empirical-analysis-r

Nine-skill stack for top-5 economics manuscripts (AER / AER: Insights / AEJ): topic selection, modern causal identification (DiD / IV / RDD / SCM / Bartik), referee-anticipating robustness, Keith-Head-style introductions, AER booktabs tables, AEA Data and Code Availability deposits (openICPSR-ready), submission preflight, and R&R rebuttal letters. Ships Stata / R / Python templates and classic-AER-paper exemplars.

15 skills

Study p-hacking in econometric designs: map researcher degrees of freedom, audit a specification search with a complete ledger and null-calibrated honest p-values, replay realistic search procedures, detect p-hacking in a literature, immunise an analysis, and benchmark whether an AI agent p-hacks. Research and teaching use only.

11 skills

Explicit 8-step empirical-analysis pipeline in the traditional Python econometrics stack (pandas + statsmodels + linearmodels + pyfixest + rdrobust + econml + causalml): cleaning -> variable construction -> Table 1 -> diagnostics -> estimation (OLS/IV/DID/RDD/PSM/SCM/DML/Causal Forest) -> robustness -> mechanism/heterogeneity/mediation -> publication-ready tables & figures. Includes epidemiology and ML-causal modes.

1 skill

Explicit 8-step empirical-analysis pipeline in the traditional Stata ecosystem (reghdfe + ivreg2 + csdid + did_imputation + sdid + rdrobust + synth + psmatch2 + teffects + esttab + coefplot): from use/import to .tex/.rtf tables and .pdf figures, with the full modern DID toolkit (bacondecomp + honestdid + rwolf + ritest). Includes epidemiology and ML-causal modes. The first choice for referee-level Stata replication packs.

1 skill

Comprehensive Stata reference — syntax, data management, econometrics, causal inference, graphics, Mata, and 20 community packages.

1 skill

Develop high-performance C/C++ plugins for Stata — SDK setup, .ado wrappers, cross-platform compilation, performance optimization, and Python/R package translation.

1 skill

Generate natively editable PPTX from PDF / DOCX / URL / Markdown — real DrawingML shapes, text boxes, charts, and animations. Note: run `pip install -r requirements.txt` inside the installed plugin directory for the Python post-processing scripts to work.

1 skill

5 skills + 35 modes + Material Passport pipeline. Includes v3.6.7 cross-model audit gate and v3.6.8 generator-evaluator contract.

5 skills · 16 commands · 4 agents · hooks

Exa AI web search, deep research, and content extraction. Provides MCP tools and research skills for comprehensive web search, people discovery, company research, academic papers, and more.

2 skills · 1 MCP server

Connect to preclinical research tools and databases (literature search, genomics analysis, target prioritization) to accelerate early-stage life sciences R&D

6 skills · 11 MCP servers

Enterprise / cross-functional Research Operations domain — the managed counterpart to the academic research/ domain. v2.9.0 ships 5 skills: orchestrator (context: fork), clinical-research (protocol synopsis, endpoint selection, sample-size/power for means/proportions/survival, phase-gate feasibility), research-finance (R&D budgeting with F&A split, burn/runway, capitalize-vs-expense routing, portfolio ROI), market-research (TAM/SAM/SOM top-down and bottoms-up, survey sampling with FPC, Kotler segmentation), and product-research (goal-matched study design, method-based saturation, insight synthesis flagging single-source anecdotes). Hard rules: outputs are estimates routed to a named clinical or finance owner, market sizes show method + assumptions, product insights require recurrence across independent participants. 24 stdlib Python tools, 12 reference docs. Distinct from ra-qm-team, finance, research/grants, product-team, and marketing-skill.

6 skills · 7 commands · 2 agents

Contribution-first, reviewer-aware academic paper and report writing system.

2 skills