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Opinionated data cleaning pipeline builder for tabular datasets. Trigger when the user asks to "clean this data", "fix this dataset", "handle missing values", "remove duplicates", "preprocess this data", "data wrangling", "normalize columns", "fix data types", "data quality", or provides messy data that needs cleaning before analysis or modeling. Also triggers on "prepare this data for modeling", "data preprocessing pipeline". Detects column types, handles missing values with strategy recommendations, normalizes strings, fixes date parsing, deduplicates rows, and outputs clean data plus a cleaning report.
Generate structured, stakeholder-ready data science reports from analysis results, notebooks, metrics, and charts. Trigger when the user asks to "write a report", "summarize my analysis", "create a findings document", "executive summary", "write up results", "document this analysis", "stakeholder report", "present findings", or has completed an analysis and needs to communicate results. Also triggers on "make this notebook into a report", "explain these results to non-technical audience", or "prepare a slide-ready summary".
Perform comprehensive Exploratory Data Analysis on any tabular dataset (CSV, Parquet, Excel, or database table). Trigger when the user asks to "explore this data", "run EDA", "analyze this dataset", "what's in this data", "profile this CSV", "show me distributions", "find patterns in this data", or provides a data file and wants to understand its contents. Also triggers on requests to check data quality, find outliers, or understand feature relationships. Generates a complete EDA notebook or script with visualizations, statistical summaries, and a written findings report.
Lightweight local experiment logging and comparison for ML projects -- no MLflow or W&B required. Trigger when the user asks to "track experiments", "log this run", "compare runs", "experiment log", "which model was best", "experiment history", "save this experiment", "leaderboard", or wants to organize ML experiments without setting up external tracking infrastructure. Also triggers on "what did I try before", "reproduce this run", or "experiment management".
Generate and engineer features from raw tabular data for machine learning. Trigger when the user asks to "engineer features", "create features", "feature engineering", "generate features", "transform features", "feature extraction", "build feature matrix", or mentions lag features, rolling averages, interaction terms, target encoding, cyclical encoding, text features, or date features. Also triggers on "improve model with better features", "what features should I create", or "feature ideas for this data". Supports numeric, categorical, datetime, and text feature engineering patterns.
End-to-end LLM fine-tuning workflow -- dataset preparation, LoRA/QLoRA configuration, training, and evaluation. Trigger when the user asks to "fine-tune a model", "fine-tune an LLM", "train a language model", "LoRA", "QLoRA", "adapt a model", "custom model training", "fine-tune BERT/Llama/Mistral/GPT", "prepare training data for fine-tuning", or wants to customize a foundation model for a specific task. Also triggers on "should I fine-tune or prompt engineer", "dataset format for fine-tuning", "how to fine-tune", "Hugging Face training".
Automated hyperparameter optimization for machine learning models using Optuna or scikit-learn search. Trigger when the user asks to "tune hyperparameters", "optimize model", "find best parameters", "grid search", "random search", "Bayesian optimization", "Optuna", "hyperparameter search", "tune this model", or wants to improve model performance by finding optimal hyperparameters. Also triggers on "what parameters should I use", "sensitivity analysis", or "parameter sweep".