Debrief: An AI Presentation Assistant for Researchers

Debrief is an AI presentation assistant designed for researchers, existing as a Claude Code-based multi-agent plugin. Its core goal is to solve the common pain point of researchers spending a lot of time converting complex research content into clear, aesthetic slides. It enables fully automatic conversion from raw research materials (papers, data, narratives) to finished slides via multi-agent workflows. Key keywords: research tool, presentation, Claude Code, multi-agent, academic report, automation.

The project targets a key challenge for researchers: preparing academic reports requires significant time to transform complex content (research papers, experimental data, unstructured narratives) into well-structured, visually appealing slides. Debrief automates this process to help researchers save time for core research work.

Debrief's workflow relies on multi-agent collaboration and multi-source input support:

Multi-source input: Handles PDF papers (extracts key findings/methods), experimental data (tables/charts for visualization), and unstructured narratives (refines core points).

Multi-agent architecture:

• Content Understanding Agent: Parses input materials to identify research questions, methods, results, conclusions.
• Structure Design Agent: Plans presentation structure (chapters, slide info density, logical progression).
• Visual Design Agent: Converts text to visual layouts (chart types, color schemes, font hierarchy per academic norms).
• Content Generation Agent: Refines raw content into concise, presentation-friendly text (adapting from detailed academic writing to focused slides).

Debrief's technical highlights:

1. Claude Code Plugin Architecture: Leverages Claude's strong understanding/generation capabilities, integrating seamlessly into familiar developer environments.
2. Prompt Engineering Optimization: Each agent uses carefully tuned prompt templates for stable, accurate task completion.
3. Customizability: Allows users to adjust presentation duration (info density), target audience (expert vs general), style preferences (formal vs casual), and template choices (per discipline conventions).

Debrief applies to various research scenarios:

• Academic Conferences: Generates draft slides from full papers, requiring minimal user modification.
• Group Meetings: Helps grad students quickly organize work progress into clear reports for advisors/peers.
• Project Defense: Creates defense slides from grant proposals to showcase research plans/expected outcomes.
• Teaching Materials: Adjusts content depth/breadth to turn research results into high-quality courseware for different levels.

Value:

• Respects Research Essence: Assists rather than replaces researchers' thinking, aligning with academic needs.
• Balances Efficiency & Quality: Multi-agent system plus quality checks (logical coherence, info completeness, visual consistency) ensure fast yet professional outputs.
• Transparency & Control: Users can view agent results and intervene at key steps.

Limitations:

• Domain Specificity: Needs tuning for different disciplines to optimize results.
• Creativity Constraints: AI-generated slides may be conservative; manual design is better for highly creative needs.
• Privacy Concerns: Users should consider data privacy when processing unpublished research (local run options are available but need attention).

Debrief represents a promising direction for AI-assisted research tools. Its multi-agent architecture decomposes complex creative tasks into manageable subtasks, boosting efficiency while maintaining quality. For time-strapped researchers with frequent presentation needs, it is highly valuable. Future expectations include more domain-specific optimizations and similar AI tools tailored to research scenarios.