菜单
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.
默认使用会先检查来源的 Prompt；你也可以切换为直接命令，或下载本地副本。
决定是否安装前，请先阅读 SKILL.md，以及 SkillsMP 当前展示的配套文件。
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt；运行前请先检查来源。
npx skills add https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills --skill kaggle-research命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
正在显示 SKILL.md
基于 SOC 职业分类
| name | kaggle-research |
| description | 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. |
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.
Route empirical-research requests through the Auto-Empirical Research Skills catalog when this whole repository is installed as one skill in Codex, CodeBuddy, Claude Code, or another IDE. Use to choose and load the right vendored AERS skill for causal inference, econometrics, replication, data acquisition, manuscript writing, peer review and referee responses, citation checking, de-AIGC editing, or full empirical-paper workflows without reading the entire repository at once.
中英双语学术降 AIGC / bilingual academic de-AIGC skill. Removes AI-generated writing signatures from empirical papers in economics, management, and the social sciences — in both English and Chinese. Covers Turnitin AI, GPTZero, Originality.ai on the English side and 知网 AMLC, 万方, 维普 on the Chinese side. Uses a six-step loop (intake → audit → claim-evidence check → differentiated rewrite → five-dimension self-score → cold-reader recheck) with two pattern libraries (22 English + 17 Chinese patterns), section-by-section strategies for empirical papers, and hard protections that keep every number, coefficient, and citation intact.
Recommend AND run open-source AI tools, agents, Claude Code / Codex skills, and MCP servers for any stage of a literature review — searching, reading, extracting, synthesizing, screening, citation-checking, and paper writing. Use when the user asks "what tool should I use to..." OR "install/run/use <tool> to ..." for research/lit-review work: automating a survey or related-work section, PDF→Markdown extraction for LLMs (MinerU/marker/docling), PRISMA / systematic review (ASReview), citation-backed Q&A over PDFs (PaperQA2), wiring papers into Claude/Cursor via MCP (arxiv/paper-search/zotero servers), or chatting with a Zotero library. Ships a launcher (scripts/litrun.py) that installs each tool in an isolated venv and runs it. Curated catalog of 70+ vetted projects. 支持中英文（用于「文献综述工具选型」与「一键安装/运行」）。