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documents

38

parsing-hecras-geometry

Parses and modifies HEC-RAS plain text geometry files (.g##) using fixed-width FORTRAN format. Handles cross sections, storage areas, bridges, culverts, lateral structures, and Manning's n tables. Use when reading geometry files, modifying cross sections, updating roughness, extracting structure data, parsing .g## files, working with XS station-elevation data, or analyzing bridge/culvert geometry. Keywords: parse, geometry, .g##, cross section, XS, Manning's n, bridge, culvert, storage, fixed-width, lateral structure, inline weir, 2D land cover, bank stations.

gpt-cmdr

content-media

cli-tools

38

presenterm

Create and deliver terminal-based presentations from markdown files with themes, code execution, mermaid/d2 diagrams, LaTeX/typst formulas, and PDF/HTML export capabilities.

lanej

tools

data-analysis

38

atlas14-spatial-variance

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

gpt-cmdr

data-ai

data-engineering

38

ebfe-validator

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.

gpt-cmdr

data-ai

data-engineering

38

analyzing-aorc-precipitation

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.

gpt-cmdr

data-ai

llm-ai

38

ideation

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}/.

nicknisi

data-ai

llm-ai

38

requirement-detector

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。

wordflowlab

data-ai

llm-ai

38

natural-language-processing

Build NLP applications using transformers library, BERT, GPT, text classification, named entity recognition, and sentiment analysis

aj-geddes

data-ai

llm-ai

38

ralph

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.

tkersey

data-ai

academic

38

consultant

Multi-model AI consultation and research. Supports CONSULTATION MODE (ask single expert), RESEARCH MODE (parallel multi-agent), or DELIBERATION MODE (agents debate and refine). USE WHEN user says 'ask gemini', 'consult codex' (consultation), 'do research' (research), or 'deliberate on' (deliberation).

nicknisi

research

machine-learning

38

recommendation-system

Build collaborative and content-based recommendation engines for product recommendations, personalization, and improving user engagement

aj-geddes

data-ai

machine-learning

38

dimensionality-reduction

Reduce feature dimensionality using PCA, t-SNE, and feature selection for feature reduction, visualization, and computational efficiency

aj-geddes

data-ai

machine-learning

38

classification-modeling

Build binary and multiclass classification models using logistic regression, decision trees, and ensemble methods for categorical prediction and classification

aj-geddes

data-ai

machine-learning

38

time-series-analysis

Analyze temporal data patterns including trends, seasonality, autocorrelation, and forecasting for time series decomposition, trend analysis, and forecasting models

aj-geddes

data-ai

machine-learning

38

computer-vision

Implement computer vision tasks including image classification, object detection, segmentation, and pose estimation using PyTorch and TensorFlow

aj-geddes

data-ai

machine-learning

38

neural-network-design

Design and architect neural networks with various architectures including CNNs, RNNs, Transformers, and attention mechanisms using PyTorch and TensorFlow

aj-geddes

data-ai

machine-learning

38

causal-inference

Determine cause-and-effect relationships using propensity scoring, instrumental variables, and causal graphs for policy evaluation and treatment effects

aj-geddes

data-ai

machine-learning

38

ml-model-explanation

Interpret machine learning models using SHAP, LIME, feature importance, partial dependence, and attention visualization for explainability

aj-geddes

data-ai

machine-learning

38

regression-modeling

Build predictive models using linear regression, polynomial regression, and regularized regression for continuous prediction, trend forecasting, and relationship quantification

aj-geddes

data-ai

machine-learning

38

anomaly-detection

Identify unusual patterns, outliers, and anomalies in data using statistical methods, isolation forests, and autoencoders for fraud detection and quality monitoring

aj-geddes

data-ai

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