Train or fine-tune language and vision models using TRL or Unsloth with Hugging Face Jobs infrastructure.
Statistical modeling: OLS/WLS/GLS, GLM (logit, probit, Poisson), time series (ARIMA, VAR), mixed effects, diagnostics.
SciPy scientific computing skill for numerical optimization, integration, and signal processing in physics
Track and visualize ML training experiments with Trackio, including metric logging, alerts and dashboard visualization.
Inspect LLM torch profiler traces at forward-pass, layer, and kernel level.
Meta-agent that routes bioinformatics requests to specialised sub-skills.
PyTorch model training skill with custom training loops, gradient management, and GPU optimization.
Analytic Hierarchy Process (AHP) calculation skill for pairwise comparison matrices, consistency checking, and weight derivation
Plotly interactive visualization. Express and Graph Objects: scatter, line, bar, heatmap, 3D, geographic charts; subplots; styling…
R-to-Python translation for data analysis.
Produce offspring genomes from parent pairs via meiotic recombination, mutation, and clinical evaluation
Shotgun metagenomics profiling — taxonomy, resistome, and functional pathways
Rigorous A/B test statistical analysis — significance calculations, sample ratio mismatch checks and test design validation.
Nanoparticle Tracking Analysis skill for single-particle size and concentration measurements
Performance guide covering index files, stats cache, and frequency cache accelerators for qsv
Queuing theory analysis skill for analytical evaluation of waiting line systems.
Small/Wide Angle X-ray Scattering skill for nanostructure size, shape, and organization analysis
Sensitivity analysis skill for identifying critical inputs and understanding model behavior under uncertainty
Configures Simulink simulations non-destructively using SimulationInput objects — parameter overrides without modifying the model, batch…
Agent skill for pagerank-analyzer - invoke with $agent-pagerank-analyzer
Combinatorial object generation and counting
Data Science & ML agent skills on mcprush, filed under one heading so the shelf can be read in one pass. The kinds are workflow, expertise, output format, voice & style and guardrail, and the one a skill belongs to, where its author declared it, is the fastest way to guess what it will do to your prompt.
What to look at before installing one is what it costs in context and what it takes away: a guardrail that refuses an action and a voice that rewrites a sentence are both skills, and only one of them will stop the agent doing something. Every skill here says what it costs in context, which kind it is where its author declared one, what it needs beside it to work and the licence it is published under, next to the publisher it came from and whether that publisher has been verified. The filters on the left narrow it further and the sort at the top decides what comes first.
A skill is not connected to anything: there is no endpoint to authorise and nothing routed through the gateway. Installing one drops a folder of instructions into the client’s own skill folder, and uninstalling it is deleting that folder. The exact line for each client is on the skill’s own page, because it differs by client rather than by skill.