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Estimate effects by comparing observations just above and below an eligibility threshold using regression discontinuity design. Covers plots, comparison range selection, sharp and fuzzy designs, validity checks, and sensitivity analysis for reporting research findings.

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• Estimate a policy effect near an eligibility threshold
• Analyze treatment with imperfect compliance
• Check manipulation and covariate balance at a cutoff

Proofread academic manuscripts in LaTeX, Markdown, or plain text for typos, grammar, and unclear wording. Review proposed changes one at a time before applying them, while preserving arguments, citations, equations, and code.

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• Checking grammar and typos in an English paper
• Reviewing wording changes one at a time
• Polishing prose in LaTeX or Markdown

Analyze empirical economics PDFs through five questions covering the research problem, causal identification, target effect, robustness, and contribution. Receive a structured Chinese report and a practical checklist of research methods to adapt to other studies.

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• Reading an empirical economics paper systematically
• Understanding how a paper identifies causal effects
• Examining robustness checks and research limitations

Choose and implement machine learning methods for estimating causal effects with many control variables or effects that differ across groups. Guidance covers DML, causal forests, and related methods, including diagnostics and limits on interpretation.

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• Estimating average effects with many control variables
• Studying how treatment effects differ across groups
• Implementing cross-fitting for causal estimation

Convert Markdown files into Word documents with headings, tables, images, and code formatting. Supports batch conversion, optional contents pages, custom templates, and Chinese document styling, including automatic superscript formatting for eligible citation numbers.

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• Converting a Markdown report into a Word document
• Formatting Chinese documents with citation superscripts
• Batch converting Markdown files in a folder

Find related research by following a paper's references and the later papers that cite it through Semantic Scholar. Filters prioritize relevant candidates, avoid duplicate reviews, and record citation relationships for a focused literature search.

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• Finding related studies from a relevant paper
• Checking which papers cite a study
• Tracing earlier work through references

Plan, run, and interpret linear regression with guidance on model choices, diagnostic checks, and reporting. Python, R, and Stata examples help examine coefficients, assess assumptions, and choose standard errors that account for different error patterns.

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• Specify a regression model for an empirical study
• Check residuals and correlations among explanatory variables
• Choose robust or clustered standard errors

Document datasets so others can understand and reproduce an analysis. Create variable definitions, source records, sample filtering tables, and merge documentation, or assemble a codebook describing how the data was constructed.

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• Prepare dataset documentation for replication materials
• Explain variables and construction steps to co-authors
• Record merge keys and matching rates

Turn a financial economics research idea into a structured, 12-part empirical proposal. It outlines hypotheses, matching data sources, methods for testing causal effects, illustrative results tables, and robustness checks, while examining the study’s contribution.

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• Drafting a financial economics research proposal
• Finding Chinese and international data sources
• Planning tests of causal effects

Review economic reports for methodological gaps, unsupported figures, and errors such as double counting. Produce a red, amber, and green scorecard with issues ranked by severity, using the relevant appraisal framework and proposing corrections when requested.

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• Checking fiscal and macroeconomic briefings before submission
• Reviewing cost-benefit assumptions and double counting
• Auditing reports against a selected appraisal framework

Automate website tasks through a command-line browser tool. Agents can navigate pages, fill forms, extract text, capture screenshots, and test web apps, with saved login state available for later sessions.

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• Automating form entry and submission
• Extracting text from web pages
• Saving screenshots and page PDFs

Organize research code, datasets, and outputs with consistent folders and script names. Provides Python and Stata templates, processing stage numbers, and guidance for paths, logs, and documentation so collaborators can follow the workflow.

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• Setting up folders for a new research project
• Standardizing Python and Stata script names
• Separating raw, intermediate, and cleaned datasets

Formats regression results as LaTeX tables for economics papers, with standard errors, significance stars, fixed-effect labels, and statistical notes. Provides templates and guidance for converting Stata, Python, or R output into main-text and appendix tables.

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• Converting Stata, Python, or R results into LaTeX tables
• Comparing baseline and fixed-effects regression models
• Preparing tables for instrumental-variable estimates

Prepare survey and administrative datasets for analysis with Python, R, or Stata. Generate cleaning code to handle missing values, duplicates, outliers, and derived variables, with documented transformations and integrity checks that support reproducible research.

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• Clean missing values, duplicates, and outliers in survey data
• Merge multiple data sources for analysis
• Prepare panel datasets that track units over time

Turn a research proposal into a corporate finance study using data from the United States, Europe, Japan, or multiple countries. The workflow supports journal selection, Python analysis, statistical checks, and preparation of LaTeX tables and figures.

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• Analyze policy effects on corporate investment and financing
• Select target journals for a research proposal
• Produce regression tables and validation figures

Analyze whether an intervention causes an outcome using DoWhy in Python. Build a graph of causal assumptions, determine whether the effect can be estimated, compare estimates, and test how well results withstand changes to assumptions or data.

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• Estimating an advertising campaign’s causal effect on sales
• Analyzing observational data with confounding factors
• Testing how robust a causal estimate is

Create Chinese academic presentation slides with Beamer, a LaTeX slide system, using a fixed CUFE template. Organizes research arguments, tables, and figures, with spacing and slide-splitting rules to address overflow, and supports updating existing decks.

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• Preparing Chinese slides for a thesis defense
• Building slides for a Chinese academic conference
• Fixing text and table overflow

Analyze academic papers by organizing research questions, data, methods, findings, and limitations. Compare studies in a table, identify unanswered questions, and document how each paper relates to a research project, with BibTeX reference entries.

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• Summarizing a paper's methods and findings
• Comparing related studies in a table
• Identifying unanswered research questions

Analyze interviews, focus groups, and open-ended survey responses using Braun and Clarke's six-phase framework. Label meaningful passages, develop and review themes, and produce an academic Word report with an annotated map showing theme relationships.

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• Identifying themes across interview transcripts
• Analyzing open-ended survey responses
• Choosing an approach before coding qualitative data

Generate Python code to retrieve economic indicators and financial prices from sources including FRED, World Bank, BLS, OECD, IMF, and Yahoo Finance. Scripts include data cleaning, indicator documentation, and error handling for reusable datasets and updates.

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• Downloading GDP, unemployment, and inflation data
• Building datasets across countries and sources
• Automating updates for ongoing research

Organize Python and Stata research code using shared conventions for folders, filenames, data checks, logs, and outputs. Templates guide data cleaning, merging, regression analysis, and figure or table creation, with documentation for reproducing results.

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• Setting up folders and script order for a research project
• Standardizing Python data cleaning and merging scripts
• Preparing Stata regressions and output tables

Guide Claude Code to check files, documentation, data, and execution results before presenting answers. The skill sets verification steps for coding, analysis, and writing, including checking citations and numbers, diagnosing errors, and clearly stating what cannot be verified.

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• Checking function names and arguments before coding
• Inspecting data columns and file paths before analysis
• Verifying row counts and missing values after joins

Coordinate complex tasks across multiple agents through planning, execution, review, and revision. Agents save their work to local files and return status summaries, helping the main agent manage limited context while delivering the final result.

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• Breaking a complex task into coordinated agent assignments
• Running independent subtasks in parallel
• Reviewing results and revising them from feedback

Edit Chinese text to remove stiff translation patterns, repetitive AI-style phrases, and unnecessary filler. Apply consistent spacing around English and numbers, appropriate punctuation, and spacing after bold Chinese text.

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• Revising Chinese text that sounds translated
• Removing repetitive AI-style phrasing
• Standardizing mixed Chinese and English formatting