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-pipeline
+# Tests: literature-review-with-traceable-ai-evidence
 
 ## Test Checklist
 

skills/text-concatenation-pipeline/SKILL.md

@@ -1,60 +0,0 @@
----
-name: text-concatenation-pipeline
-version: 1.0.0
-description: Combine two text inputs into a single output string using a Blacknode
-  node graph.
-inputs:
-- text_a (string)
-- text_b (string)
-steps:
-- Instantiate a Text node with parameter value set to text_a
-- Instantiate a Text node with parameter value set to text_b
-- Instantiate a Concat node with no parameters
-- Instantiate an Output node
-- Connect output port 'value' of first Text node to input port 'a' of Concat node
-- Connect output port 'value' of second Text node to input port 'b' of Concat node
-- Connect output port 'value' of Concat node to input port 'value' of Output node
-- Execute graph by cooking the Output node's 'value' port to obtain result
-outputs:
-- concatenated_text (string)
-tags: []
-metadata:
-  source_repo: https://github.com/temiroff/Blacknode.git
-  extracted_at: ''
-  confidence: 0.95
----
-
-# text-concatenation-pipeline
-
-Combine two text inputs into a single output string using a Blacknode node graph.
-
-## Steps
-
-1. Instantiate a Text node with parameter value set to text_a
-2. Instantiate a Text node with parameter value set to text_b
-3. Instantiate a Concat node with no parameters
-4. Instantiate an Output node
-5. Connect output port 'value' of first Text node to input port 'a' of Concat node
-6. Connect output port 'value' of second Text node to input port 'b' of Concat node
-7. Connect output port 'value' of Concat node to input port 'value' of Output node
-8. Execute graph by cooking the Output node's 'value' port to obtain result
-
-## Inputs
-
-- text_a (string)
-- text_b (string)
-
-## Outputs
-
-- concatenated_text (string)
-
-## Failure Modes
-
-- One or both text inputs missing or non-string
-- Invalid node connections (port mismatch)
-- Runtime error during graph execution
-
-## Source
-
-Extracted from: [https://github.com/temiroff/Blacknode.git](https://github.com/temiroff/Blacknode.git)
-Confidence: 0.95

skills/text-concatenation-pipeline/commands.md

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

skills/text-concatenation-pipeline/examples.md

@@ -1,10 +0,0 @@
-# Examples: text-concatenation-pipeline
-
-## Usage Example
-
-```python
-# How to use this skill
-# Inputs: text_a (string), text_b (string)
-# Process: Instantiate a Text node with parameter value set to text_a → Instantiate a Text node with parameter value set to text_b → Instantiate a Concat node with no parameters
-# Outputs: concatenated_text (string)
-```

skills/text-concatenation-pipeline/metadata.json

@@ -1,31 +0,0 @@
-{
-  "name": "text-concatenation-pipeline",
-  "version": "1.0.0",
-  "goal": "Combine two text inputs into a single output string using a Blacknode node graph.",
-  "inputs": [
-    "text_a (string)",
-    "text_b (string)"
-  ],
-  "steps": [
-    "Instantiate a Text node with parameter value set to text_a",
-    "Instantiate a Text node with parameter value set to text_b",
-    "Instantiate a Concat node with no parameters",
-    "Instantiate an Output node",
-    "Connect output port 'value' of first Text node to input port 'a' of Concat node",
-    "Connect output port 'value' of second Text node to input port 'b' of Concat node",
-    "Connect output port 'value' of Concat node to input port 'value' of Output node",
-    "Execute graph by cooking the Output node's 'value' port to obtain result"
-  ],
-  "outputs": [
-    "concatenated_text (string)"
-  ],
-  "failure_modes": [
-    "One or both text inputs missing or non-string",
-    "Invalid node connections (port mismatch)",
-    "Runtime error during graph execution"
-  ],
-  "confidence": 0.95,
-  "explanation": "Extracted from examples/converted_text_pipeline.py and referenced templates/text-pipeline.json in the Blacknode repository. The workflow is a basic reusable pattern for string concatenation using the visual node editor's graph model.",
-  "source_repo": "https://github.com/temiroff/Blacknode.git",
-  "score": 1.0
-}