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SkillsMP は brycewang-stanford/Auto-Empirical-Research-Skills から 1,125 件の skill を収集しています。skill を開くとソースと詳細を確認できます。
このプロンプトをコピーして、使用中の AI アシスタントに送ってください。
Follow https://skillsmp.com/skill-install/prompt.md to install Agent Skills from https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills.収集済み skill 1,125 件中 40 件を表示しています。
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…
原文の言語: 英語
Remove signs of AI-generated writing from academic medical papers. Use when editing or reviewing manuscripts to make them sound more natural and professionally written. Based on Wikipedia's "Signs of AI writing" guide, adapted for medical literature.
原文の言語: 英語
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…
原文の言語: 英語
Entry point for the p-hacking skills suite. Routes a request to the right sub-skill for (a) mapping researcher degrees of freedom in an econometric design, (b) running an instrumented specification search, (c) detecting p-hacking in a body of results, (d)…
原文の言語: 英語
Name and classify p-hacking strategies, and quantify what each one does to the false-positive rate. Covers the twelve-strategy compendium of Stefan and Schoenbrodt (2023), thirteen econometrics-specific degrees of freedom (clustering doctrine, fixed-effect…
原文の言語: 英語
Map the garden of forking paths for a concrete econometric design and turn it into a machine-readable design card that the specification-search engine can walk. Use when asked how many defensible analyses a dataset or research design admits, to enumerate…
原文の言語: 英語
Run an instrumented walk of a specification universe and recover honest inference from it. Estimates every specification in a design card (or walks it with a realistic search procedure), logs a complete ledger, flags pathological specifications, calibrates…
原文の言語: 英語
Catalogue of prompt framings that determine whether an agent refuses or performs specification search, and the harness for probing them. Reproduces and extends the published finding that coding agents refuse an explicit request for significant results but…
原文の言語: 英語
Audit how an empirical write-up presents its analytical choices, and detect the rhetorical moves that convert a specification search into an apparently confirmatory finding. Covers HARKing, robustness theatre, selective disclosure, the vanishing pilot study,…
原文の言語: 英語
Test whether a body of reported results shows p-hacking, selective reporting or selection between stages, using the p-curve battery and the threshold and across-stages tests of Adda, Decker and Ottaviani (2020). Implements the binomial, Fisher, Stouffer and…
原文の言語: 英語
Make an empirical analysis robust to specification search, before or after the fact. Covers pre-registration and pre-analysis plans encoded as design cards, specification-curve reporting with the Simonsohn joint tests, Romano-Wolf and effective-multiplicity…
原文の言語: 英語
Run and score the agent p-hacking benchmark. Composes prompt cells across research framing and significance pressure, drives an agent through them, and scores each run into a P-Hacking Intensity index decomposed into selection, search breadth, estimate…
原文の言語: 英語
Model how a specification universe is actually walked, rather than assuming it is enumerated. Implements the search procedures a p-hacker or a pressured agent uses (first-significant stopping, random trial within a budget, greedy coordinate descent from the…
原文の言語: 英語
Run the instrumented specification search in the user's own statistical language — Stata (reghdfe, ivreghdfe, rdrobust, did2s), R (fixest, rdrobust, did2s), Python (statsmodels, linearmodels) or StatsPAI — and bring the results back into the audit, null…
原文の言語: 英語
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…
原文の言語: 英語
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**…
原文の言語: 英語
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…
原文の言語: 英語
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 +…
原文の言語: 英語
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**…
原文の言語: 英語
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 +…
原文の言語: 英語
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…
原文の言語: 英語
Autonomous research review loop using any OpenAI-compatible LLM API. Configure via llm-chat MCP server or environment variables. Trigger with "auto review loop llm" or "llm review".
原文の言語: 英語
Autonomous multi-round research review loop using MiniMax API. Use when you want to use MiniMax instead of Codex MCP for external review. Trigger with "auto review loop minimax" or "minimax review".
原文の言語: 英語
Autonomous research review loop using any OpenAI-compatible LLM API. Configure via llm-chat MCP server or environment variables. Trigger with "auto review loop llm" or "llm review".
原文の言語: 英語
Autonomous multi-round research review loop using MiniMax API. Use when you want to use MiniMax instead of Codex MCP for external review. Trigger with "auto review loop minimax" or "minimax review".
原文の言語: 英語
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.
原文の言語: 英語
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…
原文の言語: 英語
Use when auditing a finished or near-finished AER, AER:Insights, or AEJ manuscript for internal consistency: headline numbers across abstract, introduction, results, and tables; sample sizes; log-point and percentage-point conversions; cross-references; and…
原文の言語: 英語
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…
原文の言語: 英語
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…
原文の言語: 英語
Use when positioning a manuscript against the existing economics literature, building the antecedents map for the introduction, deciding what to cite, or verifying that every reference in the bibliography is real, correctly attributed, and cited to the…
原文の言語: 英語
Use when drafting or revising the body sections of an AER, AER:Insights, or AEJ manuscript — institutional background, data, empirical strategy, results, mechanisms, and conclusion. Covers equation conventions, results-paragraph narration, magnitude…
原文の言語: 英語
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…
原文の言語: 英語
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…
原文の言語: 英語
Use when a complete draft exists and needs an adversarial internal review before submission — simulating the AER desk screen and three referee reports with calibrated severity, scoring the paper against the editorial rubric, and producing a prioritized revise…
原文の言語: 英語
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…
原文の言語: 英語
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…
原文の言語: 英語
Use when aer-identification has fixed the design, after methodology choice and before aer-robustness or aer-tables-figures, to run an AER-track analysis with StatsPAI — the agent-native Python engine and MCP server for causal inference, robustness,…
原文の言語: 英語
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.
原文の言語: 英語
Use when constructing or revising regression tables, descriptive statistics tables, or figures after results are estimated and before submission for an AER, AER:Insights, or AEJ manuscript. Implements AER booktabs house style, regression-table layout, and…
原文の言語: 英語