Add Skill: graph-based-node-workflow

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

skills/conditional-review-workflow/SKILL.md

@@ -1,56 +0,0 @@
----
-name: conditional-review-workflow
-version: 1.0.0
-description: Process a user request through branching logic to approve or reject it,
-  producing a routed decision result
-inputs:
-- 'request: string - The user''s request or input collected at runtime via the input
-  agent'
-steps:
-- 'Start: Input agent collects the user request and stores it in the ''request'' state
-  field, then routes to Classify node'
-- 'Classify: Branching agent evaluates the ''request'' field and routes to Approve
-  node on success or Reject node on failure (on_failure)'
-- 'Approve: Default agent formats an approval message using the request and stores
-  it in the ''result'' output field'
-- 'Reject: Default agent formats a rejection message using the request and stores
-  it in the ''result'' output field'
-outputs:
-- 'result: string - Final message indicating whether the request was approved or rejected,
-  containing the original request'
-tags: []
-metadata:
-  source_repo: https://github.com/jwwelbor/AgentMap.git
-  extracted_at: ''
-  confidence: 0.85
----
-
-# conditional-review-workflow
-
-Process a user request through branching logic to approve or reject it, producing a routed decision result
-
-## Steps
-
-1. Start: Input agent collects the user request and stores it in the 'request' state field, then routes to Classify node
-2. Classify: Branching agent evaluates the 'request' field and routes to Approve node on success or Reject node on failure (on_failure)
-3. Approve: Default agent formats an approval message using the request and stores it in the 'result' output field
-4. Reject: Default agent formats a rejection message using the request and stores it in the 'result' output field
-
-## Inputs
-
-- request: string - The user's request or input collected at runtime via the input agent
-
-## Outputs
-
-- result: string - Final message indicating whether the request was approved or rejected, containing the original request
-
-## Failure Modes
-
-- Input collection failure - user provides no or invalid input (no explicit on_failure defined for Start node in example)
-- Branching classification failure - if branching agent cannot evaluate, it routes to Reject via on_failure configuration
-- LLM provider unavailability - if branching or agents rely on LLM backends that are misconfigured or offline
-
-## Source
-
-Extracted from: [https://github.com/jwwelbor/AgentMap.git](https://github.com/jwwelbor/AgentMap.git)
-Confidence: 0.85

skills/conditional-review-workflow/commands.md

@@ -1,6 +0,0 @@
-# Commands: conditional-review-workflow
-
-## Available Commands
-
-- `/skill conditional-review-workflow` — Load this skill
-- `/run conditional-review-workflow` — Execute workflow

skills/conditional-review-workflow/examples.md

@@ -1,10 +0,0 @@
-# Examples: conditional-review-workflow
-
-## Usage Example
-
-```python
-# How to use this skill
-# Inputs: request: string - The user's request or input collected at runtime via the input agent
-# Process: Start: Input agent collects the user request and stores it in the 'request' state field, then routes to Classify node → Classify: Branching agent evaluates the 'request' field and routes to Approve node on success or Reject node on failure (on_failure) → Approve: Default agent formats an approval message using the request and stores it in the 'result' output field
-# Outputs: result: string - Final message indicating whether the request was approved or rejected, containing the original request
-```

skills/conditional-review-workflow/metadata.json

@@ -1,26 +0,0 @@
-{
-  "name": "conditional-review-workflow",
-  "version": "1.0.0",
-  "goal": "Process a user request through branching logic to approve or reject it, producing a routed decision result",
-  "inputs": [
-    "request: string - The user's request or input collected at runtime via the input agent"
-  ],
-  "steps": [
-    "Start: Input agent collects the user request and stores it in the 'request' state field, then routes to Classify node",
-    "Classify: Branching agent evaluates the 'request' field and routes to Approve node on success or Reject node on failure (on_failure)",
-    "Approve: Default agent formats an approval message using the request and stores it in the 'result' output field",
-    "Reject: Default agent formats a rejection message using the request and stores it in the 'result' output field"
-  ],
-  "outputs": [
-    "result: string - Final message indicating whether the request was approved or rejected, containing the original request"
-  ],
-  "failure_modes": [
-    "Input collection failure - user provides no or invalid input (no explicit on_failure defined for Start node in example)",
-    "Branching classification failure - if branching agent cannot evaluate, it routes to Reject via on_failure configuration",
-    "LLM provider unavailability - if branching or agents rely on LLM backends that are misconfigured or offline"
-  ],
-  "confidence": 0.85,
-  "explanation": "Extracted from AgentMap's documented CSV workflow example (ReviewFlow). This is a reusable conditional routing pattern that can be adapted for any approval/rejection, triage, or binary-decision scenario by modifying the branching prompt and agent types. The CSV-based declarative format makes it portable across the AgentMap framework.",
-  "source_repo": "https://github.com/jwwelbor/AgentMap.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

@@ -0,0 +1,6 @@
+# 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

@@ -0,0 +1,10 @@
+# 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

@@ -0,0 +1,31 @@
+{
+  "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: conditional-review-workflow
+# Tests: graph-based-node-workflow
 
 ## Test Checklist