What problem does it solve? Research feedback and low-stakes assessment processes often lack traceable evidence, documented constructs, and governance controls, and risk being misused for consequential personnel decisions. This Skill provides a qualitative-first developmental review workflow for scholarly works with strict safety boundaries and auditable local tooling. ## Core Features & Use Cases - Developmental Review Workflow: Guides construct definition, rubric adaptation, independent rating, and qualitative synthesis for papers, drafts, protocols, and research ideas. - Local Quality-Control CLIs: Bundled standard-library Python scripts validate rubrics, compute bounded descriptive scores, check evidence traceability, summarize inter-rater agreement, test weight sensitivity, and audit process governance. - Hard Safety Boundaries: Prohibits ranking people, proxy metrics like impact factor or h-index, and any use in hiring, tenure, funding, admissions, or award decisions. - Use Case: A journal club committee wants structured, evidence-cited feedback on a manuscript draft. They adapt the rubric template, record ratings with evidence references, run the traceability and agreement checks, and release a developmental report after human committee review. ## Quick Start Ask the assistant to developmentally review a scholarly work using the scholar-evaluation rubric template and validate it with the bundled local scripts.
