What problem does it solve? Writing a competitive KAKENHI (Japanese JSPS research grant) proposal requires deep knowledge of the review system, evaluation criteria, and unwritten conventions. Most rejections come from preventable issues: unclear research questions, missing responses to evaluation sub-criteria, poor figure design, and layouts that fail under monochrome review. This Skill encodes the full body of practical knowledge needed to avoid those failure modes. ## Core Features & Use Cases - Defense against deduction: Maps every official evaluation sub-criterion (JSPS原文) to specific sections of the proposal so reviewers cannot find a reason to mark items as insufficient, including the commonly missed budget-plan consistency statement. - Category-specific guidance: Covers Kiban B/C and Challenging Research (Hattatsu/Hoga) with distinct strategies, title conventions, page budgets, and the pre-screening reality that the summary version is the real first gate for Hoga. - Evaluation simulation workflow: Provides a self-review procedure that reproduces the actual review process—scoring each sub-criterion 4/3/2/1 with persona-based mock reviews (specialist, adjacent-field, statistics-strict, practitioner, administrative). - Production practicalities: Figure design rules (grayscale-first, 10cm canvas, font-size math), LaTeX fixed-frame page adjustment, Word-format handling for JSPS bilateral programs, and parallel draft management. - Use Case: When revising a rejected Kiban B proposal, use the Skill to map disclosure feedback (＊ marks) to weak sections, run the evaluation simulation, and restructure only the flagged criteria while preserving highly rated sections. ## Quick Start Ask the AI to review your KAKENHI proposal draft against the evaluation criteria using the kakenhi_writing skill and produce a criterion-by-criterion response table.
