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Add Skill: local-document-research-with-traceable-citations

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Extracted from: https://github.com/0verL1nk/PaperSage.git
Score: 1.0
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---
name: local-document-research-with-traceable-citations
version: 1.0.0
description: Enable users to import local documents, asynchronously process them into
an indexed knowledge base, and obtain AI-generated answers that cite specific page
locations and OCR evidence.
inputs:
- Local document files (PDF, DOCX, PPTX, XLSX, images, TXT)
- Configured LLM API endpoint and keys (via .env or settings)
- Optional web search service config if enabled
- Local OCR model cache (downloaded on first use)
steps:
- Import documents into a project; files are queued for asynchronous processing.
- Convert non-PDF formats (DOCX, PPTX, XLSX) to PDF using native Office or LibreOffice
fallback.
- Run OCR (PaddleOCR) on PDF pages/images to extract text, page numbers, polygons,
and confidence scores.
- Chunk text and generate embeddings; publish to LanceDB hybrid index (dense vector
+ full-text) only when fully processed.
- User starts a research session or branch; Leader agent analyzes query.
- Hybrid RAG retrieves candidate chunks from ready documents; dynamic material scope
ensures no half-indexed docs.
- Leader delegates tasks to sub-agents (researcher, reviewer, writer) via constrained
task capability; each delegation logs start, completion, duration, and evidence.
- Generate answer that references only actually used evidence; citations include document
ID, page, coordinates.
- User clicks citation to open original document and view highlighted OCR location.
outputs:
- Project with indexed document library (SQLite metadata + LanceDB vectors)
- AI answers with verifiable citations to source pages
- Evidence preview with page image and OCR highlight polygons
- Persistent session history, branches, and long-term memory
tags: []
metadata:
source_repo: https://github.com/0verL1nk/PaperSage.git
extracted_at: ''
confidence: 0.85
---
# local-document-research-with-traceable-citations
Enable users to import local documents, asynchronously process them into an indexed knowledge base, and obtain AI-generated answers that cite specific page locations and OCR evidence.
## Steps
1. Import documents into a project; files are queued for asynchronous processing.
2. Convert non-PDF formats (DOCX, PPTX, XLSX) to PDF using native Office or LibreOffice fallback.
3. Run OCR (PaddleOCR) on PDF pages/images to extract text, page numbers, polygons, and confidence scores.
4. Chunk text and generate embeddings; publish to LanceDB hybrid index (dense vector + full-text) only when fully processed.
5. User starts a research session or branch; Leader agent analyzes query.
6. Hybrid RAG retrieves candidate chunks from ready documents; dynamic material scope ensures no half-indexed docs.
7. Leader delegates tasks to sub-agents (researcher, reviewer, writer) via constrained task capability; each delegation logs start, completion, duration, and evidence.
8. Generate answer that references only actually used evidence; citations include document ID, page, coordinates.
9. User clicks citation to open original document and view highlighted OCR location.
## Inputs
- Local document files (PDF, DOCX, PPTX, XLSX, images, TXT)
- Configured LLM API endpoint and keys (via .env or settings)
- Optional web search service config if enabled
- Local OCR model cache (downloaded on first use)
## Outputs
- Project with indexed document library (SQLite metadata + LanceDB vectors)
- AI answers with verifiable citations to source pages
- Evidence preview with page image and OCR highlight polygons
- Persistent session history, branches, and long-term memory
## Failure Modes
- OCR quality low for scanned images leading to poor extraction
- Missing Office/LibreOffice causes conversion failure for Office docs
- Interrupted indexing leaves documents unpublished and excluded from retrieval
- LLM API outage or misconfiguration yields no answer
- Citation coordinates mismatch due to chunk drift
- Sub-agent recursion if constraints not enforced
## Source
Extracted from: [https://github.com/0verL1nk/PaperSage.git](https://github.com/0verL1nk/PaperSage.git)
Confidence: 0.85