Tools and agents for social science research workflows
Hostile-referee stress test for papers. Not balanced critique — actively tries to kill the paper by finding fatal flaws, over-claims, unaddressed confounders, and identification weaknesses. Use before submission to pressure-test the argument against the strongest possible attack.
Substantive domain review for papers and analyses. Checks derivation correctness, assumption sufficiency, citation fidelity, code-theory alignment, logical consistency, and design-specific diagnostic reporting. Use after content is drafted or before presenting or submitting.
Dataset assessor for the data-finder skill. Applies a 5-point critique to each dataset proposed by the Explorer agent — measurement validity, sample selection, external validity, identification compatibility, and known issues. Adjusts feasibility grades and flags deal-breakers.
Dataset finder for the data-finder skill. Searches across public microdata, administrative data, survey panels, international sources, and novel alternatives. Returns a graded list of candidate datasets for a given research question. Dispatched in parallel pairs by data-finder.
First-time reader perspective on a paper. Reads cold with no prior context — not the spec, not the lit review, not the analysis scripts — and reports what lands, what confuses, and what a reader walks away believing. Use to catch clarity problems authors cannot see because they know too much.
End-to-end data analysis in R or Python — exploration through regression to publication-ready tables and figures. Use when running empirical analysis, writing analysis code, or producing output from data.
Find and assess datasets for a research question. Explorer agents search data source categories; Explorer-Critic stress-tests candidates and produces ranked list with feasibility grades. Use when identifying or evaluating data sources, not searching papers or running analysis.
Structured literature review via parallel Librarian agents. Searches top journals, working paper repos (NBER, SSRN, IZA); traces citation chains from key papers. Use when surveying existing research on a topic, not finding datasets or writing a paper.
Start a new research project. Structured interview formalizes the idea; generates research questions with identification strategies and a project spec. Use when developing or documenting a new research idea, not searching literature or data.
Proofread papers and manuscripts. Checks grammar, typos, layout, consistency, writing quality. Produces report without editing files. Use when finding surface-level errors, not substantive academic critique.
Modifies files
Hook triggers on file write and edit operations
Uses power tools
Uses Bash, Write, or Edit tools
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Work in Progress. This plugin is still under active development by a university student. For now, it is an experiment on the utility of Claude Code for social science research. If you have any feedback or suggestions, reach me on X at @felpix_.
A Claude Code plugin based on Pedro Sant'Anna's Claude Code workflow designed for producing social science research.
Install the plugin via Claude Code. Project scope or user (global) scope both work — the plugin is inert until you invoke a skill, so installing globally will not scaffold anything in unrelated repos.
/plugin marketplace add Felpix-Studios/social-science-research
/plugin install social-science-research@felpix-research
/research-setup/research-setup is the bootstrap entry point. It seeds references/domain-profile.md (your field's journals, datasets, and key researchers) and CLAUDE.md (project name, author, institution) into the project directory, then walks you through configuring your field, institution, journals, datasets, key researchers, and institutional colors. Other skills create quality_reports/, output/, scripts/, and manuscripts/ lazily — only the directory a skill needs to write to gets created, at the moment of write.
Enjoy using the plugin!
| Tool | Required For | Install |
|---|---|---|
| Claude Code (with plugin support) | Everything | claude.ai/download |
| R (>= 4.0) | /data-analysis (R track), /review-r | r-project.org |
| Python (>= 3.9) | /data-analysis (Python track), compact hooks | python.org |
| LaTeX distribution | /write-paper with .tex output | tug.org/texlive |
| Step | What You Do | Skill(s) | Output |
|---|---|---|---|
| 0. Setup | Configure field, journals, datasets, R colors | /research-setup | references/domain-profile.md, CLAUDE.md |
| 1. Idea | Interview → ideation → unified project spec | /new-project | quality_reports/project_spec_*.md |
| 2a. Literature | Parallel journal + working paper + citation search | /lit-review then /validate-bib | quality_reports/lit_review_*.md |
| 2b. Data | Find and assess datasets for the research question | /data-finder | quality_reports/data_exploration_*.md |
| 3. Analysis | Run R or Python analysis, review code quality | /data-analysis then /review-r | output/tables/, output/figures/, scripts/ |
| 4. Write | Draft the manuscript, then proofread → review → revise (loop until clean) | /write-paper → /proofread → /review-paper ⇄ /revise-paper | manuscripts/[name]-draft.tex, quality_reports/paper_review_*.md |
| 5. Verify | Gate analysis-paper consistency and bibliography before submission | /quality-gate, /validate-bib | quality_reports/quality_gate_*.md |
| Agent | What It Does |
|---|---|
librarian | Search one literature angle and return BibTeX |
explorer | Find datasets across one source category |
explorer-critic | Stress-test dataset candidates on 5 dimensions |
proofreader | Grammar, typos, layout, and consistency check |
domain-reviewer | Top-journal referee review through 5 lenses |
adversarial-reviewer | Hostile-referee attack: fatal flaws, over-claims, alt. explanations, rejection letter |
fresh-eyes-reviewer | First-time reader perspective — what lands, what confuses on a cold read |
r-reviewer | R code quality and reproducibility review |
verifier | Trace paper claims to output files |
npx claudepluginhub felpix-studios/social-science-research --plugin social-science-researchAdversarial multi-agent peer review of academic paper PDFs.
Build systematic literature databases using OpenAlex API. Phased workflow for search, screening, snowballing, annotation, and synthesis with structured user interaction.
PhD-level research capabilities: literature review, multi-source investigation, critical analysis, hypothesis-driven exploration, quantitative/qualitative methods, and lateral thinking
Skills and agents for anthropological research across the full research lifecycle — from question formulation through publication and career advancement
Semi-automated research assistant for academic research and software development, with skills for literature review, experiments, analysis, writing, and project knowledge management
Oh My Paper research harness: memory system, Codex delegation, and pipeline commands for academic research projects.
深度研究 — 13 agent 协作:研究问题定义、系统性检索、偏差评估、综合分析、引用编译