AI & ML

Runs time series machine learning tasks—classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search—using the scikit-learn compatible aeon toolkit.

Build, fit, validate, and compare Bayesian models using PyMC, including hierarchical models, MCMC sampling, variational inference, posterior predictive checks, and model comparison.

Runs an autonomous optimization loop that iteratively improves an artifact against an objective and evaluator using Hypothesis Tree Refinement, with subagent executors in isolated git worktrees.

Solves single- and multi-objective optimization problems with NSGA-II/III, MOEA/D, and other evolutionary algorithms, including constraint handling, Pareto front analysis, and benchmark problems.

Access AI-ready drug discovery datasets, benchmarks, and molecular oracles from Therapeutics Data Commons for therapeutic machine learning and pharmacological prediction.

Run cloud-native molecular modeling and drug-design workflows via a Python API, covering pKa prediction, docking, conformer and tautomer ensembles, molecular dynamics, and related small-molecule or protein tasks without local HPC infrastructure.

Provides guidance for using SymPy for exact symbolic mathematics in Python, covering algebra, calculus, equation solving, linear algebra, physics, and code generation.

Fetches any X/Twitter post as clean, structured JSON via the ADHX API, including full article content, author info, and engagement metrics, without scraping or a browser.

Trains machine learning models on genomic interval data from BED files, including region embeddings, single-cell ATAC-seq analysis, and consensus peak building.

Numerical computing with MATLAB and GNU Octave for matrix operations, data analysis, visualization, and scientific computing, including script execution and syntax guidance.

Loads and processes whole-slide pathology images, builds spatial graphs, trains deep learning models, and analyzes multiplexed immunofluorescence data across 160+ slide formats.

Build, optimize, and execute quantum circuits on IBM Quantum hardware or local simulators using Qiskit, including transpilation, primitives, and algorithm libraries.

Runs standard single-cell RNA-seq analysis with Scanpy, covering QC, normalization, dimensionality reduction, clustering, marker identification, visualization, and conversion of R single-cell formats to h5ad.

Analyze RNA velocity in single-cell RNA-seq data with scVelo, estimating cell state transitions from unspliced/spliced mRNA dynamics, inferring trajectory directions, computing latent time, and identifying driver genes.

Orchestrates AI/ML workflows for building LLM applications, RAG systems, AI agents, and ML pipelines, covering design, integration, observability, and security.

Reference for building real-time voice and multimodal AI applications with Pipecat, covering pipelines, speech services, LLM integration, and transports.

Manages annotated data matrices for single-cell genomics, covering creation, I/O, concatenation, and manipulation of AnnData objects in h5ad and zarr formats.

Provides guidance on using the Astropy library for astronomy and astrophysics workflows, including units, coordinates, FITS I/O, tables, time, WCS, and cosmology.

Performs constraint-based metabolic modeling with COBRApy: FBA, FVA, gene knockouts, flux sampling, and SBML model handling for systems biology and metabolic engineering.

Process and analyze mass spectrometry data with the Matchms Python library, including importing spectra, filtering peaks, calculating similarity scores, and building reproducible analytical workflows.

Filters and prioritizes compound libraries in drug discovery using drug-likeness rules, structural alerts, complexity metrics, and a query language.

Create publication-quality statistical graphics in Python with seaborn, covering relational, distribution, and categorical plots with pandas integration.

Designs collaborative AI agent teams with CrewAI, covering agent roles, task decomposition, crew orchestration, process types, memory, and flows.

Generates images from text prompts using Google Gemini's image generation model, saving them as PNG files for use in UI, documentation, and design assets.

Automates translating and syncing App Store metadata (description, keywords, what's new, subtitle) to multiple locales using LLM translation and the asc CLI.

Runs regression evaluations comparing agent edits against human-approved golden projects, and registers new goldens after human approval.

Identifies recurring patterns across three or more domains to extract universal principles and apply them to new problems.

Entrena modelos de machine learning con Scikit-learn, LightGBM y XGBoost, desde un baseline hasta un modelo productivo con validación robusta y explicabilidad.

Guides data analysis, EDA, and ML tasks by teaching, diagnosing MCPs, and deciding with the user, offering multiple options and documenting decisions.

Profiles datasets automatically to assess data quality, structure, and completeness, generating reports with ydata-profiling, pandera, or manual pandas methods.

Orquesta el ecosistema de agentes: decide qué modelo o tier usar, delega tareas a sub-agentes especializados y planifica arquitectura técnica.

Analiza series temporales: tendencia, estacionalidad y pronóstico con Prophet, statsmodels y ML, incluyendo descomposición, tests de estacionariedad y evaluación contra baselines.

Provides detailed, structured image descriptions covering objects, people, colors, text, and scene context, with an overview and interpretation.

Generates optimized prompts for AI video and image generation across multiple platforms, using cinematic styles and behavioral frameworks for commercial outcomes.

Design, simulate, and run quantum circuits on simulators and real quantum hardware using Google's Cirq framework.

Reference for building real-time voice and multimodal AI agents with Pipecat, covering pipelines, speech services, LLMs, transports, and deployment.