Draft literature review sections from outlines and extracted notes.
npx skills add https://github.com/bionoob7/nlr-workflow --skill lit-draft
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
This Skill streamlines the process of drafting sections for academic literature reviews, ensuring accuracy and adherence to quality constraints.
To draft the "Methods" section, use the lit-draft skill with the identifier: /lit-draft "Methods".
High-intent search queries and answers about installing and using this skill.
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.
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.
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.
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.
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.
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