brycewang-stanford

面向中文学术论文的降 AIGC 检测率 Skill。针对知网、万方、维普、Turnitin 中文版的检测机制，识别并消除中文大语言模型的 17 类结构化写作痕迹。采用"定位 → 诊断 → 改写 → 自评 → 复查"五步闭环工作流，分章节差异化策略（摘要/引言/文献综述/方法/结果/讨论/结论），保持学术严谨性前提下通过检测。

Detect and humanize AI-generated Chinese text. 20+ rule detection categories plus statistical features (sentence-length CV, short-sentence fraction, comma density, perplexity, GLTR, DivEye) plus scene-aware LR fusion (rule × 0.2 + LR × 0.8) trained on three scenes: general / academic / longform 长文本 (≥1500 字)。Unified CLI: ./humanize {detect,rewrite,academic,style,compare}. 8 style transforms (casual/zhihu/xiaohongshu/wechat/academic/literary/weibo/novel)。 Multi-paragraph rewriting (paragraph length CV、跨段 trigram 重复) plus best-of-N humanize (默认 N=10 取最低 LR)。165 replacement patterns + CiLin 同义词词林 38873 with collision blacklist。 Academic paper AIGC reduction for CNKI/VIP/Wanfang (知网/维普/万方 AIGC 检测降重)。 Pure Python, no dependencies, offline。v5.0.0 — HC3 fused 准确率 95%、学术 hero 100→35 (-65)、 工作汇报 96→13 (-83)、长篇博客 96→41 (-55)。 Use when user says: "去AI味", "降AIGC", "人性化文本", "humanize chinese", "AI检测", "AIGC降重", "去除AI痕迹", "文本改写", "论文降重", "知网检测", "维普检测", "AI写作检测", "让文字更自然", "detect AI text", "humanize text", "reduce AIGC sc

A brief description of what this skill does

Classical end-to-end empirical analysis workflow in the traditional Stata ecosystem — native Stata + reghdfe + ivreg2 + csdid + did_imputation + eventstudyinteract + sdid + rdrobust + rddensity + synth + synth_runner + psmatch2 + teffects + ebalance + coefplot + esttab + asdoc + binscatter. **Defaults to economics empirical-paper style** (AER / QJE / AEJ) — every run produces a publication-ready output set with a multi-column regression table (M1→M6 progressive controls/FE) as the centerpiece, plus Table 1 (descriptives), mechanism / heterogeneity / robustness tables, and event-study + coefficient + trend figures. Covers the full 8-step Stata pipeline an applied economist runs on every paper — (1) data import & cleaning (use/import, destring, misstable, duplicates, merge assert), (2) variable construction (gen/egen/winsor2/xtile/xtset with L./F./D.), (3) descriptive statistics & Table 1 (tabstat/balancetable/asdoc), (4) classical diagnostic tests (sktest/swilk/hettest/imtest/xtserial/xttest3/vif/dfuller/kpss/

Classical end-to-end empirical analysis workflow in the traditional Python econometric stack — pandas + numpy + scipy + statsmodels + linearmodels + pyfixest + rdrobust + econml + causalml + matplotlib/seaborn. **Defaults to economics empirical-paper style** (AER / QJE / AEJ) — every run produces a publication-ready output set with a multi-column regression table (M1→M6 progressive controls/FE) as the centerpiece, plus Table 1 (descriptives), mechanism / heterogeneity / robustness tables, and event-study + coefficient + trend figures. Covers the full 8-step pipeline an applied economist or quantitative social scientist runs on every paper — (1) data cleaning, (2) variable construction & transformation, (3) descriptive statistics & Table 1, (4) statistical diagnostic tests, (5) baseline empirical modeling, (6) robustness battery, (7) further analysis (mechanism, heterogeneity, mediation, moderation), (8) publication-ready tables & figures. **Also covers two parallel domain modes that share the same 8-step scaf

Classical end-to-end empirical analysis workflow in the modern tidyverse + econometrics R ecosystem — dplyr + tidyr + haven + fixest + sandwich + lmtest + clubSandwich + AER + ivreg + did + bacondecomp + HonestDiD + eventstudyr + rdrobust + rddensity + Synth + gsynth + synthdid + MatchIt + WeightIt + cobalt + ebal + grf + DoubleML + mediation + marginaleffects + modelsummary + kableExtra + gt + ggplot2 + ggpubr + cowplot + binsreg. **Defaults to economics empirical-paper style** (AER / QJE / AEJ) — every run produces a publication-ready output set with a multi-column regression table (M1→M6 progressive controls/FE) as the centerpiece, plus Table 1 (descriptives), mechanism / heterogeneity / robustness tables, and event-study + coefficient + trend figures. Covers the full 8-step R pipeline an applied economist runs on every paper — (1) data import & cleaning (read_dta/read_csv, naniar, janitor, validate-merges), (2) variable construction (mutate/across/winsorize/group_by + lag/lead with dplyr), (3) descriptive

Conduct comprehensive, systematic literature reviews using multiple academic databases (PubMed, arXiv, bioRxiv, Semantic Scholar, etc.). This skill should be used when conducting systematic literature reviews, meta-analyses, research synthesis, or comprehensive literature searches across biomedical, scientific, and technical domains. Creates professionally formatted markdown documents and PDFs with verified citations in multiple citation styles (APA, Nature, Vancouver, etc.).

Write publication-ready ML/AI/Systems papers for NeurIPS, ICML, ICLR, ACL, AAAI, COLM, OSDI, NSDI, ASPLOS, SOSP. Use when drafting papers from research repos, structuring arguments, verifying citations, or preparing camera-ready submissions. Includes LaTeX templates, reviewer guidelines, and citation verification workflows.

Use when assembling the data-and-code replication package required by 《管理世界》 after conditional acceptance, drafting the README in Chinese, or auditing a deposit before editorial-office submission. Implements 《管理世界》"数据可获得性政策" (effective 2023, updated 2025), with explicit handling for CSMAR / Wind / 国泰安 等受限商业数据库 and Chinese government micro-data.

Use when deciding which mw-* sub-skill to invoke next, or when sequencing manuscript work from topic selection through rebuttal for 《管理世界》 (Management World). Routes — does not replace — the specialized skills.

Use when the empirical identification strategy is the bottleneck for a Management-World manuscript — quasi-experimental designs (DID, IV, RDD, DML, event study) with Chinese policy shocks. Stress-tests the design before drafting tables.

Use when drafting or revising the institutional-background section of a Management-World manuscript, when the China policy timeline is unclear, or when reviewers ask for stronger institutional context.

Use when drafting or revising the literature-review section for a Management-World manuscript, when the Chinese / English reference balance is off, or when canonical Chinese references are missing.

Use when designing or writing mechanism tests and heterogeneity analyses for a Management-World empirical manuscript. Required for nearly all MW empirical submissions.

Use when drafting or strengthening the policy-implications section of a Management-World manuscript. Enforces layered (short / mid / long-term, micro / meso / macro) actionable recommendations.

Use when responding to reviewer reports for a Management-World R&R. Produces the point-by-point response letter and aligned manuscript edits.

Use when designing or writing heterogeneity analysis for an Economic-Research manuscript. Enforces five-dimension priority and theoretical-justification discipline.

Use when the empirical identification strategy is the bottleneck for an Economic-Research manuscript — quasi-experimental designs (DID, IV, RDD, DML, event study). Stress-tests the design before drafting tables.

Use when drafting or revising the literature-review section for an Economic-Research manuscript, when the Chinese / English reference balance is off, or when canonical theory references are missing.

Use when designing or writing mechanism analyses for an Economic-Research empirical manuscript. Mechanism tests are near-mandatory for empirical submissions.

Use when drafting or strengthening the policy-implications section of an Economic-Research manuscript. Frames implications at the significance / institutional level rather than the actionable / ministry level used by Management-World.

Use when responding to reviewer reports for an Economic-Research R&R. Produces the point-by-point response letter and aligned manuscript edits.

Use when running the final pre-submission preflight for 《经济研究》 — format, word count, double-blind, references, anti-plagiarism, author info, and supplementary files.

Use when finalizing regression tables and figures for an Economic-Research manuscript. Enforces three-line table style, footnote conventions, column-count discipline, and figure aesthetics.

Use when selecting, implementing, or stress-testing the causal identification strategy for an empirical economics manuscript — difference-in-differences (including staggered designs), instrumental variables (including weak-IV-robust inference), regression discontinuity, synthetic control, or shift-share / Bartik. Apply before writing the introduction or results.

Use when drafting or rewriting the introduction of an economics manuscript targeted at AER, AER:Insights, or an AEJ, or when compressing an abstract to the mandatory 100-word limit. Implements the Keith Head / Bellemare five-paragraph formula and AER-specific formatting conventions.

Use when responding to a Revise & Resubmit decision from AER, AER:Insights, or an AEJ, and a point-by-point response letter plus aligned manuscript revisions are needed. Handles triage, the concede / clarify / push-back decision, and the response-letter format that editors actually read.

Use when assembling the AEA Data and Code Availability deposit for an AER, AER:Insights, or AEJ acceptance, writing the README, or auditing a replication package before the AEA Data Editor review. Implements the current AEA policy, including the February 2026 Data and Code Availability Policy.

Use when constructing or revising regression tables, descriptive statistics tables, or figures for an AER, AER:Insights, or AEJ manuscript. Implements AER booktabs house style, the standard regression-table layout, and the figure-notes convention.

Use when the main empirical results exist but the manuscript lacks the robustness, heterogeneity, mechanism, and placebo checks that AER referees will demand. Apply after aer-identification and before aer-introduction so that the value-added paragraph can reference these tests.

Use when running the final pre-submission audit for an AER, AER:Insights, or AEJ manuscript — length, format, cover letter, per-author disclosure statements, file packaging, and routing among the AEA journal family. Apply immediately before clicking submit.

Use when evaluating whether a research idea clears the AER top-5 bar, when routing between AER, AER:Insights, and the AEJ family, or when sharpening a fuzzy contribution sentence into one publishable claim. Apply before any writing begins.

Use when the user asks to run a full empirical / causal analysis in Python — by default in the style of an applied economics paper (AER / QJE / JPE / ReStud / AEJ) with DID / RD / IV / SCM / DML / matching, written-out estimating equation + identifying assumption, Table 1 / Table 2 / event-study figure / robustness gauntlet — OR in epidemiology / public health style (target-trial emulation, IPTW + g-formula + TMLE triplet, Mendelian randomization, KM/AFT survival, E-value sensitivity, STROBE/TRIPOD reporting) — OR in ML causal inference style (DML, S/T/X/R/DR meta-learners, causal forest, Dragonnet/TARNet/CEVAE, BCF, CATE distribution, policy learning, conformal causal, fairness audit, causal discovery). Also covers exporting multi-column regression tables to Word / Excel / LaTeX (Stata outreg2 / esttab / R modelsummary equivalent) and bundling an entire replication appendix into one .docx / .xlsx / .tex file. Triggers on keywords "StatsPAI", "statspai", "AER empirical analysis", "applied micro pipeline", "Ta

面向中文学术论文的降 AIGC 检测率 Skill。针对知网、万方、维普、Turnitin 中文版的检测机制，识别并消除中文大语言模型的 17 类结构化写作痕迹。采用"定位 → 诊断 → 改写 → 自评 → 复查"五步闭环工作流，分章节差异化策略（摘要/引言/文献综述/方法/结果/讨论/结论），保持学术严谨性前提下通过检测。

Use when the user asks to run a full empirical / causal analysis in Python — by default in the style of an applied economics paper (AER / QJE / JPE / ReStud / AEJ) with DID / RD / IV / SCM / DML / matching, written-out estimating equation + identifying assumption, Table 1 / Table 2 / event-study figure / robustness gauntlet — OR in epidemiology / public health style (target-trial emulation, IPTW + g-formula + TMLE triplet, Mendelian randomization, KM/AFT survival, E-value sensitivity, STROBE/TRIPOD reporting) — OR in ML causal inference style (DML, S/T/X/R/DR meta-learners, causal forest, Dragonnet/TARNet/CEVAE, BCF, CATE distribution, policy learning, conformal causal, fairness audit, causal discovery). Also covers exporting multi-column regression tables to Word / Excel / LaTeX (Stata outreg2 / esttab / R modelsummary equivalent) and bundling an entire replication appendix into one .docx / .xlsx / .tex file. Triggers on keywords "StatsPAI", "statspai", "AER empirical analysis", "applied micro pipeline", "Ta

Use this skill whenever the user asks to run Stata code, debug a `.do` file, work with a Stata-backed Jupyter notebook, repair a Stata error, or interpret `r()` / `e()` results — and the `stata-code` MCP server is available. The skill teaches Claude the v1.0 RunResult schema, the 15 MCP tools, token-economy defaults, and the typed-error repair loop so Stata work is done via structured tool calls instead of guessed log parsing.