crazymsn/academic-skills

Deep Cloud Creation Technology, focused on developing AI agents and AI applications to assist with research, work, and learning!

22Stars on the repository
139Mods indexed here, across every type
3mo agoLast push, which is what freshness is scored on
MITLicence, which decides whether bodies are shown

shap

121

crazymsn/academic-skills

Skill Claude CodeCodex

Model interpretability and explainability using SHAP (SHapley Additive exPlanations). Use this skill when explaining machine learning model predictions, computing feature importance, generating SHAP plots (waterfall, beeswarm, bar, scatter, force, heatmap), debugging models, analyzing model bias or fairness, comparing…

not rated 22 3mo ago A 109 tokens copy · 100% MIT

simpy

122

crazymsn/academic-skills

Skill Claude CodeCodex

Process-based discrete-event simulation framework in Python. Use this skill when building simulations of systems with processes, queues, resources, and time-based events such as manufacturing systems, service operations, network traffic, logistics, or any system where entities interact with shared resources over time.

not rated 22 3mo ago A 56 tokens copy · 91% MIT

stable-baselines3

123

crazymsn/academic-skills

Skill Claude CodeCodex

Production-ready reinforcement learning algorithms (PPO, SAC, DQN, TD3, DDPG, A2C) with scikit-learn-like API. Use for standard RL experiments, quick prototyping, and well-documented algorithm implementations. Best for single-agent RL with Gymnasium environments. For high-performance parallel training, multi-agent…

not rated 22 3mo ago A 87 tokens copy · 81% MIT

statistical-analysis

124

crazymsn/academic-skills

Skill Claude CodeCodex

Guided statistical analysis with test selection and reporting. Use when you need help choosing appropriate tests for your data, assumption checking, power analysis, and APA-formatted results. Best for academic research reporting, test selection guidance. For implementing specific models programmatically use…

not rated 22 3mo ago A 58 tokens copy · 97% MIT

statsmodels

125

crazymsn/academic-skills

Skill Claude CodeCodex

Statistical models library for Python. Use when you need specific model classes (OLS, GLM, mixed models, ARIMA) with detailed diagnostics, residuals, and inference. Best for econometrics, time series, rigorous inference with coefficient tables. For guided statistical test selection with APA reporting use…

not rated 22 3mo ago A 65 tokens copy · 100% MIT

sympy

126

crazymsn/academic-skills

Skill Claude CodeCodex

Use this skill when working with symbolic mathematics in Python. This skill should be used for symbolic computation tasks including solving equations algebraically, performing calculus operations (derivatives, integrals, limits), manipulating algebraic expressions, working with matrices symbolically, physics…

not rated 22 3mo ago A 98 tokens copy · 98% MIT

tiledbvcf

127

crazymsn/academic-skills

Skill Claude CodeCodex

Efficient storage and retrieval of genomic variant data using TileDB. Scalable VCF/BCF ingestion, incremental sample addition, compressed storage, parallel queries, and export capabilities for population genomics.

not rated 22 3mo ago A 46 tokens copy · 98% MIT

timesfm-forecasting

128

crazymsn/academic-skills

Skill Claude Code

Zero-shot time series forecasting with Google's TimesFM foundation model. Use for any univariate time series (sales, sensors, energy, vitals, weather) without training a custom model. Supports CSV/DataFrame/array inputs with point forecasts and prediction intervals. Includes a preflight system checker script to verify…

not rated 22 3mo ago A 73 tokens copy · 100% MIT

torch-geometric

129

crazymsn/academic-skills

Skill Claude CodeCodex

Guide for building Graph Neural Networks with PyTorch Geometric (PyG). Use this skill whenever the user asks about graph neural networks, GNNs, node classification, link prediction, graph classification, message passing networks, heterogeneous graphs, neighbor sampling, or any task involving torchgeometric / PyG. Also…

not rated 22 3mo ago A 129 tokens original MIT

torchdrug

130

crazymsn/academic-skills

Skill Claude CodeCodex

PyTorch-native graph neural networks for molecules and proteins. Use when building custom GNN architectures for drug discovery, protein modeling, or knowledge graph reasoning. Best for custom model development, protein property prediction, retrosynthesis. For pre-trained models and diverse featurizers use deepchem…

not rated 22 3mo ago A 67 tokens copy · 95% MIT

transformers

131

crazymsn/academic-skills

Skill Claude CodeCodex

This skill should be used when working with pre-trained transformer models for natural language processing, computer vision, audio, or multimodal tasks. Use for text generation, classification, question answering, translation, summarization, image classification, object detection, speech recognition, and fine-tuning…

not rated 22 3mo ago A 63 tokens copy · 95% MIT

treatment-plans

132

crazymsn/academic-skills

Skill Claude Code

Generate concise (3-4 page), focused medical treatment plans in LaTeX/PDF format for all clinical specialties. Supports general medical treatment, rehabilitation therapy, mental health care, chronic disease management, perioperative care, and pain management. Includes SMART goal frameworks, evidence-based…

not rated 22 3mo ago B 86 tokens copy · 100% MIT

umap-learn

133

crazymsn/academic-skills

Skill Claude CodeCodex

UMAP dimensionality reduction. Fast nonlinear manifold learning for 2D/3D visualization, clustering preprocessing (HDBSCAN), supervised/parametric UMAP, for high-dimensional data.

not rated 22 3mo ago A 43 tokens copy · 98% MIT

usfiscaldata

134

crazymsn/academic-skills

Skill Claude CodeCodex

Query the U.S. Treasury Fiscal Data API for federal financial data including national debt, government spending, revenue, interest rates, exchange rates, and savings bonds. Access 54 datasets and 182 data tables with no API key required. Use when working with U.S. federal fiscal data, national debt tracking (Debt to…

not rated 22 3mo ago A 106 tokens copy · 83% MIT

vaex

135

crazymsn/academic-skills

Skill Claude CodeCodex

Use this skill for processing and analyzing large tabular datasets (billions of rows) that exceed available RAM. Vaex excels at out-of-core DataFrame operations, lazy evaluation, fast aggregations, efficient visualization of big data, and machine learning on large datasets. Apply when users need to work with large…

not rated 22 3mo ago A 100 tokens copy · 88% MIT

venue-templates

136

crazymsn/academic-skills

Skill Claude Code

Access comprehensive LaTeX templates, formatting requirements, and submission guidelines for major scientific publication venues (Nature, Science, PLOS, IEEE, ACM), academic conferences (NeurIPS, ICML, CVPR, CHI), research posters, and grant proposals (NSF, NIH, DOE, DARPA). This skill should be used when preparing…

not rated 22 3mo ago A 97 tokens copy · 97% MIT

what-if-oracle

137

crazymsn/academic-skills

Skill Claude Code

Run structured What-If scenario analysis with multi-branch possibility exploration. Use this skill when the user asks speculative questions like "what if...", "what would happen if...", "what are the possibilities", "explore scenarios", "scenario analysis", "possibility space", "what could go wrong", "best case /…

not rated 22 3mo ago A 123 tokens copy · 100% MIT

xlsx

138

crazymsn/academic-skills

Skill Claude CodeCodex

Use this skill any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or…

not rated 22 3mo ago A 201 tokens copy · 92% MIT

zarr-python

139

crazymsn/academic-skills

Skill Claude CodeCodex

Chunked N-D arrays for cloud storage. Compressed arrays, parallel I/O, S3/GCS integration, NumPy/Dask/Xarray compatible, for large-scale scientific computing pipelines.

not rated 22 3mo ago A 42 tokens copy · 94% MIT

At most 3 mods per repository are shown here, and a mod shipped inside a plugin is left to that plugin's page — the rest are on their repository pages: