Kalshi exposes a public REST API and I need a clean, repeatable way to harvest every piece of market data it makes available—contract metadata, price history, volume, open interest, settlement info, everything the endpoint can return. The raw calls are straightforward, but turning them into tidy, analysis-ready tables is where I’d like your help. Here’s what I expect: • A well-commented Python script (requests / urllib3 and, ideally, pandas) that authenticates if needed, paginates through all active and historical markets, and writes the output to CSV. • The resulting CSVs organised logically—e.g. markets.csv, contracts.csv, daily_prices.csv—so I can drop them straight into my analytics pipeline. • A short README covering how to run the script, environment variables (API key, rate limits), and any dependencies. Acceptance criteria – Running `python fetch_kalshi.py` from a fresh clone reproduces the same data structure on my machine. – All fields returned by the API are captured; no truncation or silent drops. – Column names match Kalshi’s JSON keys unless renaming is clearly documented. Scheduling isn’t critical yet, but if you can flag where a cron or Lambda trigger would slot in, that’s a bonus. Let me know if you’ve worked with Kalshi or similar financial APIs before—examples or repos welcome.