Orchestrate academic research pipelines across literature review, writing, and analysis.
npx skills add https://github.com/wen-chen/dr-cook --skill dr-cook
Please help me install this Agent Skill. Skill: dr-cook Source: https://github.com/wen-chen/dr-cook/tree/main Command: npx skills add https://github.com/wen-chen/dr-cook --skill dr-cook
Dr. Cook is a modular AI skill suite for academic researchers. It orchestrates end-to-end research tasks—ranging from literature review and gap analysis to manuscript writing, peer review, and bioinformatics analysis—without requiring users to re-enter context between steps. By loading SKILL.md frontmatter instructions and domain-specific references, it enables reproducible pipelines in bilingual English/Chinese contexts.
High-intent search queries and answers about installing and using this skill.
Academic research workflows can be automated using a modular AI skill suite that preserves context across stages. It loads domain-specific references to chain literature reviews, gap analysis, and manuscript writing without re-entering context.
Bilingual bioinformatics data analysis and grant writing pipelines are supported natively. The suite provides domain-aware defaults for English and Chinese inputs, enabling reproducible end-to-end research automation.
To chain academic research modules, use the central router to classify intent and load modules automatically. A standardized context_output data contract passes information between stages, allowing custom module sequences for grant writing or manuscript submission.
This modular research workflow suite requires no external dependencies. You simply clone the repository into the Claude Code skills directory to install and start orchestrating end-to-end research tasks immediately.
The best way to bypass the router for peer review is direct module invocation. You can call specific modules like dr-cook:paper-writer directly to execute targeted academic tasks without automatic intent classification.
Context must be preserved across academic research stages to prevent manual data re-entry between literature reviews, gap analysis, and writing. A shared context schema enables pipeline automation and ensures consistent sum of context retention.
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