Add Skill: graph-based-node-orchestration

Extracted from: https://github.com/temiroff/Blacknode.git Score: 1.0
2026-08-06 14:41:19 +00:00
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+---
+name: graph-based-node-orchestration
+version: 1.0.0
+description: Build a typed node graph that processes data through a sequence of operations
+  and produces a final result
+inputs:
+- NIM_MODEL constant (e.g., 'nim:meta/llama-3.1-8b-instruct') for the LLM model
+- blacknode package with Graph, Node, and cook functionality
+- Python script defining node types with inputs/outputs and connecting them via edges
+steps:
+- Import required modules (blacknode as bn, NIM_MODEL) and ensure API keys are available
+  via require_nim_api_key()
+- Create a bn.Graph() instance to hold all nodes and their connections
+- Define individual nodes with specific input/output parameters (e.g., Text node with
+  'value' input, LLMAgent node with model parameter, Output node with 'value' output)
+- Connect nodes together using edge definitions (from_port -> to_port) to establish
+  data flow
+- Execute the graph using g.cook() to run the pipeline and process data through the
+  node chain
+- Extract the final result from the output node to complete the workflow
+outputs:
+- A fully constructed graph with typed nodes and defined connections
+- Executed result (e.g., processed text, summary, or other output) from the final
+  node
+- A reusable pattern that can be adapted to different models, hardware, or task types
+tags: []
+metadata:
+  source_repo: https://github.com/temiroff/Blacknode.git
+  extracted_at: ''
+  confidence: 0.95
+---
+
+# graph-based-node-orchestration
+
+Build a typed node graph that processes data through a sequence of operations and produces a final result
+
+## Setup
+
+**Dependencies:**
+
+```text
+pip install blacknode >= 0.3.0 Python >= 3.11 NVIDIA NIM API key (optional but recommended) Anthropic, OpenAI, or other LLM models
+```
+
+**Setup steps:**
+
+1. Clone the repository and install dependencies: pip install -e .
+1. Set NVIDIA_API_KEY or other required API keys in .env
+1. Run the example script: python examples/hello_agent.py
+1. For production, configure hardware pairing and deploy via the blacknode CLI
+
+## Key Files
+
+- `examples/converted_nvidia_nim.py - Full graph with Model, Text, LLMAgent, Output nodes`
+- `examples/hello_agent.py - Minimal agent example connecting Literal → LLMAgent → Print`
+- `blacknode/core - Graph and node implementation (internal)`
+
+## Steps
+
+1. Import required modules (blacknode as bn, NIM_MODEL) and ensure API keys are available via require_nim_api_key()
+2. Create a bn.Graph() instance to hold all nodes and their connections
+3. Define individual nodes with specific input/output parameters (e.g., Text node with 'value' input, LLMAgent node with model parameter, Output node with 'value' output)
+4. Connect nodes together using edge definitions (from_port -> to_port) to establish data flow
+5. Execute the graph using g.cook() to run the pipeline and process data through the node chain
+6. Extract the final result from the output node to complete the workflow
+
+## Implementation Details
+
+```python
+g = bn.Graph()
+```
+
+```python
+model = g.node('Model', **{'value': 'nim:meta/llama-3.1-8b-instruct'})
+```
+
+```python
+agent = g.node('LLMAgent', **{model})
+```
+
+```python
+result = g.cook(output, 'value')
+```
+
+## Inputs
+
+- NIM_MODEL constant (e.g., 'nim:meta/llama-3.1-8b-instruct') for the LLM model
+- blacknode package with Graph, Node, and cook functionality
+- Python script defining node types with inputs/outputs and connecting them via edges
+
+## Outputs
+
+- A fully constructed graph with typed nodes and defined connections
+- Executed result (e.g., processed text, summary, or other output) from the final node
+- A reusable pattern that can be adapted to different models, hardware, or task types
+
+## Failure Modes
+
+- Missing or invalid API key (NIM_API_KEY, OPENAI_API_KEY) causing require_nim_api_key() to fail
+- Type mismatch in node connections (e.g., expecting 'value' but receiving 'text') breaking the data flow
+- Model not available or unreachable (NIM_MODEL not found) causing the LLMAgent node to fail
+- Graph execution error due to incorrect edge configuration or circular dependencies
+- Resource exhaustion (e.g., GPU memory) when processing large inputs in the pipeline
+
+## Source
+
+Extracted from: [https://github.com/temiroff/Blacknode.git](https://github.com/temiroff/Blacknode.git)
+Confidence: 0.95