agent-skills/skills/literature-review-with-traceable-ai-evidence/SKILL.md

name, version, description, inputs, steps, outputs, tags, metadata

name	version	description	inputs	steps	outputs	tags	metadata
literature-review-with-traceable-ai-evidence	1.0.0	Enable researchers to ingest documents, asynchronously index them, and interact with multi-agent AI to answer questions with verifiable citations to original text.	Documents in PDF, Office, image, or text formats Research questions or topics of interest Optional: user model configuration via .env or settings	Upload documents to a project (via desktop app or web UI) System asynchronously converts Office docs to PDF if needed, runs OCR to extract text with coordinates, chunks and embeds into vector store User starts a main research session or creates exploration branches without waiting for indexing to finish Leader agent receives query and delegates subtasks to researcher, reviewer, writer subagents Subagents perform hybrid retrieval and rerank to find relevant chunks with source coordinates Agents synthesize answers and return evidence with clickable citations that highlight original pages User verifies conclusions by navigating to cited source locations and can save notes to research memory or mind map	AI-generated answers with traceable evidence (coordinates, page highlights) Research session history with branches Indexed document library for future queries Mind maps or structured notes Persistent run events for resuming sessions		source_repo extracted_at confidence https://github.com/0verL1nk/PaperSage.git 0.85

literature-review-with-traceable-ai-evidence

Enable researchers to ingest documents, asynchronously index them, and interact with multi-agent AI to answer questions with verifiable citations to original text.

Steps

1. Upload documents to a project (via desktop app or web UI)
2. System asynchronously converts Office docs to PDF if needed, runs OCR to extract text with coordinates, chunks and embeds into vector store
3. User starts a main research session or creates exploration branches without waiting for indexing to finish
4. Leader agent receives query and delegates subtasks to researcher, reviewer, writer subagents
5. Subagents perform hybrid retrieval and rerank to find relevant chunks with source coordinates
6. Agents synthesize answers and return evidence with clickable citations that highlight original pages
7. User verifies conclusions by navigating to cited source locations and can save notes to research memory or mind map

Inputs

• Documents in PDF, Office, image, or text formats
• Research questions or topics of interest
• Optional: user model configuration via .env or settings

Outputs

• AI-generated answers with traceable evidence (coordinates, page highlights)
• Research session history with branches
• Indexed document library for future queries
• Mind maps or structured notes
• Persistent run events for resuming sessions

Failure Modes

• Missing local Office/LibreOffice converter causes document conversion failure
• First-time model download may be slow or require network
• OCR may have low confidence on poor quality scans
• Retrieval might miss context if chunking splits semantics
• Multi-agent coordination could produce conflicting intermediate results

Source

Extracted from: https://github.com/0verL1nk/PaperSage.git Confidence: 0.85