Data Science & ML Agent Skills

Quantum Physics Simulation (QuTiP)

Simulates and analyzes quantum mechanical systems using the Quantum Toolbox in Python (QuTiP).

AgentDB Advanced Features

Enables high-performance distributed vector search and multi-agent coordination using QUIC synchronization and hybrid search.

AgentDB Reinforcement Learning Plugins

Empowers AI agents to learn and improve through experience using 9 specialized reinforcement learning algorithms and WASM-accelerated inference.

Scikit-Bio

Analyzes biological data including sequences, phylogenetic trees, and microbial diversity metrics using specialized Python tools.

Astropy Astronomy Toolkit

Provides comprehensive tools for astronomical data analysis, coordinate transformations, and cosmological calculations within Python environments.

Pysam Genomic Toolkit

Manipulates genomic datasets and processes Next-Generation Sequencing (NGS) files using a Pythonic interface to htslib.

Vercel AI SDK Assistant

Simplifies the development of AI-powered features and autonomous agents using the Vercel AI SDK.

AI Prompt Optimizer

Refines and compresses LLM prompts to minimize token usage and maximize response quality.

UMAP-Learn Dimensionality Reduction

Performs high-performance nonlinear dimensionality reduction for data visualization, clustering preprocessing, and supervised manifold learning.

Prompt Engineering Expert

Provides expert guidance and implementation patterns for crafting highly effective LLM prompts using advanced techniques like chain-of-thought and few-shot learning.

DiffDock Molecular Docking

Predicts high-accuracy 3D protein-ligand binding poses using diffusion-based deep learning for structure-based drug design.

Counter Frequency Patterns

Implements efficient counting and frequency analysis patterns using Python's collections.Counter for data processing and distribution analysis.

Side-by-Side Comparison Patterns

Visualizes code changes, algorithm results, and data states by displaying multiple outputs in parallel columns.

Priority Queue Implementation Patterns

Implements high-performance priority queues for pathfinding, scheduling, and stream processing using efficient heap-based structures.

Python Data Grouping Patterns

Optimizes Python data structures using defaultdict for efficient grouping, adjacency lists, and nested dictionary management.

Graceful Failure Coding Pattern

Implements the Norvig pattern of returning sentinel values instead of exceptions for natural algorithmic failures.

Stable Matching & Gale-Shapley

Implements the Gale-Shapley algorithm to solve stable matching problems for two-sided markets like residency and admissions.

State-Driven Class Patterns

Encapsulates complex state management into robust class structures to handle transitions, backtracking, and algorithmic branching.

Frontier-Based Graph Exploration

Implements flexible graph and tree traversal patterns with configurable DFS, BFS, and randomized exploration strategies.

Constraint Satisfaction Solver

Solves complex logic puzzles and scheduling problems using Peter Norvig's propagate-then-search algorithmic pattern.

Python Comprehension Chain Patterns

Implements elegant, idiomatic data transformations using Pythonic list, dictionary, and set comprehensions inspired by Peter Norvig.

Norvig Sparse Set Pattern

Implements memory-efficient sparse data structures using Python sets for infinite grids and large coordinate spaces.

Civitai Video Performance Analyst

Analyzes Civitai video engagement and content metrics through natural language SQL queries and automated reporting.

Iterate with Itertools

Implements memory-efficient combinatorial iteration patterns in Python using the itertools library.

Local LLM Management

Manages local Ollama LLM models for development, testing, and VRAM optimization within Claude Code workflows.

Python NamedTuple Records

Implements immutable, memory-efficient data structures using Python's namedtuple for cleaner and more readable code.

Python Operator Overloading DSL

Enables the creation of expressive domain-specific languages in Python by overloading arithmetic and logical operators.

Python Memoization Patterns

Optimizes Python functions by implementing memoization and dynamic programming patterns to eliminate redundant recursive computations.

Local Search Optimizer

Solves NP-hard optimization problems using greedy construction and iterative local improvement patterns.

Symbolic Expression Tree Builder

Implements symbolic computation patterns and Abstract Syntax Trees for mathematical modeling and code transformation.