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

name, version, description, inputs, steps, outputs, tags, metadata

name	version	description	inputs	steps	outputs	tags	metadata
graph-based-node-workflow	1.0.0	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.	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	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	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		source_repo extracted_at confidence https://github.com/temiroff/Blacknode.git 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:

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
2. Clone repository and navigate to project directory
3. Run examples/converted_text_pipeline.py to see basic graph execution
4. 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

g = bn.Graph()

g._edges = [{"from": "model", "from_port": "value", "to": "agent", "to_port": "model"}]

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 Confidence: 0.95