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kaggle-research

Use when a research task needs reproducible Kaggle discovery, metadata inspection, bounded public-data downloads, competition or kernel discovery, model discovery, or an explicitly approved Kaggle write/delete operation through the official CLI.

Published by brycewang-stanford ↗brycewang-stanford/Auto-Empirical-Research-Skills

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kaggle-research

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Use kaggle-research to discover and inspect [Kaggle dataset, competition, notebook, or model], returning reproducible read-only findings and preserved audit artifacts; do not download or mutate remote resources.

Required inputs

[Kaggle dataset, competition, notebook, or model]

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SKILL.md

name: kaggle-researchdescription: Use when a research task needs reproducible Kaggle discovery, metadata inspection, bounded public-data downloads, competition or kernel discovery, model discovery, or an explicitly approved Kaggle write/delete operation through the official CLI.

Kaggle Research

Use the official Kaggle CLI through the policy-enforcing wrapper in this skill. The wrapper records bounded, redacted audit data; confines downloads to an approved directory; and blocks remote mutation unless the user explicitly authorizes it.

Required workflow

Confirm the requested Kaggle resource and whether the action is read, download, write, or delete. Do not broaden user authority.
Use an isolated Python 3.11+ environment with kaggle>=2.2,<3.
Configure Kaggle authentication outside commands and source files. Never read, print, echo, log, or commit credential values.

Run the non-mutating prerequisite check:

python scripts/kaggle_research.py doctor --json

Run discovery commands before downloads or mutations. Keep every download under an explicitly chosen output root.
Preserve generated audit JSON and artifact hashes with the research outputs.
Report exact commands, resource references, timestamps, failures, and generated artifacts. Distinguish verified observations from assumptions.

Safe command execution

Pass Kaggle arguments after -- so their order is preserved:

python scripts/kaggle_research.py run --audit artifacts/audit.json -- datasets list -s iris -v
python scripts/kaggle_research.py run --output-root artifacts/kaggle -- datasets download -d owner/dataset

Preview any potentially mutating command first:

python scripts/kaggle_research.py run --dry-run --allow-write -- datasets create -p dataset-package

An actual remote write additionally requires explicit user authorization and --allow-write. A delete additionally requires --allow-delete and --confirm-resource matching the exact resource classified by the wrapper. The runtime never retries writes or deletes.

Real read-only verification

The live smoke workflow calls Kaggle's real service, inspects all supported resource groups, downloads a small public dataset, and verifies its hash:

python scripts/kaggle_research.py smoke-readonly --output-root artifacts/kaggle-smoke --report artifacts/kaggle-smoke-report.json

The corresponding integration test is opt-in so normal unit tests do not depend on network access:

AERS_KAGGLE_LIVE=1 python -m unittest discover -s tests -p "test_live_readonly.py" -v

Only run the live lane when credentials are already available in the process environment. It must remain read/download-only.

Reference routing

Authentication and credential boundaries: references/authentication.md
Dataset discovery and bounded downloads: references/datasets.md
Competition discovery and approved submissions: references/competitions.md
Kernel/notebook discovery and approved pushes: references/kernels.md
Model discovery and approved instance operations: references/models.md
Test lanes, safety policy, and audit artifacts: references/testing-and-safety.md

Read only the reference page required for the active task.