brycewang-stanford/Auto-Empirical-Research-Skills

Academic Paper Composer turns an optimized outline into a submission-ready manuscript through chapter-by-chapter writing with quality checks. Use it when you need systematic writing, cross-chapter consistency, and validation before submission.

Academic Proofreader performs an exhaustive, multi-pass proofread and copy-edit of an applied-microeconomics manuscript to top-economics-journal standards, checking prose, equations, table notes, footnotes, and citations for avoidable errors. Use it before submitting or circulating an empirical economics paper.

Auto-Empirical Research routes empirical-research requests to the right vendored skill from a catalog of 1,096 skills across 76 collections, covering causal inference, econometrics, data acquisition, writing, and peer review.

Avoid AI Writing audits and rewrites content to remove AI writing patterns (AI-isms), such as em dashes, hollow intensifiers, and overused vocabulary. Use it when asked to detect AI tells or make text sound less machine-generated.

Causal Inference Mixtape provides code templates for 10 causal inference identification strategies in Python, R, and Stata. Use it to implement DiD, event studies, IV, RDD, synthetic control, matching, and robustness checks.

Check Citations verifies academic citations against CrossRef, Semantic Scholar, and OpenAlex to detect AI-hallucinated references, chimeric citations, and suspicious patterns. Use it before submitting papers or when auditing bibliographies.

De-AIGC Academic Rewriting removes AI-generated writing signatures from empirical papers in economics, management, and the social sciences, in both English and Chinese. Use it to lower AIGC scores before journal submission or AI detector checks, while preserving every number, coefficient, and citation.

Deslop removes AI writing patterns from prose so text sounds like a specific human wrote it. Use it when drafting, editing, reviewing, or revising any substantial prose—including scientific writing—to eliminate predictable AI tells, slop, and formulaic patterns.

Empirical Research Navigator routes agents to the correct empirical research skill by goal, method, and language across a library of 1,052 skills. Use it before starting an empirical pipeline, paper, or replication package to select Python, Stata, or R workflows and linked AER skills.

Full Empirical Analysis provides a complete end-to-end workflow for applied empirical research in Python, covering data cleaning, variable construction, descriptive statistics, econometric modeling, robustness checks, and publication-ready tables and figures. It also includes specialized modes for target-trial emulation and ML causal inference, with explicit library choices and diagnostic steps.

Full Empirical Analysis R runs a complete 8-step empirical research pipeline in R using tidyverse and econometrics packages, generating publication-ready regression tables, descriptive statistics, robustness checks, and figures for applied economics, epidemiology, and ML causal inference.

Game Theory Paper Writer generates, revises, and stress-tests game theory research papers, covering topic selection, economic modeling, equilibrium analysis, numerical simulation, and literature-grounded adaptation. Use it when a user provides a game theory topic, draft, model idea, anchor paper, or reviewer comments and needs a rigorous manuscript or revision memo.

Humanize Chinese detects and rewrites AI-generated Chinese text, using 20+ rule categories, 8 statistical features, and scene-aware logistic regression fusion for general, academic, and longform text. It offers 8 style transforms, best-of-N humanization, and academic AIGC reduction for CNKI/VIP/Wanfang.

Humanizer Academic edits academic medical manuscripts to detect and rewrite AI-generated writing patterns, such as inflated significance claims, vague attributions, and promotional language, so they read as natural professional prose. Use it when reviewing or revising medical papers for submission.

LaTeX Document creates, compiles, and converts LaTeX documents to professional PDFs with PNG previews. Use it for resumes, reports, theses, slides, posters, cheat sheets, mail merge, PDF conversion, forms, and more.

Literature Review guides users through writing high-quality academic literature reviews, from topic selection and literature search to critical evaluation, structuring, and final writing. Use it when writing a standalone literature review paper or the literature review section of a research paper.

Marginaleffects helps users interpret statistical models in R and Python via predictions, comparisons, slopes, and hypothesis tests, following the book Model to Meaning. Use it for marginal effects, treatment effects, causal inference, or function syntax.

PyFixest Reference provides a machine-readable API reference for the PyFixest Python package, covering feols, fepois, feglm, quantreg, clustered/robust standard errors, R-style formulas, and post-estimation. Use when writing or debugging fixed-effects regressions in Python.

Python Economic Computing provides best practices for macroeconomic modeling (DSGE/HANK), causal inference, and quantitative data analysis in Python. Use it when writing Python code for DSGE models, HANK models, numerical economic computation, causal inference, or quantitative economic data analysis.

SSCI Polish polishes English academic papers for SSCI journal submission, checking grammar, improving readability, and enhancing academic tone. Use it when asked to polish, proofread, edit, or improve an academic manuscript targeting SSCI, SCI, or other international journals.

Stata Accounting Research provides tested STATA code patterns from 126 peer-reviewed Journal of Accounting Research replication files (2017–2025) for empirical archival accounting methods such as entropy balancing, PSM, DiD, RDD, IV, event studies, and Fama-MacBeth regressions. Use it when a user asks procedural questions like 'How do I implement [method]?' or 'Show me code for [technique]'.

Stata Empirical Analysis provides a complete 8-step pipeline for econometric and causal inference in Stata, covering data cleaning, variable construction, diagnostics, estimation (DID, IV, RD, matching), robustness checks, and publication-ready tables and figures. Use it when you need reproducible .do-file workflows for academic empirical papers.

StatsPAI runs full empirical and causal analysis pipelines in Python, including AER-style DID/RD/IV/SCM/DML tables, epidemiological target-trial emulation, ML causal inference, and decomposition methods. Use it when the user asks for applied micro, public health, or causal ML results with paper-ready Word/Excel/LaTeX outputs.

Stop Slop removes predictable AI writing patterns from prose. Use it when drafting, editing, or reviewing text to eliminate filler phrases, formulaic structures, passive voice, and vague language.

Systematic Literature Review guides users through writing a systematic literature review following the PRISMA 2020 framework, producing a journal-format Word document, an annotated PRISMA flow diagram, and APA 7th edition references. Use it when drafting or auditing an SLR.