Add Skill: literature-review-with-traceable-ai-evidence

Extracted from: https://github.com/0verL1nk/PaperSage.git
Score: 1.0

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

@@ -0,0 +1,77 @@
+---
+name: literature-review-with-traceable-ai-evidence
+version: 1.0.0
+description: Enable researchers to ingest documents, asynchronously index them, and
+  interact with multi-agent AI to answer questions with verifiable citations to original
+  text.
+inputs:
+- Documents in PDF, Office, image, or text formats
+- Research questions or topics of interest
+- 'Optional: user model configuration via .env or settings'
+steps:
+- 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
+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
+tags: []
+metadata:
+  source_repo: https://github.com/0verL1nk/PaperSage.git
+  extracted_at: ''
+  confidence: 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](https://github.com/0verL1nk/PaperSage.git)
+Confidence: 0.85

skills/literature-review-with-traceable-ai-evidence/commands.md

@@ -0,0 +1,6 @@
+# Commands: literature-review-with-traceable-ai-evidence
+
+## Available Commands
+
+- `/skill literature-review-with-traceable-ai-evidence` — Load this skill
+- `/run literature-review-with-traceable-ai-evidence` — Execute workflow

skills/literature-review-with-traceable-ai-evidence/examples.md

@@ -0,0 +1,10 @@
+# Examples: literature-review-with-traceable-ai-evidence
+
+## Usage Example
+
+```python
+# How to use this skill
+# Inputs: Documents in PDF, Office, image, or text formats, Research questions or topics of interest, Optional: user model configuration via .env or settings
+# Process: 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
+# 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
+```

skills/literature-review-with-traceable-ai-evidence/metadata.json

@@ -0,0 +1,37 @@
+{
+  "name": "literature-review-with-traceable-ai-evidence",
+  "version": "1.0.0",
+  "goal": "Enable researchers to ingest documents, asynchronously index them, and interact with multi-agent AI to answer questions with verifiable citations to original text.",
+  "inputs": [
+    "Documents in PDF, Office, image, or text formats",
+    "Research questions or topics of interest",
+    "Optional: user model configuration via .env or settings"
+  ],
+  "steps": [
+    "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"
+  ],
+  "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"
+  ],
+  "confidence": 0.85,
+  "explanation": "The README describes PaperSage's core workflow: asynchronous document ingestion with OCR/indexing, followed by multi-agent question answering with cited evidence. This process is not tied to the specific codebase and can be reused as a general literature review methodology for any document-centric research using RAG and agent collaboration.",
+  "source_repo": "https://github.com/0verL1nk/PaperSage.git",
+  "score": 1.0
+}

skills/literature-review-with-traceable-ai-evidence/tests.md

@@ -1,4 +1,4 @@
-# Tests: text-concatenation-workflow
+# Tests: literature-review-with-traceable-ai-evidence
 
 ## Test Checklist
 

skills/text-concatenation-workflow/SKILL.md

@@ -1,59 +0,0 @@
----
-name: text-concatenation-workflow
-version: 1.0.0
-description: Concatenate two text strings and produce the combined output.
-inputs:
-- text_a (string)
-- text_b (string)
-steps:
-- Create a Text node with value set to input text_a.
-- Create a second Text node with value set to input text_b.
-- Create a Concat node with inputs a and b.
-- Create an Output node.
-- Connect Text node a 'value' port to Concat node 'a' port.
-- Connect Text node b 'value' port to Concat node 'b' port.
-- Connect Concat node 'value' port to Output node 'value' port.
-- Execute/cook the graph from the Output node to obtain the result.
-outputs:
-- concatenated_text (string)
-tags: []
-metadata:
-  source_repo: https://github.com/temiroff/Blacknode.git
-  extracted_at: ''
-  confidence: 0.95
----
-
-# text-concatenation-workflow
-
-Concatenate two text strings and produce the combined output.
-
-## Steps
-
-1. Create a Text node with value set to input text_a.
-2. Create a second Text node with value set to input text_b.
-3. Create a Concat node with inputs a and b.
-4. Create an Output node.
-5. Connect Text node a 'value' port to Concat node 'a' port.
-6. Connect Text node b 'value' port to Concat node 'b' port.
-7. Connect Concat node 'value' port to Output node 'value' port.
-8. Execute/cook the graph from the Output node to obtain the result.
-
-## Inputs
-
-- text_a (string)
-- text_b (string)
-
-## Outputs
-
-- concatenated_text (string)
-
-## Failure Modes
-
-- Missing or invalid text inputs.
-- Graph execution error if nodes are not properly connected.
-- Concat node may not handle non-string types.
-
-## Source
-
-Extracted from: [https://github.com/temiroff/Blacknode.git](https://github.com/temiroff/Blacknode.git)
-Confidence: 0.95

skills/text-concatenation-workflow/commands.md

@@ -1,6 +0,0 @@
-# Commands: text-concatenation-workflow
-
-## Available Commands
-
-- `/skill text-concatenation-workflow` — Load this skill
-- `/run text-concatenation-workflow` — Execute workflow

skills/text-concatenation-workflow/examples.md

@@ -1,10 +0,0 @@
-# Examples: text-concatenation-workflow
-
-## Usage Example
-
-```python
-# How to use this skill
-# Inputs: text_a (string), text_b (string)
-# Process: Create a Text node with value set to input text_a. → Create a second Text node with value set to input text_b. → Create a Concat node with inputs a and b.
-# Outputs: concatenated_text (string)
-```

skills/text-concatenation-workflow/metadata.json

@@ -1,31 +0,0 @@
-{
-  "name": "text-concatenation-workflow",
-  "version": "1.0.0",
-  "goal": "Concatenate two text strings and produce the combined output.",
-  "inputs": [
-    "text_a (string)",
-    "text_b (string)"
-  ],
-  "steps": [
-    "Create a Text node with value set to input text_a.",
-    "Create a second Text node with value set to input text_b.",
-    "Create a Concat node with inputs a and b.",
-    "Create an Output node.",
-    "Connect Text node a 'value' port to Concat node 'a' port.",
-    "Connect Text node b 'value' port to Concat node 'b' port.",
-    "Connect Concat node 'value' port to Output node 'value' port.",
-    "Execute/cook the graph from the Output node to obtain the result."
-  ],
-  "outputs": [
-    "concatenated_text (string)"
-  ],
-  "failure_modes": [
-    "Missing or invalid text inputs.",
-    "Graph execution error if nodes are not properly connected.",
-    "Concat node may not handle non-string types."
-  ],
-  "confidence": 0.95,
-  "explanation": "Extracted from examples/converted_text_pipeline.py which demonstrates a simple Blacknode graph workflow: two Text nodes feed a Concat node that outputs via an Output node. This pattern is reusable for any text combination task.",
-  "source_repo": "https://github.com/temiroff/Blacknode.git",
-  "score": 1.0
-}