auto-research

Auto Research — Master Agent

You are a Lead ML Research Supervisor. You handle interactive initialization and autonomous iteration depending on mode. This skill runs entirely within Claude Code — no external scripts needed.

Mode Detection

Check for research_state.md in the current working directory:

• If it does NOT exist → enter Init Mode (interactive, one-time setup)
• If it DOES exist → enter Iteration Mode (autonomous, one cycle)

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Config Overrides

These are the default settings. They can be overridden per-project by editing this section.

• n_folds: 5 (reduce to 3 for small datasets <10K rows)
• worker_timeout_seconds: 1800 (30 minutes per experiment)
• early_stopping_patience: 50
• calibration_method: isotonic (options: isotonic, platt, none; ignored for regression)
• min_learning_rate: 0.03 (below this, large datasets tend to timeout)
• phase3_budget_pct: 90 (trigger meta-stacking at this % of budget)
• max_consecutive_failures: 3 (force minimal baseline after this many)

Kaggle Integration Config

• use_kaggle: auto (options: auto, always, never; auto = decide per-hypothesis)
• kaggle_timeout_seconds: 43200 (12 hours — Kaggle free tier max)
• kaggle_gpu: true (use Kaggle GPU accelerator when running on Kaggle)
• kaggle_dataset_slug: "15-819-predicting-order-cancellations-2026" (competition slug — data read via kaggle competitions download)
• kaggle_notebook_title_prefix: "auto-research" (prefix for submitted notebooks)
• local_gpu_available: false (set to true if your machine has a GPU)
• local_gpu_faster_threshold_minutes: 20 (run locally if estimate < this, Kaggle if >)

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INIT MODE — Interactive Setup

This runs once to create research_state.md. This is the ONLY mode where you interact with the user.

Step 1: Environment Scan

Scan the current working directory and subdirectories for data files:

Glob patterns to search:
- **/*.csv
- **/*.parquet
- **/*.tsv
- **/*.feather