Total 50,370 skills, Data Processing has 2557 skills
Showing 12 of 2557 skills
通过专利ID或公开号查询智慧芽专利数据库中的专利著录（书目）信息。当用户提到专利著录信息查询、专利书目信息、专利申请人查询、专利发明人查询、专利分类号、专利摘要获取、专利引用分析、专利优先权主张、专利申请引用、专利审查员信息、patent bibliographic data, inventor lookup, applicant lookup, patent classification, patent metadata, PatSnap, patent citations时触发此技能。即使用户未明确提及"著录信息"，只要其需求涉及通过专利ID或公开号查询特定专利的详细元数据，也应触发此技能。
Implied volatility analysis for options via Longbridge — IV vs HV comparison, IV percentile rank, volatility smile and skew, options pricing assessment, strategy selection guidance. Triggers: "隐含波动率", "IV", "期权波动率", "波动率偏斜", "波动率微笑", "HV", "历史波动率", "IV百分位", "期权定价", "隱含波動率", "期權波動率", "波動率偏斜", "波動率微笑", "歷史波動率", "IV百分位", "期權定價", "implied volatility", "IV percentile", "volatility smile", "volatility skew", "HV vs IV", "options pricing", "vol surface", "TSLA.US implied vol".
Historical-volatility (HV) regime strategy via Longbridge Securities — computes 20-day and 60-day HV, ranks the current level as a percentile over the past year, and recommends a vol regime trade: long volatility (buy straddle) when HV percentile < 25%; short volatility (sell straddle / iron condor) when HV percentile > 75%; neutral otherwise. Triggers: "波动率策略", "历史波动率", "低波动率", "高波动率", "波动率分位", "做多波动率", "做空波动率", "波動率策略", "歷史波動率", "低波動率", "高波動率", "波動率分位", "做多波動率", "做空波動率", "volatility strategy", "historical volatility", "low volatility", "high volatility", "volatility percentile", "long volatility", "short volatility", "vol regime", "HV20", "HV60", "buy straddle", "sell straddle", "iron condor".
Risk-return optimisation for investment portfolios via Longbridge — builds risk-adjusted return-optimal portfolios based on fund size, risk preference (conservative / balanced / aggressive), and investment horizon. Asset allocation across equities / bonds / cash / commodities / alternatives. Evaluates current portfolio efficiency versus the efficient frontier. Triggers: "风险收益优化", "组合效率", "有效前沿", "风险偏好配置", "最优组合", "风险调整收益", "大类资产配置", "投资组合优化", "風險收益優化", "組合效率", "有效前沿", "風險偏好配置", "最優組合", "risk-return optimization", "portfolio efficiency", "efficient frontier", "risk preference", "optimal portfolio", "risk-adjusted return", "asset class allocation", "portfolio optimisation", "mean variance".
Landscape evolution and surface process modelling in Python. Build 2D numerical models for erosion, hydrology, soil transport, and geomorphology. Use when Claude needs to: (1) Model landscape evolution over time, (2) Simulate river/stream erosion, (3) Route water flow across terrain, (4) Model hillslope diffusion processes, (5) Simulate weathering and soil production, (6) Analyze drainage networks, (7) Combine multiple geomorphic processes, (8) Load/save DEM data for modeling.
Stereonet plots for structural geology using matplotlib. Create lower-hemisphere stereographic projections for orientation data. Use when Claude needs to: (1) Create stereonet plots for structural data, (2) Plot planes as great circles or poles, (3) Plot lineations with trend/plunge, (4) Generate density contours for orientations, (5) Calculate mean orientations and statistics, (6) Analyze fold axes with pi-diagrams, (7) Convert between strike/dip and trend/plunge formats.
Petrophysical analysis and formation evaluation from well logs. Calculate porosity, water saturation, permeability, and lithology. Use when Claude needs to: (1) Calculate shale volume from gamma ray, (2) Compute porosity from density/neutron/sonic logs, (3) Estimate water saturation using Archie or Simandoux, (4) Calculate permeability from porosity and saturation, (5) Perform pay zone identification, (6) Conduct multi-mineral analysis, (7) Generate petrophysical summation plots.
Magnetotelluric data processing and modelling. Read EDI files, analyze MT responses, perform inversions, and visualize resistivity models. Use when Claude needs to: (1) Read/write EDI files, (2) Process MT impedance tensors, (3) Analyze phase tensors and dimensionality, (4) Plot apparent resistivity and phase curves, (5) Create pseudosections, (6) Perform strike analysis, (7) Run 1D inversions, (8) Prepare data for 2D/3D modelling.
Compute surface wave dispersion curves for layered Earth models using the Thomson-Haskell matrix method with Numba acceleration. Use when Claude needs to: (1) Calculate Rayleigh or Love wave phase velocities, (2) Compute group velocity dispersion, (3) Generate sensitivity kernels for inversion, (4) Forward model dispersion curves from velocity profiles, (5) Compare dispersion between different Earth models, (6) Set up surface wave tomography workflows.
3D visualization and mesh analysis for geoscience data using PyVista/VTK. Use when Claude needs to: (1) Create 3D visualizations of geological models, (2) Render seismic volumes or voxel data, (3) Visualize point clouds or well paths, (4) Plot surfaces and meshes in 3D, (5) Read/write VTK, STL, OBJ files, (6) Create cross-sections through 3D models, (7) Export publication-quality figures or interactive HTML.
Linear operators for large-scale inverse problems with matrix-free representations. Use when Claude needs to: (1) Define linear operators for forward/adjoint operations, (2) Solve inverse problems (deconvolution, imaging, tomography), (3) Apply signal processing transforms (FFT, convolution, derivatives), (4) Compose operators for complex workflows, (5) Perform regularized inversion with smoothness or sparsity constraints, (6) Process seismic or image data at scale.
Segment integration. Manage Workspaces. Use when the user wants to interact with Segment data.