Felpix-Studios

End-to-end empirical data analysis workflow for R or Python projects.

Find and assess datasets for a research question.

Repository-wide consistency audit for skills, hooks, rules, and docs.

Systematic literature review workflow using parallel librarian agents.

Structured interview and project-spec workflow for new research ideas.

Proofreading workflow for academic manuscripts and papers.

Verify that paper claims match analysis outputs before submission.

Configure field, institution, datasets, journals, and project metadata.

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.

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.

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.

Interactive wizard that configures the social-science-research plugin for a new project. Asks about field, institution, journals, datasets, key researchers, R palette; writes answers to references/domain-profile.md and CLAUDE.md. Use first when starting fresh or configuring the plugin.

Create or update a session log capturing decisions, changes, progress. Use to document what was accomplished during a work session.

Verify every quantitative claim in the paper traces to an analysis output file, and no important output was omitted. Use when checking the paper matches the analysis before submission.

R code review on existing scripts. Checks quality, reproducibility, domain correctness, professional standards. Produces report without editing files. Use when evaluating or auditing existing R code, not writing new analysis.

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.

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.

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.

Verify every quantitative claim in the paper traces to an analysis output file, and no important output was omitted. Use when checking the paper matches the analysis before submission.

Interactive wizard that configures the social-science-research plugin for a new project. Asks about field, institution, journals, datasets, key researchers, R palette; writes answers to references/domain-profile.md and CLAUDE.md. Use first when starting fresh or configuring the plugin.

R code review on existing scripts. Checks quality, reproducibility, domain correctness, professional standards. Produces report without editing files. Use when evaluating or auditing existing R code, not writing new analysis.

Create or update a session log capturing decisions, changes, progress. Use to document what was accomplished during a work session.

Adversarial multi-agent peer review of an academic paper PDF. Use when the user invokes /peer-review, or asks to peer-review, critique, evaluate, or red-team an academic paper. Orchestrates a Red Team → Blue Team → Assessor → Reviewer verification cascade (~20 subagents) with optional math and code audits, producing a structured peer-review report.