code-llm-papers-guide
Survey and paper collection on LLMs for code generation
Browse reusable Agent Skills, each with a clear purpose and practical guidance.
Survey and paper collection on LLMs for code generation
Design conjoint experiments: attributes, power, AMCE/AMIE estimation.
Diagnose conjoint design integrity, estimation choices, and validity.
Design cross-national survey experiments: power, equivalence, localization.
Use when collecting data for a research project, downloading time series, building a dataset, accessing economic or social data APIs, or scraping data from a non-API source. Handles source discovery, respectful collection, local caching, and manifest documentation.
Fetch economic data from FRED, World Bank, BLS, OECD, and Yahoo Finance
Data science methodology for Python research: EDA, validation, causal inference (IV, DiD, RD, synthetic control), clustering/PCA/UMAP, supervised ML, geospatial, visualization. Method selection guidance. For syntax, load tool-specific skills.
AI-driven multi-agent research assistant for end-to-end studies
Challenge research design decisions, assumptions, and methodology choices with specific critical questions. Helps strengthen the paper before submission.
Econometrics skill for Difference-in-Differences (DID) analysis. Activates when the user asks about: "difference in differences", "DID", "DiD", "diff-in-diff", "parallel trends", "treatment group", "control group", "pre-treatment", "post-treatment", "policy evaluation", "natural experiment", "staggered DID", "event study regression", "two-way fixed effects DID", "callaway santanna", "sun and abraham", "双重差分", "倍差法", "平行趋势", "处理组", "对照组", "政策评估", "事件研究", "交错DID", "渐进处理"