skills/60-regisely-superpapers/skills/academic-baseline/SKILL.md
Use when working on any empirical academic research context — paper writing, data analysis, literature review, or any task involving citations, results, or publication artifacts. Establishes non-negotiable principles that govern all other superpapers skills.
npx skillsauth add brycewang-stanford/Awesome-Agent-Skills-for-Empirical-Research academic-baselineInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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This skill is the constitution of the superpapers plugin. It sets inviolable rules that every other skill must respect. Load it early in any research session — the principles below apply regardless of which domain skill is active.
The nine principles below are non-negotiable. Every other superpapers skill operates under them.
Never fabricate citations. Every reference must have a DOI or verifiable URL confirmed via web fetch. Citing from memory is forbidden. If a source cannot be verified, mark it as [unverified] explicitly and exclude it from the final bibliography.
Replication is mandatory. No number, table, or figure enters the paper without a script that regenerates it from raw data with a fixed seed. Manual copying of results into the paper is forbidden. See replication-driven-research for the full discipline.
LaTeX is the default output format. Tables use booktabs and threeparttable. Figures are vector PDFs. Papers are .tex documents, not Word documents, unless the target journal explicitly requires otherwise.
Distinguish causal from correlational claims. Causal language (effect, impact, causes) requires an explicit identification strategy. When in doubt, use correlational language (associated with, correlated with, related to). Wrong framing is a substantive error, not a stylistic one.
YAGNI applies to robustness. Include canonical robustness checks for the chosen design. Do not pile on thirty tests to impress referees — noise is not evidence. See robustness-checks for the selection criterion.
Exploratory is not confirmatory. If a result was found by exploring the data, declare it as exploratory. Do not retroactively frame it as a prior hypothesis. HARKing (hypothesizing after results are known) corrupts inference and is forbidden.
Numerical integrity is non-negotiable. Fix the random seed in every script that uses randomness. Document package versions in a lockfile. Never hardcode intermediate results in the paper. Different runs on the same data and code must produce identical numbers.
Respect the user's paper language. Plugin internals and code are English-only. Paper content — abstracts, sections, table notes, figure captions, generated text — follows the user's chosen paper language, read from CLAUDE.superpapers.md when present or obtained from explicit user instruction otherwise. Never mix plugin language into user output.
Write clean, flowing prose. Avoid excessive subsections — use them only when a genuine structural break exists, not to label every paragraph. Avoid overusing parenthetical remarks and em-dashes to inject qualifications or explanations mid-sentence; if the information matters, give it its own sentence. Parentheses and dashes should be rare punctuation, not a writing habit. Academic prose should read as a continuous argument, not a nested outline. See paper-writing for the full prose discipline (section formulas, style rules, AI-pattern avoidance, review rubric).
CLAUDE.superpapers.md by attempting to Read it from the current working directory. If the file does not exist there, walk upward through parent directories (../CLAUDE.superpapers.md, ../../CLAUDE.superpapers.md, and so on) until found or until the filesystem root is reached. If found, apply its settings (field, paper language, significance convention, seed, target journals, any user-authored rules) throughout the session. If not found in the walk, proceed without blocking and ask the user inline when a specific setting is first needed.CLAUDE.superpapers.md was resolved via walk-up Read; its settings were applied, or its absence was explicitly handledtools
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」)。
development
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.
documentation
Use when the project collects primary data or runs a field, lab, or survey experiment, before the intervention begins — write the pre-analysis plan, size the sample from a power calculation, and register with the AEA RCT Registry. Apply after the design is chosen in aer-identification and before any outcome data are seen.
tools
Guide economists to authoritative data sources with explicit, confirmed data specifications before retrieval; interfaces with Playwright MCP to navigate portals and extract real data, not articles about data.