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End-to-end data preprocessing pipeline to transform raw files (CSV, Excel, Parquet) into AI-Ready assets. Use this skill when the user requests to clean data, handle missing values, fix data types, remove duplicates, standardize formats, or prepare datasets for model training. Includes automatic profiling and dataset card generation.
Convert structured data (CSV/Parquet/Excel) into semantic, readable text documents or experimental reports for LLM fine-tuning. Use when the user wants to transform tabular data into natural language narratives, generate training corpus, or create AI-Ready text documents from datasets. Output is Markdown format ready for further processing.
Interact with GitHub using the `gh` CLI. Use `gh issue`, `gh pr`, `gh run`, and `gh api` for issues, PRs, CI runs, and advanced queries.
Generate images for existing SFT datasets (JSON/JSONL) to build multimodal fine-tuning data; use when you need to add image prompts, descriptions, and saved image files for each QA pair or conversation, via SubAgents + image_generate tool.
Parse specialized scientific data formats (e.g., HDF5, NetCDF, FITS, mzML, CIF, PDB) or unfamiliar raw data files. Use when the user wants to process complex scientific files into AI-Ready formats.
Transform AI-Ready documents (Markdown) into LLM fine-tuning datasets using Easy Dataset. Use when the user wants to generate SFT training data from existing documents, create QA pairs from text, or prepare datasets for model fine-tuning. Requires Easy Dataset service running.