Search Kaggle datasets, competitions, notebooks, and models through its official command-line tool. Download public data into a chosen directory and preserve audit records and file hashes, identifiers used to check downloaded files.
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.
Run score Not tested
See how to installReview the instructions the agent will receive before you install.
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.
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.
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.
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.
Read only the reference page required for the active task.
No reviews yet. Tell others what you thought.
No login needed. Share how it worked for you, ask a question, or note where it did not run.
No comments yet. Be the first to write one.
Turn a broad research idea into clear, testable questions. Compare familiar and less conventional approaches, structure the selected question around its population and methods, and assess variables, feasibility, and potential theoretical contributions.
Helps build a research framework by comparing theories, mapping relationships between variables, and deriving testable hypotheses. Explores less conventional options, examines weaknesses and competing explanations, and produces diagrams with anticipated reviewer objections.
Clarify the philosophical assumptions behind a research project and check whether they fit the proposed methods. Get a reasoned research stance, draft proposal wording, and guidance for explaining how the researcher's background and values shape the study.
Plan and draft academic papers through specialized agent roles covering literature, arguments, citations, and review. Get chapter guidance, revision roadmaps, Traditional Chinese and English abstracts, and documents formatted as LaTeX, DOCX, PDF, or Markdown.
Turn a detailed outline into a philosophy or interdisciplinary manuscript. Draft chapters with repeated quality reviews, check terminology and arguments across sections, and prepare the manuscript, evaluation reports, abstract, and metadata for submission.
Simulate academic peer review through five reviewer roles tailored to the paper's field. Receive separate critiques, an editorial decision, and a prioritized revision roadmap, or check whether a revised manuscript addresses earlier comments.