Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add Pavel-Tk/auto-data-scientist --skill auto-researchgit clone --depth 1 https://github.com/Pavel-Tk/auto-data-scientistWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/pavel-tk/auto-data-scientist/auto-research)<a href="https://agentmods.dev/skills/pavel-tk/auto-data-scientist/auto-research"><img src="https://agentmods.dev/badge/skills/pavel-tk/auto-data-scientist/auto-research.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00072 | $0.05571 |
| Opus 5 | $0.00036 | $0.02786 |
| Sonnet 5 | $0.00014 | $0.01114 |
| Haiku 4.5 | $0.00007 | $0.00557 |
Grade A, and why
auto-research scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 512 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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)
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 >)
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
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 8d ago First seen · 512 lines · 72 tokens per session scan A d283dc9980c9
auto-research is a skill published in the GitHub repository Pavel-Tk/auto-data-scientist (6 stars, last pushed 5mo ago), licensed MIT. It adds 72 tokens to every session and 5,571 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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