lit-draft

Draft literature review sections from outlines and extracted notes.

37|5|Updated Jun 6, 2026

Search Tags:#quality control #literature review #academic writing #section drafting #numeric accuracy

Ready to run

npx skills add https://github.com/bionoob7/nlr-workflow --skill lit-draft

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: lit-draft
Source: https://github.com/bionoob7/nlr-workflow/tree/main/.claude/skills/lit-draft
Command: npx skills add https://github.com/bionoob7/nlr-workflow --skill lit-draft

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, numpy, requests, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill streamlines the process of drafting sections for academic literature reviews, ensuring accuracy and adherence to quality constraints.

Core Features & Use Cases

Section Drafting: Drafts individual sections of a literature review based on a project outline and extracted data.
Quality Control: Enforces numeric accuracy, citation density, and narrative synthesis style.
Use Case: For a review manuscript on AI in healthcare, this Skill can be used to draft the "Methods" section, incorporating specific metrics and patient counts from the extracted data.

Quick Start

To draft the "Methods" section, use the lit-draft skill with the identifier: /lit-draft "Methods".

Frequently Asked Questions about lit-draft

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

FAQPage Schema

How do I draft specific sections of an academic literature review manuscript?▼

To draft academic literature review sections, you can use a skill that generates manuscript text based on a project outline and extracted data. This process requires maintaining per-paper extraction notes to ensure precise narrative synthesis.

How does numeric accuracy work when drafting a literature review?▼

Numeric accuracy in literature review drafting is enforced through data verification using Python libraries like pandas and numpy. This ensures specific metrics and patient counts from extracted data are correctly incorporated into the text.

Do I need Python libraries to automate literature review drafting?▼

Yes, you need Python libraries including pandas, numpy, and requests to automate literature review drafting. These dependencies handle the text processing and data verification required to enforce quality constraints and numeric accuracy.

Can I use this approach to draft a methods section incorporating specific metrics?▼

Yes, you can draft a methods section incorporating specific metrics by processing per-paper extraction notes. The system synthesizes narrative text while enforcing citation density and extracting patient counts directly from your structured data.

What are the limitations of using automated tools for academic writing quality control?▼

Limitations of automated academic writing quality control include the strict requirement for a pre-existing section outline and per-paper extraction notes. Without structured input data, the system cannot enforce narrative synthesis or verify numeric accuracy.