What does Paper-WorkFlow do?
Empirical papers in economics/social sciences · An end-to-end meta-orchestrator that turns a research idea into a submission-ready empirical paper using AI.
An end-to-end meta-orchestrator for empirical papers in economics/social sciences that turns a single-sentence idea into a submission-ready full paper using AI.
Paper-WorkFlow is a comprehensive AI-powered meta-orchestrator designed for researchers in economics and social sciences. It automates the entire empirical paper writing process—from a rough idea to a polished, submission-ready manuscript. The tool supports various AI agents and developer environments (e.g., Claude Code, Cursor, Codex) and guides users through structured steps: literature review, data collection, empirical analysis, and writing. Its value lies in reducing manual effort by maintaining context across tasks, ensuring consistency, and allowing researchers to focus on substantive analysis rather than workflow logistics.
npx skills add brycewang-stanford/Paper-WorkFlowInstalled? Explore more Research & Data Analysis skills: affaan-m/uspto-database, affaan-m/scholar-evaluation, affaan-m/literature-review · View all 6 →
Empirical papers in economics/social sciences · An end-to-end meta-orchestrator that turns a research idea into a submission-ready empirical paper using AI.
USPTO patent and trademark data workflow for official record lookup, PatentSearch queries, TSDR checks, assignment data, and reproducible IP research logs.
Structured scholarly-work evaluation for papers, proposals, literature reviews, methods sections, evidence quality, citation support, and research-writing feedback.
Systematic literature-review workflow for academic, biomedical, technical, and scientific topics, including search planning, source screening, synthesis, citation checks, and evidence logging.
The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond.
Agent skill repository discovered by 10x-chat research.
Review backend code for quality, security, maintainability, and best practices based on established checklist rules. Use when the user requests a review, analysis, or improvement of backend files (e.g., `.py`) under the `api/` directory. Do NOT use for frontend files (e.g., `.tsx`, `.ts`, `.js`). Supports pending-change review, code snippets review, and file-focused review.