Add Skill: blacknode-graph-workflow

Extracted from: https://github.com/temiroff/Blacknode.git Score: 1.0
2026-08-05 17:03:28 +00:00
9 changed files with 143 additions and 168 deletions
@@ -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
+}
@@ -1,4 +1,4 @@
-# Tests: langgraph-agent-workflow
+# Tests: blacknode-graph-workflow

 ## Test Checklist

@@ -1,115 +0,0 @@
---
-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
@@ -1,6 +0,0 @@
-# Commands: langgraph-agent-workflow
-
-## Available Commands
-
- `/skill langgraph-agent-workflow` — Load this skill
- `/run langgraph-agent-workflow` — Execute workflow
@@ -1,10 +0,0 @@
-# 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
-```
@@ -1,36 +0,0 @@
-{
-  "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
-}