paper-ppt-deep-research

name	paper-ppt-deep-research
description	Deep paper analysis for Paper PPT Agent decks. Use when Agent mode has deep research enabled or when a long/technical paper needs focused reading passes, SubAgent/task decomposition, evidence extraction, limitation analysis, and a slide-ready synthesis before manuscript and SVG generation.

Paper PPT Deep Research

Overview

Use this skill after reading agent_task.json and the extracted paper assets. It organizes the uploaded paper into focused research passes so the final deck is faithful, detailed, and coherent.

Workflow

1. Read source_assets/paper.md and source_assets/figures.json or figures.md.
2. Create research/deep/plan.md with focused passes. Typical passes:
   • problem, motivation, and research gap
   • method/architecture and algorithm details
   • data, experiments, metrics, baselines, and ablations
   • figures/tables/equations worth showing
   • limitations, assumptions, failure modes, and implications
   • slide narrative and audience framing
3. If the runtime provides Task/SubAgent tools, assign focused readers when deep research is enabled. Use separate readers for the paper's background/related work, method, experiments, and critique when the paper is complex. Skip only when the tool is unavailable, fails, or the paper is too short/simple for meaningful decomposition; record the concrete reason in agent_report.json.subagents.
4. Store pass notes under research/deep/notes/. Keep notes factual, with section/page/figure anchors where available.
5. Run scripts/compile_deep_notes.py to write research/deep/notes_index.json, even when a failed/unavailable SubAgent leaves the index empty and the limitation must be described.
6. Write research/deep/brief.md with slide-ready synthesis and conflicts/uncertainties.

When this skill is enabled, the backend blocks manuscript.md, design_spec.md, notes, agent_report.json, and slide SVG authoring until research/deep/notes_index.json and research/deep/brief.md exist. Merge the synthesis into manuscript.md; do not paste independent reader styles into the deck.

Script

Use the Python interpreter from agent_task.json.paths.python or PAPER_PPT_PYTHON.

"<python>" skills/paper-ppt-deep-research/scripts/compile_deep_notes.py \
  --notes-dir research/deep/notes \
  --out research/deep/notes_index.json

The script only indexes notes you or SubAgents already wrote. It must not replace paper reading or decide what matters.

Quality Rules

• Tie findings back to the uploaded paper. Prefer concrete paper facts, metrics, figures, tables, equations, and named components.
• Separate what the paper proves from your interpretation or external context.
• Record SubAgent usage or skip reasons in agent_report.json.subagents.
• Keep the final deck narrative unified; the main Agent owns synthesis and style consistency.