Structural topic modeling: STM spec, topic count, coherence-exclusivity.
原文の言語: 英語
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このリポジトリの skills
SkillsMP は brycewang-stanford/Auto-Empirical-Research-Skills から 1,111 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。
brycewang-stanford/Auto-Empirical-Research-Skills収集済み skill 1,111 件中 40 件を表示しています。
Structural topic modeling: STM spec, topic count, coherence-exclusivity.
原文の言語: 英語
VLM-based OCR pipeline: model selection, prompts, architecture, evaluation.
原文の言語: 英語
Design, run, and critique causal inference workflows in Stata. Use when the user is working on identification, treatment effects, DiD, IV, event studies, RD, or assumption-sensitive empirical claims.
原文の言語: 英語
Audit datasets for structure, missingness, labeling, suspicious values, duplicate identifiers, and documentation readiness. Use when a researcher asks for data QA, codebook review, sanity checks, or pre-analysis cleanup guidance.
原文の言語: 英語
Track dataset lineage, transformation steps, merge logic, and reproducibility risks in Stata workflows. Use when the user needs to explain where data came from, how it changed, or why a pipeline can be trusted.
原文の言語: 英語
Diagnose local Stata, MCP, package, startup, graph-export, and permissions issues. Use when setup is failing, Stata is not discovered, packages are missing, logs are truncated, or a managed machine behaves differently from a normal workstation.
原文の言語: 英語
List, export, and review Stata graphs from the current session.
原文の言語: 英語
Look up Stata command documentation and display formatted help text.
原文の言語: 英語
Describe and summarize the current dataset in memory. Optionally inspect a specific variable with codebook.
原文の言語: 英語
Run static analysis on a Stata .do or .ado file and report style and best-practice issues.
原文の言語: 英語
Tail, read, or search a Stata log file from a previous command or background task.
原文の言語: 英語
Improve, modernize, and optimize existing Stata code for performance, portability, and maintainability. Use when legacy patterns such as preserve/restore, cd,
原文の言語: 英語
Plan and critique power, MDE, and sample-size calculations for Stata-based research workflows. Use when the user is designing a study, checking detectability, or defending precision claims.
原文の言語: 英語
Review regression outputs, tables, and graphs for publication readiness. Use when the user asks whether a result is ready for a paper, appendix, seminar, referee response, or coauthor review.
原文の言語: 英語
Organize and execute Stata workflows for referee responses, robustness requests, and coauthor follow-ups. Use when the user needs to answer a critique with targeted reruns, tables, figures, and a defensible audit trail.
原文の言語: 英語
Run replication, robustness, and specification-sensitivity workflows for Stata projects. Use when a researcher wants to reproduce a result, rerun a pipeline, compare specifications, audit a do-file sequence, or check whether a claim is stable.
原文の言語: 英語
Fetch and display stored r(), e(), and s() results from the last Stata command.
原文の言語: 英語
Run arbitrary Stata code or a .do file and display the result.
原文の言語: 英語
Install, configure, update, or verify mcp-stata across Claude Code, Codex, Gemini CLI, Cursor, Windsurf, and VS Code. Activate when users ask to set up the Stata toolkit or troubleshoot the installation.
原文の言語: 英語
Show mcp-stata identity, connected tools, and status. Use when the user asks if mcp-stata is available, asks about access to the toolkit, or asks what Stata tools are connected.
原文の言語: 英語
Build and review paper-ready regression, balance, and summary tables from Stata outputs. Use when the user needs a clean table for a draft, appendix, or coauthor share-out.
原文の言語: 英語
Activate when users mention Stata commands, .do files, regressions, econometrics, stored results, graphs, dataset inspection, replication, or Stata errors. Route the task through mcp-stata tools and the specialized research skills instead of treating it as…
原文の言語: 英語
Choose the appropriate CausalPy experiment class from a causal question, data structure, treatment assignment, and identification assumptions. Use before writing analysis code when the method is not yet settled.
原文の言語: 英語
Fit, summarize, plot, and interpret a chosen CausalPy experiment. Use after the causal method has been selected, including when configuring PyMC/sklearn models and scale-aware custom priors.
原文の言語: 英語
Performs placebo-in-time sensitivity analysis with hierarchical null model and optional Bayesian assurance. Use when checking model robustness, verifying lack of pre-intervention effects, or estimating study power.
原文の言語: 英語
Guide users through writing a systematic literature review (SLR) following the PRISMA 2020 framework. Use this skill whenever the user mentions 'systematic review', 'systematic literature review', 'SLR', 'PRISMA', 'PRISMA 2020', 'PRISMA flow diagram', 'PRISMA…
原文の言語: 英語
Conduct rigorous thematic analysis (TA) of qualitative data following Braun and Clarke's (2006) six-phase framework. Use whenever the user mentions 'thematic analysis', 'TA', 'Braun and Clarke', 'qualitative coding', 'identifying themes', or asks for help…
原文の言語: 英語
Audit in-text/reference parity, DOIs, claim support, and citation style.
原文の言語: 英語
Clean and reshape Qualtrics conjoint exports to analysis-ready long format.
原文の言語: 英語
Diagnose conjoint design integrity, estimation choices, and validity.
原文の言語: 英語
Before implementing, generate 3-5 conceptually distinct approaches labeled by creativity dimension (Novel, Surprising, Diverse, Conventional), then hold for selection. Brainstorm-then-select to resist defaulting to the most obvious solution.
原文の言語: 英語
Audit manuscript and replication package against FAIR open-science principles.
原文の言語: 英語
Audit figures, tables, captions, cross-references, and statistical notes.
原文の言語: 英語
Draft a senior peer-review report on a social-science manuscript.
原文の言語: 英語
Design and diagnose list experiments (item count technique).
原文の言語: 英語
Check methods reporting against CONSORT, JARS, DA-RT standards.
原文の言語: 英語
Scaffold or audit a social-science replication package at a target directory. Generates folder structure, README, master.R, figure/table crosswalk, codebook template, LICENSE placeholder, and pre-release checklist. Adapted from Yusaku Horiuchi's…
原文の言語: 英語
Patterns for Bayesian inference in R using brms, including multilevel models, DAG validation, and marginal effects. Use when performing Bayesian analysis.
原文の言語: 英語
R object-oriented programming guide for S7, S3, S4, and vctrs. Use when designing R classes or choosing an OOP system.
原文の言語: 英語
R package development guide covering dependencies, API design, testing, and documentation. Use when developing R packages.
原文の言語: 英語