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

Extracted from: https://github.com/0verL1nk/PaperSage.git Score: 1.0
Add 5 skills from LFM + 12 skills total
2026-08-08 23:04:46 +00:00 · 2026-08-05 17:05:21 +00:00
26 changed files with 792 additions and 3 deletions
@@ -16,10 +16,10 @@ llm:
  api_key: ""
  max_tokens: 8000
-# Secondary LLM for pipeline tasks — uses Ollama on 3060 (non-reasoning model)
+# Secondary LLM for pipeline tasks — uses LFM on 3060 (llama.cpp)
 llm_pipeline:
-  base_url: http://100.64.0.4:11434
+  base_url: http://100.64.0.4:8080
-  model: qwen2.5:7b
+  model: C:\models\LFM2.5-2.6B-Q4_K_M.gguf
  api_key: ""
  max_tokens: 6000
@@ -0,0 +1,96 @@
 ---
 name: blacknode-graph-workflow
 version: 1.0.0
 description: Build and execute node-based AI workflows with LLM agents and processing
  nodes
 inputs:
 - Model configuration (NIM_MODEL, API keys for NVIDIA NIM, OpenAI, Anthropic)
 - Node definitions with specified inputs and outputs (Literal, LLMAgent, FileWrite,
  etc.)
 - Data sources (URLs, text content, or other inputs for the workflow)
 steps:
 - Initialize a blacknode.Graph instance to create the workflow structure
 - Add nodes to the graph with defined inputs and outputs (e.g., Literal, LLMAgent,
  FileWrite)
 - Define edges connecting nodes to establish data flow between them
 - Execute the graph using cook() to run the workflow and generate outputs
 outputs:
 - Processed results from the final node (e.g., printed text, written files, or generated
  data)
 - Graph execution status and any errors encountered during execution
 tags: []
 metadata:
  source_repo: https://github.com/temiroff/Blacknode.git
  extracted_at: ''
  confidence: 0.95
 ---
 # blacknode-graph-workflow
 Build and execute node-based AI workflows with LLM agents and processing nodes
 ## Setup
 **Dependencies:**
 ```text
 pip install blacknode (core package) anthropic>=0.25 openai>=1.0 petgraph (for graph operations)
 ```
 **Setup steps:**
 1. Install blacknode package: pip install blacknode
 1. Configure model API keys (NIM_API_KEY, OPENAI_API_KEY, etc.) in .env or editor
 1. Create a Graph instance and add nodes with inputs/outputs
 1. Define node connections in g._edges list
 1. Execute with g.cook() to run the workflow and capture results
 ## Key Files
 - `blacknode/blacknode.py (Graph class implementation)`
 - `examples/hello_agent.py (simple LLM agent workflow)`
 - `examples/converted_nvidia_nim.py (NIM model workflow)`
 ## Steps
 1. Initialize a blacknode.Graph instance to create the workflow structure
 2. Add nodes to the graph with defined inputs and outputs (e.g., Literal, LLMAgent, FileWrite)
 3. Define edges connecting nodes to establish data flow between them
 4. Execute the graph using cook() to run the workflow and generate outputs
 ## Implementation Details
 ```python
 g = bn.Graph()
 ```
 ```python
 g._edges = [{'from': 'model', 'from_port': 'value', 'to': 'agent', 'to_port': 'model'}]
 ```
 ```python
 result = g.cook(output_node, 'value')
 ```
 ## Inputs
 - Model configuration (NIM_MODEL, API keys for NVIDIA NIM, OpenAI, Anthropic)
 - Node definitions with specified inputs and outputs (Literal, LLMAgent, FileWrite, etc.)
 - Data sources (URLs, text content, or other inputs for the workflow)
 ## Outputs
 - Processed results from the final node (e.g., printed text, written files, or generated data)
 - Graph execution status and any errors encountered during execution
 ## Failure Modes
 - Missing or invalid model API key causing graph initialization failure
 - Incorrect node connections or missing edge definitions leading to runtime errors
 - Model not found or unavailable in the specified environment causing execution failure
 - Graph edges not properly defined or mismatched causing cook() to fail
 ## Source
 Extracted from: [https://github.com/temiroff/Blacknode.git](https://github.com/temiroff/Blacknode.git)
 Confidence: 0.95
@@ -0,0 +1,6 @@
 # Commands: blacknode-graph-workflow
 ## Available Commands
 - `/skill blacknode-graph-workflow` — Load this skill
 - `/run blacknode-graph-workflow` — Execute workflow
@@ -0,0 +1,10 @@
 # Examples: blacknode-graph-workflow
 ## Usage Example
 ```python
 # How to use this skill
 # Inputs: Model configuration (NIM_MODEL, API keys for NVIDIA NIM, OpenAI, Anthropic), Node definitions with specified inputs and outputs (Literal, LLMAgent, FileWrite, etc.), Data sources (URLs, text content, or other inputs for the workflow)
 # Process: Initialize a blacknode.Graph instance to create the workflow structure → Add nodes to the graph with defined inputs and outputs (e.g., Literal, LLMAgent, FileWrite) → Define edges connecting nodes to establish data flow between them
 # Outputs: Processed results from the final node (e.g., printed text, written files, or generated data), Graph execution status and any errors encountered during execution
 ```
@@ -0,0 +1,30 @@
 {
  "name": "blacknode-graph-workflow",
  "version": "1.0.0",
  "goal": "Build and execute node-based AI workflows with LLM agents and processing nodes",
  "inputs": [
    "Model configuration (NIM_MODEL, API keys for NVIDIA NIM, OpenAI, Anthropic)",
    "Node definitions with specified inputs and outputs (Literal, LLMAgent, FileWrite, etc.)",
    "Data sources (URLs, text content, or other inputs for the workflow)"
  ],
  "steps": [
    "Initialize a blacknode.Graph instance to create the workflow structure",
    "Add nodes to the graph with defined inputs and outputs (e.g., Literal, LLMAgent, FileWrite)",
    "Define edges connecting nodes to establish data flow between them",
    "Execute the graph using cook() to run the workflow and generate outputs"
  ],
  "outputs": [
    "Processed results from the final node (e.g., printed text, written files, or generated data)",
    "Graph execution status and any errors encountered during execution"
  ],
  "failure_modes": [
    "Missing or invalid model API key causing graph initialization failure",
    "Incorrect node connections or missing edge definitions leading to runtime errors",
    "Model not found or unavailable in the specified environment causing execution failure",
    "Graph edges not properly defined or mismatched causing cook() to fail"
  ],
  "confidence": 0.95,
  "explanation": "Blacknode provides a standardized Graph-based workflow pattern where users create node graphs using the blacknode.Graph class. This pattern is reusable across projects as it follows a consistent structure: initialize a graph, add nodes with defined inputs/outputs, connect them with edges, and execute with cook(). The examples demonstrate this pattern with LLM agents and text processing pipelines, making it adaptable to various robotics and AI workflows.",
  "source_repo": "https://github.com/temiroff/Blacknode.git",
  "score": 1.0
 }
@@ -0,0 +1,9 @@
 # Tests: blacknode-graph-workflow
 ## Test Checklist
 - [ ] Workflow has at least 3 steps
 - [ ] All inputs are defined
 - [ ] All outputs are defined
 - [ ] Failure modes are documented
 - [ ] Skill can be loaded without errors
@@ -0,0 +1,115 @@
 ---
 name: langgraph-agent-workflow
 version: 1.0.0
 description: Orchestrate multi-step AI agents using LangGraph with SerperDevTool for
  RAG, code execution, and citation generation
 inputs:
 - LangGraph chain configuration files defining agent workflows
 - SerperDevTool integration for LLM tool access
 - React agent creation scripts via create_react_agent
 - Knowledge graph retrieval and citation generation pipelines
 steps:
 - 'Step 1: Define LangGraph chain architecture with SerperDevTool integration - Create
  a LangGraph chain that combines retrieval, reasoning, and response generation using
  SerperDevTool for tool access'
 - 'Step 2: Implement React agent wrapper - Use create_react_agent to build a frontend
  agent that can interact with the LangGraph chain'
 - 'Step 3: Configure RAG pipeline - Set up vector search (Qdrant/OpenSearch) and knowledge
  graph retrieval (Neo4j/ArangoDB) with citation generation'
 - 'Step 4: Add code execution sandbox - Integrate artifact generation capabilities
  for code-related tasks'
 - "Step 5: Orchestrate multi-step research workflow - Chain search \u2192 deep research\
  \ \u2192 agent response in a single LangGraph workflow"
 outputs:
 - Reusable LangGraph chain definition (pyfile) with configurable steps
 - React agent frontend component that can be deployed independently
 - RAG pipeline that generates block citations and grounded answers
 - Code execution sandbox for artifact generation
 - Documentation for parameterizing workflows for different tasks
 tags: []
 metadata:
  source_repo: https://github.com/pipeshub-ai/pipeshub-ai.git
  extracted_at: ''
  confidence: 0.95
 ---
 # langgraph-agent-workflow
 Orchestrate multi-step AI agents using LangGraph with SerperDevTool for RAG, code execution, and citation generation
 ## Setup
 **Dependencies:**
 ```text
 pip install langgraph>=0.7.0 serper-dev-tool>=0.1.0 qdrant-client or opensearch-dsl neo4j-driver or arango-database-driver react, next.js
 ```
 **Setup steps:**
 1. Install LangGraph and SerperDevTool dependencies
 1. Configure vector store (Qdrant/OpenSearch) and knowledge graph (Neo4j/ArangoDB)
 1. Define chain topology with retrieval, reasoning, and response steps
 1. Build React agent frontend using create_react_agent
 1. Test multi-step agent workflows end-to-end
 ## Key Files
 - `pipeshub-ai/workflows/agent_chain.py - Main LangGraph chain definition`
 - `pipeshub-ai/workflows/agent_react.py - React agent wrapper`
 - `pipeshub-ai/workflows/rag_pipeline.py - RAG with citation generation`
 - `pipeshub-ai/workflows/code_sandbox.py - Code execution sandbox`
 ## Steps
 1. Step 1: Define LangGraph chain architecture with SerperDevTool integration - Create a LangGraph chain that combines retrieval, reasoning, and response generation using SerperDevTool for tool access
 2. Step 2: Implement React agent wrapper - Use create_react_agent to build a frontend agent that can interact with the LangGraph chain
 3. Step 3: Configure RAG pipeline - Set up vector search (Qdrant/OpenSearch) and knowledge graph retrieval (Neo4j/ArangoDB) with citation generation
 4. Step 4: Add code execution sandbox - Integrate artifact generation capabilities for code-related tasks
 5. Step 5: Orchestrate multi-step research workflow - Chain search → deep research → agent response in a single LangGraph workflow
 ## Implementation Details
 ```python
 chain = LangGraph()
 ```
 ```python
 chain.add_step(SerperDevToolAgent())
 ```
 ```python
 agent = create_react_agent(chain, SerperDevToolAgent())
 ```
 ```python
 workflow = chain.start()
 ```
 ## Inputs
 - LangGraph chain configuration files defining agent workflows
 - SerperDevTool integration for LLM tool access
 - React agent creation scripts via create_react_agent
 - Knowledge graph retrieval and citation generation pipelines
 ## Outputs
 - Reusable LangGraph chain definition (pyfile) with configurable steps
 - React agent frontend component that can be deployed independently
 - RAG pipeline that generates block citations and grounded answers
 - Code execution sandbox for artifact generation
 - Documentation for parameterizing workflows for different tasks
 ## Failure Modes
 - GraphDB connection failures if Neo4j/ArangoDB is not properly configured
 - Vector store unavailability (Qdrant/OpenSearch) causing RAG pipeline to fail
 - LLM tool access errors if SerperDevTool is not properly initialized
 - Agent timeout if complex multi-step reasoning exceeds time limits
 - Sandbox execution failures if code has security vulnerabilities or infinite loops
 ## Source
 Extracted from: [https://github.com/pipeshub-ai/pipeshub-ai.git](https://github.com/pipeshub-ai/pipeshub-ai.git)
 Confidence: 0.95
@@ -0,0 +1,6 @@
 # Commands: langgraph-agent-workflow
 ## Available Commands
 - `/skill langgraph-agent-workflow` — Load this skill
 - `/run langgraph-agent-workflow` — Execute workflow
@@ -0,0 +1,10 @@
 # Examples: langgraph-agent-workflow
 ## Usage Example
 ```python
 # How to use this skill
 # Inputs: LangGraph chain configuration files defining agent workflows, SerperDevTool integration for LLM tool access, React agent creation scripts via create_react_agent, Knowledge graph retrieval and citation generation pipelines
 # Process: Step 1: Define LangGraph chain architecture with SerperDevTool integration - Create a LangGraph chain that combines retrieval, reasoning, and response generation using SerperDevTool for tool access → Step 2: Implement React agent wrapper - Use create_react_agent to build a frontend agent that can interact with the LangGraph chain → Step 3: Configure RAG pipeline - Set up vector search (Qdrant/OpenSearch) and knowledge graph retrieval (Neo4j/ArangoDB) with citation generation
 # Outputs: Reusable LangGraph chain definition (pyfile) with configurable steps, React agent frontend component that can be deployed independently, RAG pipeline that generates block citations and grounded answers, Code execution sandbox for artifact generation, Documentation for parameterizing workflows for different tasks
 ```
@@ -0,0 +1,36 @@
 {
  "name": "langgraph-agent-workflow",
  "version": "1.0.0",
  "goal": "Orchestrate multi-step AI agents using LangGraph with SerperDevTool for RAG, code execution, and citation generation",
  "inputs": [
    "LangGraph chain configuration files defining agent workflows",
    "SerperDevTool integration for LLM tool access",
    "React agent creation scripts via create_react_agent",
    "Knowledge graph retrieval and citation generation pipelines"
  ],
  "steps": [
    "Step 1: Define LangGraph chain architecture with SerperDevTool integration - Create a LangGraph chain that combines retrieval, reasoning, and response generation using SerperDevTool for tool access",
    "Step 2: Implement React agent wrapper - Use create_react_agent to build a frontend agent that can interact with the LangGraph chain",
    "Step 3: Configure RAG pipeline - Set up vector search (Qdrant/OpenSearch) and knowledge graph retrieval (Neo4j/ArangoDB) with citation generation",
    "Step 4: Add code execution sandbox - Integrate artifact generation capabilities for code-related tasks",
    "Step 5: Orchestrate multi-step research workflow - Chain search \u2192 deep research \u2192 agent response in a single LangGraph workflow"
  ],
  "outputs": [
    "Reusable LangGraph chain definition (pyfile) with configurable steps",
    "React agent frontend component that can be deployed independently",
    "RAG pipeline that generates block citations and grounded answers",
    "Code execution sandbox for artifact generation",
    "Documentation for parameterizing workflows for different tasks"
  ],
  "failure_modes": [
    "GraphDB connection failures if Neo4j/ArangoDB is not properly configured",
    "Vector store unavailability (Qdrant/OpenSearch) causing RAG pipeline to fail",
    "LLM tool access errors if SerperDevTool is not properly initialized",
    "Agent timeout if complex multi-step reasoning exceeds time limits",
    "Sandbox execution failures if code has security vulnerabilities or infinite loops"
  ],
  "confidence": 0.95,
  "explanation": "PipesHub provides a reusable LangGraph-based agent workflow framework that can be parameterized for different tasks. The core pattern involves defining a LangGraph chain with SerperDevTool integration for tool access, creating a React agent wrapper, and configuring RAG pipelines with citation generation. This framework can be reused across RAG, code execution, and research workflows by adjusting the chain definition and agent configuration.",
  "source_repo": "https://github.com/pipeshub-ai/pipeshub-ai.git",
  "score": 1.0
 }
@@ -0,0 +1,9 @@
 # Tests: langgraph-agent-workflow
 ## Test Checklist
 - [ ] Workflow has at least 3 steps
 - [ ] All inputs are defined
 - [ ] All outputs are defined
 - [ ] Failure modes are documented
 - [ ] Skill can be loaded without errors
@@ -0,0 +1,96 @@
 ---
 name: langgraph-multi-agent-router
 version: 1.0.0
 description: Orchestrate a multi-agent workflow where specialized agents collaborate
  sequentially to gather information, structure it, and generate a final response
 inputs:
 - User query string (e.g., destination location)
 - BedrockModel configuration (model_id, temperature, top_p)
 - Pre-configured agents with specific system prompts and tool sets
 steps:
 - Researcher agent executes with system prompt to gather raw destination facts (places,
  history, accommodations, food, web pages) using BedrockModel and available tools
  (calculator, current_time)
 - Travel guide agent receives raw research output and structures it into labeled sections
  (Must-See Attractions, Historical Highlights, Accommodation Areas, Culinary Delights,
  Suggested Web Pages)
 - Writer agent receives the structured guide and synthesizes it into a professional
  client-facing response with clear formatting and emphasis on the suggested web pages
 outputs:
 - Raw research data (JSON string containing gathered facts)
 - Structured guide content (markdown-formatted travel guide with labeled sections)
 - Final client response (professional formatted response ready for delivery)
 tags: []
 metadata:
  source_repo: https://github.com/omerbsezer/Fast-LLM-Agent-MCP.git
  extracted_at: ''
  confidence: 0.95
 ---
 # langgraph-multi-agent-router
 Orchestrate a multi-agent workflow where specialized agents collaborate sequentially to gather information, structure it, and generate a final response
 ## Setup
 **Dependencies:**
 ```text
 pip install langchain langgraph bedrock-model pydantic
 ```
 **Setup steps:**
 1. Install langchain and langgraph packages
 1. Configure BedrockModel with desired parameters (model_id, temperature, top_p)
 1. Create three Agent instances with specific system prompts and tool sets
 1. Initialize LangGraph with the agent chain and run the workflow
 ## Key Files
 - `agents/langchain_langgraph/00-basic-agent/agent.py`
 - `agents/langchain_langgraph/02-agent-with-tools-structured-output/agent.py`
 - `agents/langchain_langgraph/08-langgraph-multi-agents-sequential-pattern/agent.py`
 ## Steps
 1. Researcher agent executes with system prompt to gather raw destination facts (places, history, accommodations, food, web pages) using BedrockModel and available tools (calculator, current_time)
 2. Travel guide agent receives raw research output and structures it into labeled sections (Must-See Attractions, Historical Highlights, Accommodation Areas, Culinary Delights, Suggested Web Pages)
 3. Writer agent receives the structured guide and synthesizes it into a professional client-facing response with clear formatting and emphasis on the suggested web pages
 ## Implementation Details
 ```python
 Researcher agent uses BedrockModel with temperature=0.7, top_p=0.9 to gather destination facts
 ```
 ```python
 Travel guide agent receives raw output and formats into 5 labeled sections
 ```
 ```python
 Writer agent takes structured guide and writes professional client response
 ```
 ## Inputs
 - User query string (e.g., destination location)
 - BedrockModel configuration (model_id, temperature, top_p)
 - Pre-configured agents with specific system prompts and tool sets
 ## Outputs
 - Raw research data (JSON string containing gathered facts)
 - Structured guide content (markdown-formatted travel guide with labeled sections)
 - Final client response (professional formatted response ready for delivery)
 ## Failure Modes
 - Researcher agent fails to gather sufficient data or returns incomplete results
 - Travel guide agent fails to structure information correctly or produces unreadable output
 - Writer agent fails to format the final response properly or loses key information from the guide
 ## Source
 Extracted from: [https://github.com/omerbsezer/Fast-LLM-Agent-MCP.git](https://github.com/omerbsezer/Fast-LLM-Agent-MCP.git)
 Confidence: 0.95
@@ -0,0 +1,6 @@
 # Commands: langgraph-multi-agent-router
 ## Available Commands
 - `/skill langgraph-multi-agent-router` — Load this skill
 - `/run langgraph-multi-agent-router` — Execute workflow
@@ -0,0 +1,10 @@
 # Examples: langgraph-multi-agent-router
 ## Usage Example
 ```python
 # How to use this skill
 # Inputs: User query string (e.g., destination location), BedrockModel configuration (model_id, temperature, top_p), Pre-configured agents with specific system prompts and tool sets
 # Process: Researcher agent executes with system prompt to gather raw destination facts (places, history, accommodations, food, web pages) using BedrockModel and available tools (calculator, current_time) → Travel guide agent receives raw research output and structures it into labeled sections (Must-See Attractions, Historical Highlights, Accommodation Areas, Culinary Delights, Suggested Web Pages) → Writer agent receives the structured guide and synthesizes it into a professional client-facing response with clear formatting and emphasis on the suggested web pages
 # Outputs: Raw research data (JSON string containing gathered facts), Structured guide content (markdown-formatted travel guide with labeled sections), Final client response (professional formatted response ready for delivery)
 ```
@@ -0,0 +1,29 @@
 {
  "name": "langgraph-multi-agent-router",
  "version": "1.0.0",
  "goal": "Orchestrate a multi-agent workflow where specialized agents collaborate sequentially to gather information, structure it, and generate a final response",
  "inputs": [
    "User query string (e.g., destination location)",
    "BedrockModel configuration (model_id, temperature, top_p)",
    "Pre-configured agents with specific system prompts and tool sets"
  ],
  "steps": [
    "Researcher agent executes with system prompt to gather raw destination facts (places, history, accommodations, food, web pages) using BedrockModel and available tools (calculator, current_time)",
    "Travel guide agent receives raw research output and structures it into labeled sections (Must-See Attractions, Historical Highlights, Accommodation Areas, Culinary Delights, Suggested Web Pages)",
    "Writer agent receives the structured guide and synthesizes it into a professional client-facing response with clear formatting and emphasis on the suggested web pages"
  ],
  "outputs": [
    "Raw research data (JSON string containing gathered facts)",
    "Structured guide content (markdown-formatted travel guide with labeled sections)",
    "Final client response (professional formatted response ready for delivery)"
  ],
  "failure_modes": [
    "Researcher agent fails to gather sufficient data or returns incomplete results",
    "Travel guide agent fails to structure information correctly or produces unreadable output",
    "Writer agent fails to format the final response properly or loses key information from the guide"
  ],
  "confidence": 0.95,
  "explanation": "This workflow demonstrates a reusable multi-stage agent pattern where specialized agents collaborate in sequence. The Researcher agent gathers raw information using a domain-specific model, the Travel Guide agent structures that information into a consistent format, and the Writer agent synthesizes the final output. This pattern can be adapted to other domains (e.g., code generation, data analysis, research workflows) by swapping the agent types and system prompts while maintaining the same three-step structure.",
  "source_repo": "https://github.com/omerbsezer/Fast-LLM-Agent-MCP.git",
  "score": 1.0
 }
@@ -0,0 +1,9 @@
 # Tests: langgraph-multi-agent-router
 ## Test Checklist
 - [ ] Workflow has at least 3 steps
 - [ ] All inputs are defined
 - [ ] All outputs are defined
 - [ ] Failure modes are documented
 - [ ] Skill can be loaded without errors
@@ -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
@@ -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
@@ -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
 ```
@@ -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
 }
@@ -0,0 +1,9 @@
 # Tests: literature-review-with-traceable-ai-evidence
 ## Test Checklist
 - [ ] Workflow has at least 3 steps
 - [ ] All inputs are defined
 - [ ] All outputs are defined
 - [ ] Failure modes are documented
 - [ ] Skill can be loaded without errors
@@ -0,0 +1,113 @@
 ---
 name: multi-agent-workflow-execution
 version: 1.0.0
 description: Execute multi-agent AI workflows defined in YAML blueprints by creating
  sessions, submitting user prompts, and polling for completion until final answers
  are returned.
 inputs:
 - Blueprint ID or name (to identify the workflow to execute)
 - User shortcut (authentication identifier for the user)
 - User question or prompt (input to the workflow)
 - Base URL of the UnifAI API (endpoint for session management)
 - Polling interval (seconds between status checks during execution)
 steps:
 - Resolve the blueprint ID from either direct ID or name lookup via the API, handling
  cases where the blueprint is not found or not unique
 - Create a new session from the resolved blueprint using the session creation endpoint
 - Submit the session with the user's prompt to start the multi-agent workflow execution
 - Poll the session status at regular intervals until the session completes, fails,
  or is cancelled
 - Retrieve and return the final answer from the completed workflow
 outputs:
 - Final workflow result or answer (text or structured data)
 - Session status (completed, failed, or cancelled)
 - Error details if the workflow execution fails or times out
 tags: []
 metadata:
  source_repo: https://github.com/redhat-community-ai-tools/UnifAI.git
  extracted_at: ''
  confidence: 0.95
 ---
 # multi-agent-workflow-execution
 Execute multi-agent AI workflows defined in YAML blueprints by creating sessions, submitting user prompts, and polling for completion until final answers are returned.
 ## Setup
 **Dependencies:**
 ```text
 pip install requests urllib3 python-langgraph temporalio
 ```
 **Setup steps:**
 1. Install Python 3.11+ and required packages (requests, langgraph, temporalio)
 1. Configure API base URL and user credentials in environment variables or config
 1. Define or select a blueprint from the available workflows in the system
 1. Run the execution_workflow.py script with blueprint ID/name and user prompt
 ## Key Files
 - `scripts/execution_workflow.py - Main workflow execution script`
 - `multi-agent/lib/mas/engine/ - LangGraph-based orchestration modules`
 - `multi-agent/lib/mas/elements/ - Node definitions (custom_agent_node, merger_node, etc.)`
 - `multi-agent/lib/mas/blueprints/ - Blueprint resolution and validation logic`
 ## Steps
 1. Resolve the blueprint ID from either direct ID or name lookup via the API, handling cases where the blueprint is not found or not unique
 2. Create a new session from the resolved blueprint using the session creation endpoint
 3. Submit the session with the user's prompt to start the multi-agent workflow execution
 4. Poll the session status at regular intervals until the session completes, fails, or is cancelled
 5. Retrieve and return the final answer from the completed workflow
 ## Implementation Details
 ```python
 resolve_blueprint_id() - Resolves blueprint by ID or name lookup with error handling
 ```
 ```python
 create_session() - Creates a new session from a blueprint via POST /user.session.create
 ```
 ```python
 submit_session() - Submits user prompt to start workflow via POST /user.session.submit
 ```
 ```python
 poll_session_status() - Polls session.stream.status at configurable intervals
 ```
 ```python
 get_final_answer() - Retrieves final output via GET /session.chat.get
 ```
 ## Inputs
 - Blueprint ID or name (to identify the workflow to execute)
 - User shortcut (authentication identifier for the user)
 - User question or prompt (input to the workflow)
 - Base URL of the UnifAI API (endpoint for session management)
 - Polling interval (seconds between status checks during execution)
 ## Outputs
 - Final workflow result or answer (text or structured data)
 - Session status (completed, failed, or cancelled)
 - Error details if the workflow execution fails or times out
 ## Failure Modes
 - Blueprint not found or not unique - script exits with an error listing available blueprints
 - Session creation fails - may be due to invalid blueprint ID, authentication issues, or rate limiting
 - Session submission fails - could be due to network issues, invalid parameters, or API rate limits
 - Polling loop times out - session may be stuck in a long-running state without progress
 - Final answer retrieval fails - could be due to session cleanup or network issues after completion
 ## Source
 Extracted from: [https://github.com/redhat-community-ai-tools/UnifAI.git](https://github.com/redhat-community-ai-tools/UnifAI.git)
 Confidence: 0.95
@@ -0,0 +1,6 @@
 # Commands: multi-agent-workflow-execution
 ## Available Commands
 - `/skill multi-agent-workflow-execution` — Load this skill
 - `/run multi-agent-workflow-execution` — Execute workflow
@@ -0,0 +1,10 @@
 # Examples: multi-agent-workflow-execution
 ## Usage Example
 ```python
 # How to use this skill
 # Inputs: Blueprint ID or name (to identify the workflow to execute), User shortcut (authentication identifier for the user), User question or prompt (input to the workflow), Base URL of the UnifAI API (endpoint for session management), Polling interval (seconds between status checks during execution)
 # Process: Resolve the blueprint ID from either direct ID or name lookup via the API, handling cases where the blueprint is not found or not unique → Create a new session from the resolved blueprint using the session creation endpoint → Submit the session with the user's prompt to start the multi-agent workflow execution
 # Outputs: Final workflow result or answer (text or structured data), Session status (completed, failed, or cancelled), Error details if the workflow execution fails or times out
 ```
@@ -0,0 +1,35 @@
 {
  "name": "multi-agent-workflow-execution",
  "version": "1.0.0",
  "goal": "Execute multi-agent AI workflows defined in YAML blueprints by creating sessions, submitting user prompts, and polling for completion until final answers are returned.",
  "inputs": [
    "Blueprint ID or name (to identify the workflow to execute)",
    "User shortcut (authentication identifier for the user)",
    "User question or prompt (input to the workflow)",
    "Base URL of the UnifAI API (endpoint for session management)",
    "Polling interval (seconds between status checks during execution)"
  ],
  "steps": [
    "Resolve the blueprint ID from either direct ID or name lookup via the API, handling cases where the blueprint is not found or not unique",
    "Create a new session from the resolved blueprint using the session creation endpoint",
    "Submit the session with the user's prompt to start the multi-agent workflow execution",
    "Poll the session status at regular intervals until the session completes, fails, or is cancelled",
    "Retrieve and return the final answer from the completed workflow"
  ],
  "outputs": [
    "Final workflow result or answer (text or structured data)",
    "Session status (completed, failed, or cancelled)",
    "Error details if the workflow execution fails or times out"
  ],
  "failure_modes": [
    "Blueprint not found or not unique - script exits with an error listing available blueprints",
    "Session creation fails - may be due to invalid blueprint ID, authentication issues, or rate limiting",
    "Session submission fails - could be due to network issues, invalid parameters, or API rate limits",
    "Polling loop times out - session may be stuck in a long-running state without progress",
    "Final answer retrieval fails - could be due to session cleanup or network issues after completion"
  ],
  "confidence": 0.95,
  "explanation": "The UnifAI repository contains a concrete, reusable workflow pattern for executing multi-agent AI workflows. The scripts/execution_workflow.py script demonstrates a complete pipeline: resolving blueprints by ID or name, creating sessions from blueprints, submitting user prompts to start workflows, polling session status until completion, and retrieving final answers. This pattern can be adapted to any multi-agent workflow defined in the YAML blueprint system, making it reusable across different use cases and teams.",
  "source_repo": "https://github.com/redhat-community-ai-tools/UnifAI.git",
  "score": 1.0
 }
@@ -0,0 +1,9 @@
 # Tests: multi-agent-workflow-execution
 ## Test Checklist
 - [ ] Workflow has at least 3 steps
 - [ ] All inputs are defined
 - [ ] All outputs are defined
 - [ ] Failure modes are documented
 - [ ] Skill can be loaded without errors