Add Skill: graph-based-node-workflow

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

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

@@ -0,0 +1,100 @@
+---
+name: graph-based-node-workflow
+version: 1.0.0
+description: 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.
+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
+steps:
+- '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'
+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
+tags: []
+metadata:
+  source_repo: https://github.com/temiroff/Blacknode.git
+  extracted_at: ''
+  confidence: 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:**
+
+```text
+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
+1. Clone repository and navigate to project directory
+1. Run examples/converted_text_pipeline.py to see basic graph execution
+1. 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
+
+```python
+g = bn.Graph()
+```
+
+```python
+g._edges = [{"from": "model", "from_port": "value", "to": "agent", "to_port": "model"}]
+```
+
+```python
+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](https://github.com/temiroff/Blacknode.git)
+Confidence: 0.95

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

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+# Commands: graph-based-node-workflow
+
+## Available Commands
+
+- `/skill graph-based-node-workflow` — Load this skill
+- `/run graph-based-node-workflow` — Execute workflow

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

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+# Examples: graph-based-node-workflow
+
+## Usage Example
+
+```python
+# How to use this skill
+# 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
+# Process: 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
+# 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
+```

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

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+{
+  "name": "graph-based-node-workflow",
+  "version": "1.0.0",
+  "goal": "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.",
+  "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"
+  ],
+  "steps": [
+    "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"
+  ],
+  "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"
+  ],
+  "confidence": 0.95,
+  "explanation": "This workflow pattern is reusable across different AI/robotics applications because it provides a standardized way to compose complex pipelines from typed nodes. The pattern can be adapted to various use cases like research pipelines, agent workflows, or robotics control graphs by simply adding/removing nodes and edges while maintaining the same graph-cooking execution model.",
+  "source_repo": "https://github.com/temiroff/Blacknode.git",
+  "score": 1.0
+}

skills/graph-based-node-workflow/tests.md

@@ -1,4 +1,4 @@
-# Tests: langgraph-multi-agent-sequential
+# Tests: graph-based-node-workflow
 
 ## Test Checklist
 

skills/langgraph-multi-agent-sequential/SKILL.md

@@ -1,99 +0,0 @@
----
-name: langgraph-multi-agent-sequential
-version: 1.0.0
-description: Orchestrate a sequence of specialized agents to perform multi-step tasks
-  like research, data processing, and final output generation
-inputs:
-- BedrockModel with temperature=0.3, top_p=0.8
-- Researcher agent with system prompt for destination research (places, history, accommodations,
-  food, web pages)
-- Travel Guide Generator agent with system prompt for structuring travel guides into
-  labeled sections
-- Writer agent with system prompt for formatting professional client responses
-steps:
-- Researcher agent gathers raw destination facts (top 5 attractions, historical facts,
-  best areas, local foods, suggested web pages) using BedrockModel
-- Travel Guide Generator agent structures the raw facts into a comprehensive travel
-  guide with clearly labeled sections
-- Writer agent formats the structured guide into a professional client-facing response
-  with the full guide and highlighted web pages
-outputs:
-- Raw research data (JSON string containing destination facts and categories)
-- Structured travel guide content (markdown with sections for attractions, history,
-  accommodations, cuisine, and web pages)
-- Final client response (formatted travel guide ready for delivery)
-tags: []
-metadata:
-  source_repo: https://github.com/omerbsezer/Fast-LLM-Agent-MCP.git
-  extracted_at: ''
-  confidence: 0.95
----
-
-# langgraph-multi-agent-sequential
-
-Orchestrate a sequence of specialized agents to perform multi-step tasks like research, data processing, and final output generation
-
-## Setup
-
-**Dependencies:**
-
-```text
-pip install langchain langgraph bedrock-model pydantic
-```
-
-**Setup steps:**
-
-1. Install langchain and langgraph packages
-1. Configure BedrockModel with temperature=0.3 and top_p=0.8
-1. Create three Agent instances with appropriate system prompts and tools
-1. Deploy the FastAPI server with the LangGraph application
-
-## Key Files
-
-- `agents/langchain_langgraph/08-langgraph-multi-agents-sequential-pattern/agent.py`
-- `agents/langchain_langgraph/08-langgraph-multi-agents-sequential-pattern/app.py`
-
-## Steps
-
-1. Researcher agent gathers raw destination facts (top 5 attractions, historical facts, best areas, local foods, suggested web pages) using BedrockModel
-2. Travel Guide Generator agent structures the raw facts into a comprehensive travel guide with clearly labeled sections
-3. Writer agent formats the structured guide into a professional client-facing response with the full guide and highlighted web pages
-
-## Implementation Details
-
-```python
-Researcher agent with system_prompt for destination research and tools=[calculator, current_time]
-```
-
-```python
-Travel Guide Generator agent with system_prompt requiring structured sections (attractions, history, accommodations, cuisine, web pages)
-```
-
-```python
-Writer agent with system_prompt for client-facing response formatting
-```
-
-## Inputs
-
-- BedrockModel with temperature=0.3, top_p=0.8
-- Researcher agent with system prompt for destination research (places, history, accommodations, food, web pages)
-- Travel Guide Generator agent with system prompt for structuring travel guides into labeled sections
-- Writer agent with system prompt for formatting professional client responses
-
-## Outputs
-
-- Raw research data (JSON string containing destination facts and categories)
-- Structured travel guide content (markdown with sections for attractions, history, accommodations, cuisine, and web pages)
-- Final client response (formatted travel guide ready for delivery)
-
-## Failure Modes
-
-- Researcher agent fails to retrieve sufficient information, causing downstream agents to receive incomplete or empty data
-- Travel Guide Generator fails to structure data properly, resulting in unorganized or malformed output
-- Writer agent fails to format the final response correctly, producing garbled or incomplete output
-- Model timeouts or errors in any agent step causing the entire pipeline to fail
-
-## Source
-
-Extracted from: [https://github.com/omerbsezer/Fast-LLM-Agent-MCP.git](https://github.com/omerbsezer/Fast-LLM-Agent-MCP.git)
-Confidence: 0.95

skills/langgraph-multi-agent-sequential/commands.md

@@ -1,6 +0,0 @@
-# Commands: langgraph-multi-agent-sequential
-
-## Available Commands
-
-- `/skill langgraph-multi-agent-sequential` — Load this skill
-- `/run langgraph-multi-agent-sequential` — Execute workflow

skills/langgraph-multi-agent-sequential/examples.md

@@ -1,10 +0,0 @@
-# Examples: langgraph-multi-agent-sequential
-
-## Usage Example
-
-```python
-# How to use this skill
-# Inputs: BedrockModel with temperature=0.3, top_p=0.8, Researcher agent with system prompt for destination research (places, history, accommodations, food, web pages), Travel Guide Generator agent with system prompt for structuring travel guides into labeled sections, Writer agent with system prompt for formatting professional client responses
-# Process: Researcher agent gathers raw destination facts (top 5 attractions, historical facts, best areas, local foods, suggested web pages) using BedrockModel → Travel Guide Generator agent structures the raw facts into a comprehensive travel guide with clearly labeled sections → Writer agent formats the structured guide into a professional client-facing response with the full guide and highlighted web pages
-# Outputs: Raw research data (JSON string containing destination facts and categories), Structured travel guide content (markdown with sections for attractions, history, accommodations, cuisine, and web pages), Final client response (formatted travel guide ready for delivery)
-```

skills/langgraph-multi-agent-sequential/metadata.json

@@ -1,31 +0,0 @@
-{
-  "name": "langgraph-multi-agent-sequential",
-  "version": "1.0.0",
-  "goal": "Orchestrate a sequence of specialized agents to perform multi-step tasks like research, data processing, and final output generation",
-  "inputs": [
-    "BedrockModel with temperature=0.3, top_p=0.8",
-    "Researcher agent with system prompt for destination research (places, history, accommodations, food, web pages)",
-    "Travel Guide Generator agent with system prompt for structuring travel guides into labeled sections",
-    "Writer agent with system prompt for formatting professional client responses"
-  ],
-  "steps": [
-    "Researcher agent gathers raw destination facts (top 5 attractions, historical facts, best areas, local foods, suggested web pages) using BedrockModel",
-    "Travel Guide Generator agent structures the raw facts into a comprehensive travel guide with clearly labeled sections",
-    "Writer agent formats the structured guide into a professional client-facing response with the full guide and highlighted web pages"
-  ],
-  "outputs": [
-    "Raw research data (JSON string containing destination facts and categories)",
-    "Structured travel guide content (markdown with sections for attractions, history, accommodations, cuisine, and web pages)",
-    "Final client response (formatted travel guide ready for delivery)"
-  ],
-  "failure_modes": [
-    "Researcher agent fails to retrieve sufficient information, causing downstream agents to receive incomplete or empty data",
-    "Travel Guide Generator fails to structure data properly, resulting in unorganized or malformed output",
-    "Writer agent fails to format the final response correctly, producing garbled or incomplete output",
-    "Model timeouts or errors in any agent step causing the entire pipeline to fail"
-  ],
-  "confidence": 0.95,
-  "explanation": "This workflow demonstrates a reusable LangGraph pattern where three specialized agents work sequentially: a Researcher agent gathers raw destination facts, a Travel Guide Generator agent structures those facts into a travel guide, and a Writer agent formats the final output for clients. The pattern is modular and can be adapted to other multi-step tasks by swapping agent roles and prompts while maintaining the same pipeline structure.",
-  "source_repo": "https://github.com/omerbsezer/Fast-LLM-Agent-MCP.git",
-  "score": 1.0
-}