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

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

skills/graph-based-node-workflow/SKILL.md

@@ -1,100 +0,0 @@
----
-name: graph-based-node-workflow
-version: 1.0.0
-description: Create and execute typed node graphs for AI/robotics workflows by defining
-  nodes with inputs/outputs and connecting them with edges, then cooking the graph
-  to run the workflow.
-inputs:
-- bn.Graph() - the graph container for the workflow
-- Node definitions (Literal, Text, LLMAgent, Concat, Output, etc.) with specified
-  inputs and outputs
-- Edge connections mapping from_port to to_port between nodes
-steps:
-- 'Step 1: Initialize a bn.Graph() instance to serve as the workflow container'
-- 'Step 2: Define individual nodes with their required inputs and outputs (e.g., Literal
-  for data, LLMAgent for inference, Concat for combining, Output for final results)'
-- 'Step 3: Create edges connecting nodes by specifying source from_port and destination
-  to_port for each data flow'
-- 'Step 4: Execute the graph by calling g.cook() to process the defined workflow and
-  produce results'
-outputs:
-- Executed workflow results stored in the graph's output nodes
-- Cooked graph ready for inspection, replay, or deployment
-- Potential error states if node dependencies are missing or ports don't match
-tags: []
-metadata:
-  source_repo: https://github.com/temiroff/Blacknode.git
-  extracted_at: ''
-  confidence: 0.95
----
-
-# graph-based-node-workflow
-
-Create and execute typed node graphs for AI/robotics workflows by defining nodes with inputs/outputs and connecting them with edges, then cooking the graph to run the workflow.
-
-## Setup
-
-**Dependencies:**
-
-```text
-pip install blacknode (core Python package) anthropic, openai, docker, petgraph (dependencies) Rust extensions in blacknode-core, blacknode-runtime (optional)
-```
-
-**Setup steps:**
-
-1. Install blacknode with Python 3.11+ and required dependencies
-1. Clone repository and navigate to project directory
-1. Run examples/converted_text_pipeline.py to see basic graph execution
-1. Modify node definitions and edges to create custom workflows
-
-## Key Files
-
-- `examples/converted_text_pipeline.py - basic pipeline pattern`
-- `examples/hello_agent.py - LLM agent workflow pattern`
-- `examples/research_pipeline.py - multi-node research workflow`
-- `blacknode.py - main CLI entry point`
-
-## Steps
-
-1. Step 1: Initialize a bn.Graph() instance to serve as the workflow container
-2. Step 2: Define individual nodes with their required inputs and outputs (e.g., Literal for data, LLMAgent for inference, Concat for combining, Output for final results)
-3. Step 3: Create edges connecting nodes by specifying source from_port and destination to_port for each data flow
-4. Step 4: Execute the graph by calling g.cook() to process the defined workflow and produce results
-
-## Implementation Details
-
-```python
-g = bn.Graph()
-```
-
-```python
-g._edges = [{"from": "model", "from_port": "value", "to": "agent", "to_port": "model"}]
-```
-
-```python
-result = g.cook(output, "value")
-```
-
-## Inputs
-
-- bn.Graph() - the graph container for the workflow
-- Node definitions (Literal, Text, LLMAgent, Concat, Output, etc.) with specified inputs and outputs
-- Edge connections mapping from_port to to_port between nodes
-
-## Outputs
-
-- Executed workflow results stored in the graph's output nodes
-- Cooked graph ready for inspection, replay, or deployment
-- Potential error states if node dependencies are missing or ports don't match
-
-## Failure Modes
-
-- Missing node dependencies causing undefined variable errors
-- Port mismatch in edge connections leading to no data flow
-- Incomplete graph definition causing cook() to fail
-- Model API key missing or invalid for LLMAgent nodes
-
-## Source
-
-Extracted from: [https://github.com/temiroff/Blacknode.git](https://github.com/temiroff/Blacknode.git)
-Confidence: 0.95

skills/graph-based-node-workflow/commands.md

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

skills/graph-based-node-workflow/examples.md

@@ -1,10 +0,0 @@
-# Examples: graph-based-node-workflow
-
-## Usage Example
-
-```python
-# How to use this skill
-# Inputs: bn.Graph() - the graph container for the workflow, Node definitions (Literal, Text, LLMAgent, Concat, Output, etc.) with specified inputs and outputs, Edge connections mapping from_port to to_port between nodes
-# Process: Step 1: Initialize a bn.Graph() instance to serve as the workflow container → Step 2: Define individual nodes with their required inputs and outputs (e.g., Literal for data, LLMAgent for inference, Concat for combining, Output for final results) → Step 3: Create edges connecting nodes by specifying source from_port and destination to_port for each data flow
-# Outputs: Executed workflow results stored in the graph's output nodes, Cooked graph ready for inspection, replay, or deployment, Potential error states if node dependencies are missing or ports don't match
-```

skills/graph-based-node-workflow/metadata.json

@@ -1,31 +0,0 @@
-{
-  "name": "graph-based-node-workflow",
-  "version": "1.0.0",
-  "goal": "Create and execute typed node graphs for AI/robotics workflows by defining nodes with inputs/outputs and connecting them with edges, then cooking the graph to run the workflow.",
-  "inputs": [
-    "bn.Graph() - the graph container for the workflow",
-    "Node definitions (Literal, Text, LLMAgent, Concat, Output, etc.) with specified inputs and outputs",
-    "Edge connections mapping from_port to to_port between nodes"
-  ],
-  "steps": [
-    "Step 1: Initialize a bn.Graph() instance to serve as the workflow container",
-    "Step 2: Define individual nodes with their required inputs and outputs (e.g., Literal for data, LLMAgent for inference, Concat for combining, Output for final results)",
-    "Step 3: Create edges connecting nodes by specifying source from_port and destination to_port for each data flow",
-    "Step 4: Execute the graph by calling g.cook() to process the defined workflow and produce results"
-  ],
-  "outputs": [
-    "Executed workflow results stored in the graph's output nodes",
-    "Cooked graph ready for inspection, replay, or deployment",
-    "Potential error states if node dependencies are missing or ports don't match"
-  ],
-  "failure_modes": [
-    "Missing node dependencies causing undefined variable errors",
-    "Port mismatch in edge connections leading to no data flow",
-    "Incomplete graph definition causing cook() to fail",
-    "Model API key missing or invalid for LLMAgent nodes"
-  ],
-  "confidence": 0.95,
-  "explanation": "This workflow pattern is reusable across different AI/robotics applications because it provides a standardized way to compose complex pipelines from typed nodes. The pattern can be adapted to various use cases like research pipelines, agent workflows, or robotics control graphs by simply adding/removing nodes and edges while maintaining the same graph-cooking execution model.",
-  "source_repo": "https://github.com/temiroff/Blacknode.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: graph-based-node-workflow
+# Tests: literature-review-with-traceable-ai-evidence
 
 ## Test Checklist