Data Skills
A curated collection of 8 data skills for building with AI coding agents.
Chunking Strategy Optimizer
Find the chunking strategy and size that maximizes retrieval quality for a specific corpus, by sweeping configurations against a fixed eval set instead of guessing. Use when RAG answers miss obvious content, when standing up a new corpus, or when picking chunk size/overlap.
Embedding Set Inspector
Diagnose the health of an embedding set before blaming the retriever — checking normalization, dimensionality, near-duplicates, degenerate vectors, and corpus/query distribution mismatch. Use when retrieval quality is poor, after a re-embed, or before shipping a new index.
Finetune Dataset Builder
Turn raw examples into a training-ready fine-tuning dataset — normalize to the trainer's chat/instruction format, deduplicate (including near-duplicates), strip PII, balance, validate the schema and token lengths, and carve a leak-free eval split. Use when you have raw examples and need a clean, formatted, split dataset before training.
LLM As Judge Scorer
Design a reliable LLM-as-judge metric — a calibrated rubric, a clear scoring scale, and bias controls — and validate it against human labels before trusting it. Use when grading open-ended LLM output (summaries, answers, tone) that exact-match can't score.
LLM Eval Suite Scaffolder
Stand up an evaluation suite for an LLM feature from scratch — a representative dataset, the right metrics, a baseline score, and a CI gate — using DeepEval, promptfoo, or RAGAS. Use when a feature has no evals, before tuning a prompt, or when adding an LLM feature to CI.
Multimodal Document Extractor
Extract structured data from documents and images with a vision-language model — define the target schema, prompt the VLM to fill it from the page (invoices, forms, receipts, statements, IDs), and verify critical fields against the source. Use when you need reliable structured output from messy, varied, or scanned documents that defeat template-based OCR.
Qlora Finetune Runner
Run a QLoRA (4-bit LoRA) fine-tune of an open-weight model from a prepared dataset — set up the config, train memory-efficiently (e.g. with Unsloth/PEFT), watch for overfitting, save the adapter, and run a quick eval against the prepared split. Use when you have a clean dataset and want to execute a parameter-efficient fine-tune on a single GPU.
SQL Optimizer
Diagnose a slow SQL query from its execution plan and propose a verified optimization — finding the real bottleneck (sequential scan, missing or unused index, bad join order, app-side N+1) and measuring the fix before and after. Use when a query is slow and you need a fix backed by EXPLAIN ANALYZE, not a guess.