示しています。
mdtraj molecular dynamics trajectory analysis (Python). Reads DCD/XTC/TRR/NetCDF/H5/PDB topologies and trajectories; computes RMSD vs time, radius of gyration, per-residue RMSF, residue-residue contact frequency maps, phi/psi torsions for Ramachandran plots (general + Gly/Pro), and 8-state DSSP secondary structure. Modules: trajectory I/O, geometry (distances/angles/dihedrals), structural analysis (RMSD/Rg/RMSF/SASA), contacts, hydrogen bonds, secondary structure (DSSP), NMR observables. For broader atom-selection grammar use mdanalysis-trajectory; for running MD simulations use OpenMM/GROMACS.
日本語の概要は準備中です。原文の説明を表示しています。
Python Materials Genomics library for structure analysis, thermodynamics, and electronic properties. Parse/create crystal structures (CIF, POSCAR), query Materials Project for DFT-computed properties, analyze phase and Pourbaix diagrams, compute XRD patterns, generate DFT inputs for VASP, Quantum ESPRESSO, CP2K. Alternatives: ASE (MD/geometry), AFLOW (high-throughput), OVITO (visualization).
日本語の概要は準備中です。原文の説明を表示しています。
Pipeline for Neuropixels extracellular electrophysiology: probe geometry (ProbeInterface), Kilosort sorting via SpikeInterface, quality metrics, unit curation (ISI, firing rate, SNR), post-sort analysis (PSTH, tuning curves, population decoding). Supports Neuropixels 1.0/2.0/Ultra in rodent/primate experiments.
日本語の概要は準備中です。原文の説明を表示しています。
NEB-IRC activation energy pipeline for reaction barriers using GFN2-xTB and pysisyphus. Optimize reactant and product geometries, run CI-NEB path search, optimize the transition state with a Hessian, verify with IRC (one imaginary mode, endpoints matching reactant/product, single NEB maximum), and report the electronic and Gibbs barriers. Use when you need a transition state, reaction barrier, activation energy, minimum energy path, or intrinsic reaction coordinate. Covers reactant/product atom-ordering pitfalls, feasibility sizing for single-core runs, and thermochemistry corrections. Renders an IRC energy-profile plot and an animated TS imaginary-mode HTML viewer. For 2D reaction scheme drawing use rdkit-chemdraw-cdxml.
日本語の概要は準備中です。原文の説明を表示しています。
Convert AutoCAD DWG files (1983-2026) to Excel databases using DwgExporter CLI. Extract layers, blocks, attributes, and geometry data without Autodesk licenses.
日本語の概要は準備中です。原文の説明を表示しています。
Apply geographic location factors to CWICR estimates. Adjust costs for regional labor rates, material prices, and market conditions.
日本語の概要は準備中です。原文の説明を表示しています。
Convert IFC files (2x3, 4x1, 4x3) to Excel databases using IfcExporter CLI. Extract BIM data, properties, and geometry without proprietary software.
日本語の概要は準備中です。原文の説明を表示しています。
Detect and analyze geometric clashes in BIM models. Identify MEP, structural, and architectural conflicts before construction.
日本語の概要は準備中です。原文の説明を表示しています。
Detect and analyze geometric clashes between BIM elements. Identify hard clashes, soft clashes, and workflow conflicts using spatial analysis and rule-based detection.
日本語の概要は準備中です。原文の説明を表示しています。
Databricks SQL (DBSQL) advanced features and SQL warehouse capabilities. This skill MUST be invoked when the user mentions: "DBSQL", "Databricks SQL", "SQL warehouse", "SQL scripting", "stored procedure", "CALL procedure", "materialized view", "CREATE MATERIALIZED VIEW", "pipe syntax", "|>", "geospatial", "H3", "ST_", "spatial SQL", "collation", "COLLATE", "ai_query", "ai_classify", "ai_extract", "ai_gen", "AI function", "http_request", "remote_query", "read_files", "Lakehouse Federation", "recursive CTE", "WITH RECURSIVE", "multi-statement transaction", "temp table", "temporary view", "pipe operator". SHOULD also invoke when the user asks about SQL best practices, data modeling patterns, or advanced SQL features on Databricks.
日本語の概要は準備中です。原文の説明を表示しています。
Account assignment by revenue potential, geography, relationship. Workload balancing, TAM/SAM calculation, coverage models.
日本語の概要は準備中です。原文の説明を表示しています。
Use GemPy 3.x for CPU-first 3-D implicit geological modeling, structural data preparation, grid evaluation, visualization, persistence, and optional integrations.
日本語の概要は準備中です。原文の説明を表示しています。
Use geemap for Google Earth Engine interactive mapping, geospatial conversion, visualization, timelapse, and ML helper workflows.
日本語の概要は準備中です。原文の説明を表示しています。
Guides Fiona workflows for GDAL-backed vector data I/O, GeoJSON-like features and schemas, CRS transforms, local virtual files, cloud-session boundaries, and the fio CLI.
日本語の概要は準備中です。原文の説明を表示しています。
Create and troubleshoot Folium maps, GeoJSON layers, choropleths, and plugins for browser-rendered Leaflet output.
日本語の概要は準備中です。原文の説明を表示しています。
Use the city2graph 1.0.0 Python package to construct, validate, transform, and troubleshoot geospatial, spatial-topology, morphology, mobility, transportation, heterogeneous, and GNN-ready graphs.
日本語の概要は準備中です。原文の説明を表示しています。
Use ArcGIS API for Python for GIS administration, spatial dataframes, feature/raster analysis, mapping, location services, geospatial deep learning, app automation, and Knowledge Graph workflows.
日本語の概要は準備中です。原文の説明を表示しています。
Guide 3DDFA Python inference, geometry rendering, training/evaluation, and optional C++ ONNX workflows for 3D dense face alignment.
日本語の概要は準備中です。原文の説明を表示しています。
Research why neural architectures and training methods work through forward computation, geometry, gradients, optimization dynamics, historical experiments, and competing explanations. Use for deep mechanism questions about neural networks, AI models, layers, representations, or training; explain the meaning in plain language with evidence and explicit limits.
日本語の概要は準備中です。原文の説明を表示しています。