Add Skill: graph-based-node-workflow

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

skills/conditional-request-routing/SKILL.md

@@ -1,54 +0,0 @@
----
-name: conditional-request-routing
-version: 1.0.0
-description: Classify an input request and route it to either an approval or rejection
-  path, producing a corresponding result
-inputs:
-- 'request: the input request or content to be reviewed and routed'
-steps:
-- 'Collect input: Use an input agent to capture the user''s request into state field
-  ''request'''
-- 'Branch: Use a branching agent to evaluate ''request'' and route to ''Approve''
-  node on success or ''Reject'' node on failure'
-- 'Approve path: Use a default agent to format and output an approval message with
-  the request'
-- 'Reject path: Use a default agent to format and output a rejection message with
-  the request'
-outputs:
-- 'result: the approval or rejection message containing the original request'
-tags: []
-metadata:
-  source_repo: https://github.com/jwwelbor/AgentMap.git
-  extracted_at: ''
-  confidence: 0.85
----
-
-# conditional-request-routing
-
-Classify an input request and route it to either an approval or rejection path, producing a corresponding result
-
-## Steps
-
-1. Collect input: Use an input agent to capture the user's request into state field 'request'
-2. Branch: Use a branching agent to evaluate 'request' and route to 'Approve' node on success or 'Reject' node on failure
-3. Approve path: Use a default agent to format and output an approval message with the request
-4. Reject path: Use a default agent to format and output a rejection message with the request
-
-## Inputs
-
-- request: the input request or content to be reviewed and routed
-
-## Outputs
-
-- result: the approval or rejection message containing the original request
-
-## Failure Modes
-
-- Branching agent cannot evaluate request and neither path is taken
-- Missing or empty input request
-- Prompt misconfiguration causing incorrect routing
-
-## Source
-
-Extracted from: [https://github.com/jwwelbor/AgentMap.git](https://github.com/jwwelbor/AgentMap.git)
-Confidence: 0.85

skills/conditional-request-routing/commands.md

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

skills/conditional-request-routing/examples.md

@@ -1,10 +0,0 @@
-# Examples: conditional-request-routing
-
-## Usage Example
-
-```python
-# How to use this skill
-# Inputs: request: the input request or content to be reviewed and routed
-# Process: Collect input: Use an input agent to capture the user's request into state field 'request' → Branch: Use a branching agent to evaluate 'request' and route to 'Approve' node on success or 'Reject' node on failure → Approve path: Use a default agent to format and output an approval message with the request
-# Outputs: result: the approval or rejection message containing the original request
-```

skills/conditional-request-routing/metadata.json

@@ -1,26 +0,0 @@
-{
-  "name": "conditional-request-routing",
-  "version": "1.0.0",
-  "goal": "Classify an input request and route it to either an approval or rejection path, producing a corresponding result",
-  "inputs": [
-    "request: the input request or content to be reviewed and routed"
-  ],
-  "steps": [
-    "Collect input: Use an input agent to capture the user's request into state field 'request'",
-    "Branch: Use a branching agent to evaluate 'request' and route to 'Approve' node on success or 'Reject' node on failure",
-    "Approve path: Use a default agent to format and output an approval message with the request",
-    "Reject path: Use a default agent to format and output a rejection message with the request"
-  ],
-  "outputs": [
-    "result: the approval or rejection message containing the original request"
-  ],
-  "failure_modes": [
-    "Branching agent cannot evaluate request and neither path is taken",
-    "Missing or empty input request",
-    "Prompt misconfiguration causing incorrect routing"
-  ],
-  "confidence": 0.85,
-  "explanation": "Extracted from AgentMap's README example 'ReviewFlow' CSV workflow. This is a declarative LangGraph pattern using AgentMap's CSV format that implements conditional branching - a universally reusable pattern for request triage, content moderation, approval gates, or any two-path decision flow. It can be adapted by changing agent types (e.g., using LLM agents instead of default) and prompts.",
-  "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-request-routing
+# Tests: graph-based-node-workflow
 
 ## Test Checklist