Create conference talk outlines and iA Presenter markdown slides using the Story Circle narrative framework. Use when the user wants to structure a tech talk, create presentation slides, or needs help organizing talk ideas into a story-driven format.
Complete USGS gauge data integration workflow from spatial discovery to model validation. Handles gauge finding, data retrieval, matching to HEC-RAS features, boundary condition generation, initial conditions, real-time monitoring, and validation metrics (NSE, KGE). Use when working with USGS data, NWIS gauges, generating boundaries from observed flow, calibrating models, validating with observed data, or setting up operational forecasting.
Reads HEC-DSS files (V6 and V7) for boundary condition extraction using RasDss class. Handles JVM configuration, HEC Monolith download, catalog reading, and time series extraction. Use when working with DSS files, extracting boundary data, reading HEC-HMS output, or integrating DSS workflows. Triggers: DSS, HEC-DSS, boundary condition, time series, JVM, Java, catalog, pathname, HEC-HMS, Monolith, pyjnius, read DSS, extract DSS, DSS boundary.
Extract HEC-RAS hydraulic results from HDF files including water surface elevations (WSE), depths, velocities, and flows for both steady and unsteady simulations. Handles cross section time series, 2D mesh results, maximum envelopes, and dam breach results. Use when you need to extract, analyze, or post-process HEC-RAS simulation outputs, retrieve water levels, query velocity fields, get depth grids, extract flow data, analyze breach hydrographs, or pull hydraulic variables from .hdf result files.
Analyze spatial variability of NOAA Atlas 14 precipitation frequency estimates within HEC-RAS model domains using intelligent extent-based downloading. Helps determine whether uniform rainfall assumptions are appropriate for rain-on-grid modeling by calculating min/max/mean/range statistics within 2D flow areas or project extents. Uses NOAA CONUS NetCDF with HTTP byte-range requests for 99.9% data reduction compared to traditional state-level ZIP downloads. Primary sources: - ras_commander/precip/CLAUDE.md (lines 118-629) - Complete workflows - ras_commander/precip/Atlas14Grid.py - API reference - ras_commander/precip/Atlas14Variance.py - Variance analysis API - examples/725_atlas14_spatial_variance.ipynb - Working demonstration
Validate organized eBFE/BLE model using ras-commander dataframes. Uses init_ras_project() then checks plan_df, boundary_df, rasmap_df to verify: - All plan files exist - All DSS files exist with relative paths - All terrain files exist with relative paths - All HDF results accessible - No absolute paths (would cause GUI popups) Use after organizing eBFE model to verify it's actually runnable. Generates validation report and script for user re-verification.
Retrieves and processes AORC precipitation data for HEC-RAS/HMS models. Handles spatial averaging over watersheds, temporal aggregation, DSS export, and Atlas 14 design storms. Use when working with historical precipitation, AORC data, calibration workflows, design storm generation, rainfall analysis, SCS Type II distributions, AEP events, 100-year storms, or generating precipitation boundary conditions for rain-on-grid models. Triggers: precipitation, AORC, Atlas 14, design storm, rainfall, SCS Type II, AEP, 100-year, rain-on-grid, hyetograph, temporal distribution, areal reduction, calibration, historical precipitation.
Use when writing dialogue scenes or when user asks about character conversations - provides techniques for natural, character-consistent dialogue that reveals character and advances plot
Transform raw brain dumps (dictated freestyle) into structured implementation artifacts. Use when user has messy ideas, scattered thoughts, or dictated stream-of-consciousness about something they want to build. Produces contracts, phased PRDs, and implementation specs written to ./docs/ideation/{project-name}/.
探测用户的写作规范需求并加载对应文档。当用户提到AI味重、去AI味、自然、爽文、快节奏、爽点、无毒点、不降智、严肃文学、有深度、强情绪、打动人、甜文、撒糖、虐文、虐心、BE等关键词时自动激活。适用于讨论写作要求、AI去味方法、节奏控制、情感表达时使用。
Build NLP applications using transformers library, BERT, GPT, text classification, named entity recognition, and sentiment analysis
Implement the Ralph Wiggum iterative AI loop for Codex/Claude Code: create /ralph-loop and /cancel-ralph commands, wire a stop hook or wrapper loop, persist a fixed prompt, track iterations, and detect a completion promise with safety limits. Use when asked to build or integrate self-referential AI iteration loops, stop hooks, or command wrappers for repeated agent runs.
Build collaborative and content-based recommendation engines for product recommendations, personalization, and improving user engagement
Brief description of what this Skill does and when to use it. Include specific trigger phrases users would say. Use when user asks "trigger phrase 1", "trigger phrase 2", or wants to [outcome]. Max 1024 characters.
Reduce feature dimensionality using PCA, t-SNE, and feature selection for feature reduction, visualization, and computational efficiency
Build binary and multiclass classification models using logistic regression, decision trees, and ensemble methods for categorical prediction and classification
Analyze temporal data patterns including trends, seasonality, autocorrelation, and forecasting for time series decomposition, trend analysis, and forecasting models
Implement computer vision tasks including image classification, object detection, segmentation, and pose estimation using PyTorch and TensorFlow
Design and architect neural networks with various architectures including CNNs, RNNs, Transformers, and attention mechanisms using PyTorch and TensorFlow
Determine cause-and-effect relationships using propensity scoring, instrumental variables, and causal graphs for policy evaluation and treatment effects
Interpret machine learning models using SHAP, LIME, feature importance, partial dependence, and attention visualization for explainability
Build predictive models using linear regression, polynomial regression, and regularized regression for continuous prediction, trend forecasting, and relationship quantification
Identify unusual patterns, outliers, and anomalies in data using statistical methods, isolation forests, and autoencoders for fraud detection and quality monitoring
