Section Writing Agent
This is the fourth step in the PaperOrchestra pipeline that generates a complete LaTeX manuscript in a single multimodal LLM call. It drafts the methodology, experiments, abstract, and conclusion sections, parses numeric results from experimental logs into booktabs tables, embeds figures generated in step 2, and merges everything into the template that already has your intro and related work. The implementation is careful about preserving existing content verbatim and includes deterministic gates to catch orphan citations and LaTeX syntax errors before passing to the refinement stage. It reads a TeX profile to know which packages are available so it generates compatible cross-references and formatting patterns. The single-call design is intentional for maintaining coherence across sections, and it uses vision input to ground figure references in what the images actually show.
- GitHub stars
- 572
- last updated
- Jun 2, 2026
Install
$ npx -y skills add ar9av/paperorchestra --skill section-writing-agent --agent claude-codeInstalls into .claude/skills of the current project.
Files
Section Writing Agent (Step 4)
Faithful implementation of the Section Writing Agent from PaperOrchestra (Song et al., 2026, arXiv:2604.05018, §4 Step 4, App. F.1 pp. 47–49).
Cost: ONE LLM call (App. B: "Section Writing Agent (1 call): A single, comprehensive multimodal call to draft and compile the complete LaTeX manuscript"). Do NOT split this into per-section calls — the paper explicitly designs it as one comprehensive call so the model can maintain global coherence across sections.
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- Repository
- github.com/ar9av/paperorchestra
- Categories
- Backend & APIs, DevOps & CI/CD, Documentation, AI & Agent Building, Data Science & ML, Automation & Workflows, Sales & Marketing
- First seen
- Jun 11, 2026
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