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Paper Ppt Deep Research

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paper-ppt-deep-research

Paper Ppt Deep Research is an Agent Skill for documents published in the CRui5in/paper-ppt-agent repository on GitHub: a SKILL.md instruction file an AI agent can load to do this task.

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.

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How to use this skill

Bring this guide into your coding agent with a prompt tailored to the tool you use.

  1. Open your project in Codex.
  2. Copy and paste the prompt below.
  3. Review files and risks before approving any action.
Prompt to paste
Inspect the Agent Skill at https://github.com/CRui5in/paper-ppt-agent/blob/HEAD/assets/agent_skills/paper-ppt-deep-research/SKILL.md and all supporting files. Report provenance, permissions, scripts, dependencies, and security risks. Propose an exact installation into .agents/skills/paper-ppt-deep-research, but wait for my approval before writing files or running scripts.

Copying this prompt does not install or run the skill. Review third-party files before use.

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.