Add Skill: blacknode-text-concatenation-workflow

Extracted from: https://github.com/temiroff/Blacknode.git
Score: 1.0

skills/blacknode-text-concatenation-workflow/SKILL.md

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+---
+name: blacknode-text-concatenation-workflow
+version: 1.0.0
+description: Concatenate two text strings using a Blacknode graph of Text, Concat,
+  and Output nodes.
+inputs:
+- 'text_a: string'
+- 'text_b: string'
+steps:
+- Initialize a Blacknode Graph object.
+- Add a Text node with parameter value set to text_a.
+- Add a second Text node with parameter value set to text_b.
+- Add a Concat node (no parameters required).
+- Add an Output node (no parameters required).
+- Connect the 'value' output port of the first Text node to the 'a' input port of
+  the Concat node.
+- Connect the 'value' output port of the second Text node to the 'b' input port of
+  the Concat node.
+- Connect the 'value' output port of the Concat node to the 'value' input port of
+  the Output node.
+- Evaluate the graph by cooking the Output node's 'value' port to obtain the concatenated
+  result.
+outputs:
+- 'concatenated_text: string'
+tags: []
+metadata:
+  source_repo: https://github.com/temiroff/Blacknode.git
+  extracted_at: ''
+  confidence: 0.95
+---
+
+# blacknode-text-concatenation-workflow
+
+Concatenate two text strings using a Blacknode graph of Text, Concat, and Output nodes.
+
+## Steps
+
+1. Initialize a Blacknode Graph object.
+2. Add a Text node with parameter value set to text_a.
+3. Add a second Text node with parameter value set to text_b.
+4. Add a Concat node (no parameters required).
+5. Add an Output node (no parameters required).
+6. Connect the 'value' output port of the first Text node to the 'a' input port of the Concat node.
+7. Connect the 'value' output port of the second Text node to the 'b' input port of the Concat node.
+8. Connect the 'value' output port of the Concat node to the 'value' input port of the Output node.
+9. Evaluate the graph by cooking the Output node's 'value' port to obtain the concatenated result.
+
+## Inputs
+
+- text_a: string
+- text_b: string
+
+## Outputs
+
+- concatenated_text: string
+
+## Failure Modes
+
+- Node types 'Text', 'Concat', or 'Output' not registered in Blacknode runtime
+- Port name mismatches during edge creation
+- Missing input values causing empty concatenation
+- Graph evaluation error if cycles or disconnected required ports
+
+## Source
+
+Extracted from: [https://github.com/temiroff/Blacknode.git](https://github.com/temiroff/Blacknode.git)
+Confidence: 0.95

skills/blacknode-text-concatenation-workflow/commands.md

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+# Commands: blacknode-text-concatenation-workflow
+
+## Available Commands
+
+- `/skill blacknode-text-concatenation-workflow` — Load this skill
+- `/run blacknode-text-concatenation-workflow` — Execute workflow

skills/blacknode-text-concatenation-workflow/examples.md

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+# Examples: blacknode-text-concatenation-workflow
+
+## Usage Example
+
+```python
+# How to use this skill
+# Inputs: text_a: string, text_b: string
+# Process: Initialize a Blacknode Graph object. → Add a Text node with parameter value set to text_a. → Add a second Text node with parameter value set to text_b.
+# Outputs: concatenated_text: string
+```

skills/blacknode-text-concatenation-workflow/metadata.json

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+{
+  "name": "blacknode-text-concatenation-workflow",
+  "version": "1.0.0",
+  "goal": "Concatenate two text strings using a Blacknode graph of Text, Concat, and Output nodes.",
+  "inputs": [
+    "text_a: string",
+    "text_b: string"
+  ],
+  "steps": [
+    "Initialize a Blacknode Graph object.",
+    "Add a Text node with parameter value set to text_a.",
+    "Add a second Text node with parameter value set to text_b.",
+    "Add a Concat node (no parameters required).",
+    "Add an Output node (no parameters required).",
+    "Connect the 'value' output port of the first Text node to the 'a' input port of the Concat node.",
+    "Connect the 'value' output port of the second Text node to the 'b' input port of the Concat node.",
+    "Connect the 'value' output port of the Concat node to the 'value' input port of the Output node.",
+    "Evaluate the graph by cooking the Output node's 'value' port to obtain the concatenated result."
+  ],
+  "outputs": [
+    "concatenated_text: string"
+  ],
+  "failure_modes": [
+    "Node types 'Text', 'Concat', or 'Output' not registered in Blacknode runtime",
+    "Port name mismatches during edge creation",
+    "Missing input values causing empty concatenation",
+    "Graph evaluation error if cycles or disconnected required ports"
+  ],
+  "confidence": 0.95,
+  "explanation": "Extracted from examples/converted_text_pipeline.py and referenced templates/text-pipeline.json in the Blacknode repo. This workflow is a foundational, dependency-free pattern for building directed graphs of typed nodes and is applicable to any simple data combination task.",
+  "source_repo": "https://github.com/temiroff/Blacknode.git",
+  "score": 1.0
+}

skills/blacknode-text-concatenation-workflow/tests.md

@@ -1,4 +1,4 @@
-# Tests: graph-based-node-orchestration
+# Tests: blacknode-text-concatenation-workflow
 
 ## Test Checklist
 

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

@@ -1,108 +0,0 @@
----
-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

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

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

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

@@ -1,10 +0,0 @@
-# Examples: graph-based-node-orchestration
-
-## Usage Example
-
-```python
-# How to use this skill
-# 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
-# Process: 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)
-# 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
-```

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

@@ -1,34 +0,0 @@
-{
-  "name": "graph-based-node-orchestration",
-  "version": "1.0.0",
-  "goal": "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"
-  ],
-  "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"
-  ],
-  "confidence": 0.95,
-  "explanation": "This workflow demonstrates a declarative graph-based orchestration pattern where nodes are connected via explicit ports and data flows through the graph. The pattern is highly reusable across different domains (robotics, research, data processing) because it separates graph structure from execution logic. The same graph construction and cook pattern can be adapted to different models (NIM, Anthropic, OpenAI), hardware targets (CPU, GPU, Jetson), and task types (LLM reasoning, file I/O, sensor processing).",
-  "source_repo": "https://github.com/temiroff/Blacknode.git",
-  "score": 1.0
-}