Automate end-to-end meta-analysis orchestration from TOPIC.txt to manuscript and reviewer responses.
npx skills add https://github.com/htlin222/meta-pipe --skill ma-end-to-end
Please help me install this Agent Skill. Skill: ma-end-to-end Source: https://github.com/htlin222/meta-pipe/tree/main/ma-end-to-end Command: npx skills add https://github.com/htlin222/meta-pipe --skill ma-end-to-end
End-to-end AI-assisted meta-analysis pipeline orchestration from TOPIC.txt to final manuscript and reviewer responses. Use when the user provides a topic and wants the full meta-analysis workflow, tracking, and final paper.
Create a project with TOPIC.txt at the root and ask Claude to start the end-to-end meta-analysis workflow.
High-intent search queries and answers about installing and using this skill.
You can automate a reproducible meta-analysis workflow by creating a project with a TOPIC.txt file at the root. This orchestrates the 9-stage pipeline from protocol creation and screening to manuscript rendering and reviewer responses.
Managing a full meta-analysis project requires standard directory scaffolding to ensure consistency. This pipeline coordinates a numbered project structure that predictably outputs standardized artifacts and tracks progress transparently.
Yes, the end-to-end meta-analysis pipeline enables both Python and R tooling. It coordinates the environment to run statistical analysis and data extraction while relying on the bibtexparser dependency for references.
Yes, you can generate reviewer responses automatically. The workflow orchestrates the pipeline past manuscript rendering to produce final reviewer notes and inquiries, ensuring a complete end-to-end production-grade output.
You need a TOPIC.txt file at the project root to start the meta-analysis automation process. This topic-driven approach triggers the full workflow, moving from protocol creation through searches to the final paper.
The pipeline is limited to its defined 9-stage structure and standardized directory layout. Users needing custom workflow stages or non-standard artifact formats may find the predictable directory constraints too rigid for alternative project management approaches.
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