nsfc_i2_revision

Name: nsfc_i2_revision
Availability: InStock
Author: jinyh

Parse NSFC review comments and generate six-section revision reports with literature-backed edits.

19|4|Updated Feb 23, 2026

Search Tags:#ai #review #revision #grant-writing #interdisciplinary #nsfc

Ready to run

npx skills add https://github.com/jinyh/nsfc-review --skill nsfc-i2-revision

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: nsfc_i2_revision
Source: https://github.com/jinyh/nsfc-review/tree/main
Command: npx skills add https://github.com/jinyh/nsfc-review --skill nsfc-i2-revision

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill interprets NSFC review comments and converts them into precise, revision-ready actions, reducing turnaround time and improving submission quality.

Core Features & Use Cases

Parse multi-expert review notes (Markdown or PDF) and categorize criticisms, suggestions, and affirmations using a standard problem taxonomy.
Generate concrete revision proposals (section/page references, specific edits, and literature-backed justifications) and craft polite reply templates.
Produce a structured revision report with an appendixed literature summary for traceability.

Quick Start

Provide the review comments file, and run the skill to generate the structured revision report.

Frequently Asked Questions about nsfc_i2_revision

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I turn NSFC review comments into a structured revision plan?▼

To turn NSFC review comments into a structured revision plan, provide the review notes file to parse criticisms and generate concrete edits. The system produces a six-section revision report with issue diagnoses, modification details, reply templates, and a literature appendix.

Can I use AI to generate revision proposals for interdisciplinary NSFC grant proposals?▼

Yes, you can use AI to generate revision proposals for interdisciplinary NSFC grants. The system applies to AI+ reviews across physics, chemistry, biology, earth science, materials, energy, and environment, retrieving supporting literature to justify each suggested change.

What is the best way to categorize criticisms and suggestions from multi-expert grant reviews?▼

The best way to categorize criticisms from multi-expert grant reviews is by applying a standard problem taxonomy. The system parses Markdown or PDF review notes to categorize criticisms, suggestions, and affirmations, then generates literature-backed justifications for your revisions.

Does the generated revision report include literature references for the proposed edits?▼

Yes, the generated revision report includes literature references for the proposed edits. It outputs a structured revision report with an appendixed literature summary for traceability, ensuring each suggested modification is backed by retrieved supporting literature.

How do I format reply templates for NSFC grant reviewers based on their comments?▼

To format reply templates for NSFC grant reviewers, parse the review comments to generate polite response templates addressing diagnosed issues. The system creates targeted revision proposals with specific edits and literature-backed justifications to include in your reviewer replies.