ppw:repo-to-paper

To generate an academic paper outline from a Python ML repository, provide the repository path to initiate a guided workflow that scans files and creates a hierarchical H1–H3 structure with staged user approvals. The system formats the output using CEUS or IMRaD standards.

Yes, you can automatically collect literature citations for your machine learning experiment code. The workflow integrates with Semantic Scholar MCP to gather and attach relevant references to support each subsection of your generated outline.

Yes, the repo-to-paper workflow supports bilingual academic writing output. When enabled, the system generates body text and outlines in two languages, providing bilingual annotations and evidence extraction directly from your repository files.

The best way to structure a research paper from existing code using IMRaD formatting is to use an automated extraction tool that scans your repository, proposes a hierarchical outline with user-approved checkpoints, and maps content to IMRaD sections.

To annotate evidence from repository files during academic paper generation, the system scans your Python ML experiment repository, extracts relevant content, and links specific code or data evidence directly to subsections within your CEUS or IMRaD formatted outline.