What problem does it solve? Researchers struggle to track how the same construct or variable is operationalized differently across papers, datasets, and models, making it hard to reconcile measurement choices with what is actually available in a local project. ## Core Features & Use Cases - Cross-paper measurement comparison: Reads wiki entries for variables, papers, datasets, and models to build a comparison table covering construct, role, measurement, data source, sample frequency, advantages, and risks. - Local availability audit: Checks project READMEs and research notes to mark literature fields that are missing from the local project as missing from project. - Archived, logged outputs: Saves results to wiki/outputs/variable-map-{slug}-{date}.md and logs the run via tools/research_wiki.py log. - Use Case: Before specifying an econometric model, ask how "firm innovation" is measured across your literature wiki; the Skill returns a table contrasting patent counts, R&D intensity, and survey measures, flagging which data sources your project lacks. ## Quick Start Ask the assistant to map how the variable "firm innovation" is measured across all papers in the research wiki and check which data sources are available locally.
