Machine Learning Claude Skills
1 780 machine learning skills from 282 authors. They train and fine-tune models, build embeddings, run RAG and measure quality. Half of them fit into 2 252 tokens or less — that is what one costs your context window when the agent loads it. 424 ship runnable scripts rather than instructions alone. 10 of them cannot work without an MCP server, most often rube. We also found 397 copies of these same skills sitting in other people's repositories — counted once here, not 397 times.
1 780 unique 282 authors 5 updated this month 189 from vendors
Provides strategies for traversing and filtering hierarchical JSON datasets stored in flat files.
A skill for finding best string matches in datasets using fuzzy matching libraries like fuzzywuzzy or rapidfuzz.
Using Hugging Face TRL library for building custom trainers for Preference Optimization methods like DPO, SimPO, etc.
Implement the SimPO (Simple Preference Optimization) loss function for LLM alignment. Use this skill whenever implementing SimPO training objectives, reference-free reward optimization, or Bradley-Terry preference loss with target reward margin. Triggers on: SimPO, preference optimization loss, average log probability reward, gamma margin.
Work with MultiPolygon and MultiLineString geometries from real plate boundary datasets.
Proper initialization of SimPOTrainer with model loading and args setup
Advanced filtering and validation of travel datasets with strict cuisine matching, budget constraints, and data quality checks.
Excel statistical functions for datasets including MIN, MAX, MEDIAN, AVERAGE, QUARTILE, and SUMPRODUCT
Environment setup and testing patterns for SimPO preference optimization with PyTorch.
Explore and query travel dataset files (CSV/TXT) to find cities, restaurants, accommodations, attractions, and driving distances — with precise filtering patterns and data field notes.
Build a multi-city road-trip travel itinerary from dataset files with complete budget tracking, pet-friendly constraints, cuisine coverage, and correct JSON output format.
Efficient recursive exploration and content searching across large datasets.
Improved 13F dataset analysis functions (includes better handling of TSVs).
Robust PyTorch tensor operations for implementing loss functions, including handling device placement and numerical stability.
A refined skill to implement SimPO (Simple Preference Optimization) loss in PyTorch, precisely matching the official paper implementation without a reference model.
Load citizen science and expert annotation datasets, match them by image using file_rad column, handle missing data, and prepare data for clustering evaluation.
Filter the boundary dataset to include only boundaries relevant to the Pacific plate (where PlateA or PlateB equals the Pacific plate identifier). Use this skill to exclude irrelevant boundaries before distance calculations.
Examine the plates dataset to find the exact identifier used for the Pacific plate (e.g., "PA", "Pacific", etc.). Use this skill early in the analysis to ensure consistent filtering across boundaries and plates.
Load plate boundary GeoJSON and create a GeoDataFrame with proper geometry parsing. Verify column names (PlateA, PlateB) match the dataset structure. Use this skill to prepare boundary data before filtering for Pacific plate relevance.
Inspect the actual holdings dataset to understand its schema, field names, and data patterns before applying filters. Use this to identify the correct field name that distinguishes equity securities from bonds, options, warrants, and other non-stock instruments.
Search the COVERPAGE dataset using fuzzy matching to locate a specific fund by name and extract its accession_number for subsequent analysis. Use this when you need to identify a fund's filing by partial or approximate name matching.
Retrieve and load the complete holdings dataset for a specific fund using its accession_number. Use this to access position-level details needed for AUM, stock count, and comparative analysis.
Use when implementing the `simpo_loss` function in SimPOTrainer class. Extract loss computation logic from the paper and translate it to PyTorch code that accepts the expected tensor inputs.
Understanding the structure of SEC 13F filing datasets stored in /root/2025-q2 and /root/2025-q3 folders, including available scripts and data files
Specific knowledge of the PB2002 (Bird, 2002) dataset structure for identifying the Pacific plate.
Methods for calculating or retrieving the number of tokens consumed during an LLM inference task.
Load earthquake GeoJSON and plate boundary datasets, and ensure consistent projection for distance analysis.
Implements the SimPO loss function using stable log-probability operations in the specified trainer file.
Instructions for querying the provided datasets to ensure constraints regarding pet-friendly lodging, specific cuisines, and valid locations are met.
Extracts and tracks the consumed token usage from an LLM API response to monitor API cost and utilization.
Work with tectonic plate boundary datasets (PB2002 format) to identify plate regions, extract specific plate boundaries, and perform spatial filtering. Load plate boundary and plate polygon data, identify specific plates like the Pacific plate, and extract relevant boundaries. Use this skill when parsing plate tectonics datasets, identifying plate regions from boundary lines, or filtering spatial data by plate membership.
Set up Python environment for PyTorch-based NLP projects with transformers and alignment training. Use this skill when initializing project environments, managing dependencies from environment.yml files, installing required packages, and ensuring CUDA/device compatibility. Essential for reproducible machine learning research requiring specific package versions.
Run PyTorch unit tests, save results to NumPy files, and log environment information for reproducibility. Use this skill when executing test suites for neural network functions, validating loss computations, saving tensor outputs for verification, and creating reproducibility logs with Python/package versions.
Guidelines for implementing the SimPO (Simple Preference Optimization) loss function for NLP model training. Use this skill when modifying trainers or implementing preference-based loss functions in transformer projects.
Computing RMSE (Root Mean Squared Error) and other metrics for water temperature model validation. Use this skill whenever you need to match simulated temperatures with field observations, compute RMSE by depth categories, calculate annual/seasonal subsets, or prepare model evaluation metrics. Essential for lake model calibration and validation workflows.
Set up Python environments for NLP/ML projects using PyTorch, transformers, and TRL. Use this skill when installing dependencies for preference optimization, RLHF, or transformer-based training pipelines.
Procedures for performing precise distance calculations between points and geometries using GeoPandas. Use this skill when calculating distances in kilometers, reprojecting GeoDataFrames, or finding the nearest features in a geospatial dataset.
Techniques for processing tectonic plate data, specifically using the PB2002 dataset. Use this skill when filtering points within plates, identifying plate boundaries, or working with global tectonic geometries in GeoPandas.
How to optimize DBSCAN hyperparameters for Mars cloud clustering. Use this skill whenever performing grid search, DBSCAN clustering, or evaluating F1 and delta for Mars datasets.
> Handles loading and processing of earthquake and plate boundary data. Use this for reading, filtering, and joining geospatial datasets related to tectonics (plates, boundaries, earthquake points).
Procedures for setting up the environment for research projects involving Python, PyTorch, and NLP models. Use whenever environment requirements (environment.yml) are present.
How to calculate and save GLM performance metrics (RMSE) to metrics.json. Use this whenever the user asks for RMSE checks or final model evaluation.
How to implement custom loss functions in PyTorch. Use this skill whenever the user asks to implement a loss function, write a custom criterion, or mentions PyTorch tensor operations for backpropagation.
invoke this skill when you need to perform database search for travel planning. This skill provides some useful pre-packaged tools to look up accommodations, attractions, cities, driving distance, flights, and restaurants from the bundled dataset.
Automates the Karpathy LLM Wiki workflow: turns web, GitHub, and YouTube URLs into well-structured, citable, wikilinked pages with automatic linting and sourcing — invoke with /pin-llm-wiki
Applies fixes from a prior review-llm-artifacts run, with safe/risky classification. Respects verify-llm-artifacts output when present to skip false positives.
Detects common LLM coding agent artifacts in codebases. Identifies test quality issues, dead code, over-abstraction, and verbose LLM style patterns. Use when cleaning up AI-generated code or reviewing for agent-introduced cruft.
Confirms or rejects findings from review-llm-artifacts before deletes or risky refactors. Loads review-verification-protocol-style checks per finding. Use after a review run, when the user wants to reduce false positives, before fix-llm-artifacts on dead code, or when validating a full-project scan.