Add Skill: branching-agent-pattern

Extracted from: https://github.com/jwwelbor/AgentMap.git
Score: 1.0

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

@@ -1,67 +0,0 @@
----
-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

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

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

@@ -1,33 +0,0 @@
-{
-  "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/branching-agent-pattern/SKILL.md

@@ -0,0 +1,101 @@
+---
+name: branching-agent-pattern
+version: 1.0.0
+description: Define and execute AI agent workflows using CSV-based declarative definitions
+  with configurable branching logic
+inputs:
+- 'CSV workflow files defining agent graphs with columns: graph_name, node_name, agent_type,
+  next_node, on_failure, prompt, input_fields, output_field'
+- LLM provider configuration (OpenAI, Anthropic, Google) in agentmap_config.yaml
+- Storage backend configuration in agentmap_config_storage.yaml
+steps:
+- Define workflow graph in CSV with nodes representing agent steps and their connections
+  (next_node, on_failure)
+- Configure BranchingAgent with customizable success/failure values and fallback fields
+  in the context dictionary
+- Initialize the agent runtime with ensure_initialized() and configure execution tracking
+  and state adapter services
+- Execute the workflow using agentmap run with appropriate inputs and monitor the
+  execution trace
+outputs:
+- Executed workflow with results stored in the specified output_field
+- Detailed execution trace showing success/failure decisions at each branching point
+- Updated workflow state persisted in the configured storage backend
+tags: []
+metadata:
+  source_repo: https://github.com/jwwelbor/AgentMap.git
+  extracted_at: ''
+  confidence: 0.95
+---
+
+# branching-agent-pattern
+
+Define and execute AI agent workflows using CSV-based declarative definitions with configurable branching logic
+
+## Setup
+
+**Dependencies:**
+
+```text
+pip install langgraph>=1.0.0 langchain-core>=0.3.0 pyyaml>=6.0.0 fastapi>=0.111.0 uvicorn>=0.34.3
+```
+
+**Setup steps:**
+
+1. Install AgentMap: pip install agentmap[all]
+1. Configure llm providers in agentmap_config.yaml (OpenAI, Anthropic, Google models)
+1. Create CSV workflow files with graph definitions
+1. Initialize runtime with ensure_initialized()
+1. Run workflow with agentmap run <csv_file> --pretty
+
+## Key Files
+
+- `agentmap_config.yaml - Main configuration with LLM and storage settings`
+- `agentmap_config_storage.yaml - Storage backend configuration`
+- `hello_world.csv - Sample workflow demonstrating basic agent chain`
+- `examples/host_integration/custom_agents.py - Custom agent implementations with host service integration`
+
+## Steps
+
+1. Define workflow graph in CSV with nodes representing agent steps and their connections (next_node, on_failure)
+2. Configure BranchingAgent with customizable success/failure values and fallback fields in the context dictionary
+3. Initialize the agent runtime with ensure_initialized() and configure execution tracking and state adapter services
+4. Execute the workflow using agentmap run with appropriate inputs and monitor the execution trace
+
+## Implementation Details
+
+```python
+CSV format: graph_name,node_name,agent_type,next_node,on_failure,prompt,input_fields,output_field
+```
+
+```python
+BranchingAgent context example: {'input_fields': ['success'], 'output_field': 'result', 'success_values': ['PASSED', 'COMPLETED']}
+```
+
+```python
+Execution command: agentmap run hello_world.csv --pretty
+```
+
+## Inputs
+
+- CSV workflow files defining agent graphs with columns: graph_name, node_name, agent_type, next_node, on_failure, prompt, input_fields, output_field
+- LLM provider configuration (OpenAI, Anthropic, Google) in agentmap_config.yaml
+- Storage backend configuration in agentmap_config_storage.yaml
+
+## Outputs
+
+- Executed workflow with results stored in the specified output_field
+- Detailed execution trace showing success/failure decisions at each branching point
+- Updated workflow state persisted in the configured storage backend
+
+## Failure Modes
+
+- Invalid CSV format causing parsing errors during workflow loading
+- Missing or misconfigured LLM provider settings leading to execution failures
+- Storage backend unavailable or misconfigured preventing workflow persistence
+- Agent execution timeout due to long-running operations or infinite loops
+
+## Source
+
+Extracted from: [https://github.com/jwwelbor/AgentMap.git](https://github.com/jwwelbor/AgentMap.git)
+Confidence: 0.95

skills/branching-agent-pattern/commands.md

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

skills/branching-agent-pattern/examples.md

@@ -0,0 +1,10 @@
+# Examples: branching-agent-pattern
+
+## Usage Example
+
+```python
+# How to use this skill
+# Inputs: CSV workflow files defining agent graphs with columns: graph_name, node_name, agent_type, next_node, on_failure, prompt, input_fields, output_field, LLM provider configuration (OpenAI, Anthropic, Google) in agentmap_config.yaml, Storage backend configuration in agentmap_config_storage.yaml
+# Process: Define workflow graph in CSV with nodes representing agent steps and their connections (next_node, on_failure) → Configure BranchingAgent with customizable success/failure values and fallback fields in the context dictionary → Initialize the agent runtime with ensure_initialized() and configure execution tracking and state adapter services
+# Outputs: Executed workflow with results stored in the specified output_field, Detailed execution trace showing success/failure decisions at each branching point, Updated workflow state persisted in the configured storage backend
+```

skills/branching-agent-pattern/metadata.json

@@ -0,0 +1,31 @@
+{
+  "name": "branching-agent-pattern",
+  "version": "1.0.0",
+  "goal": "Define and execute AI agent workflows using CSV-based declarative definitions with configurable branching logic",
+  "inputs": [
+    "CSV workflow files defining agent graphs with columns: graph_name, node_name, agent_type, next_node, on_failure, prompt, input_fields, output_field",
+    "LLM provider configuration (OpenAI, Anthropic, Google) in agentmap_config.yaml",
+    "Storage backend configuration in agentmap_config_storage.yaml"
+  ],
+  "steps": [
+    "Define workflow graph in CSV with nodes representing agent steps and their connections (next_node, on_failure)",
+    "Configure BranchingAgent with customizable success/failure values and fallback fields in the context dictionary",
+    "Initialize the agent runtime with ensure_initialized() and configure execution tracking and state adapter services",
+    "Execute the workflow using agentmap run with appropriate inputs and monitor the execution trace"
+  ],
+  "outputs": [
+    "Executed workflow with results stored in the specified output_field",
+    "Detailed execution trace showing success/failure decisions at each branching point",
+    "Updated workflow state persisted in the configured storage backend"
+  ],
+  "failure_modes": [
+    "Invalid CSV format causing parsing errors during workflow loading",
+    "Missing or misconfigured LLM provider settings leading to execution failures",
+    "Storage backend unavailable or misconfigured preventing workflow persistence",
+    "Agent execution timeout due to long-running operations or infinite loops"
+  ],
+  "confidence": 0.95,
+  "explanation": "The BranchingAgent pattern provides a reusable framework for creating conditional AI workflows. The CSV-based workflow definition allows defining complex agent graphs declaratively, while the BranchingAgent handles dynamic branching based on success/failure conditions with customizable value sets. This pattern can be adapted to various use cases including task routing, error handling, and conditional execution paths across different domains.",
+  "source_repo": "https://github.com/jwwelbor/AgentMap.git",
+  "score": 1.0
+}

skills/branching-agent-pattern/tests.md

@@ -1,4 +1,4 @@
-# Tests: blacknode-text-concatenation-workflow
+# Tests: branching-agent-pattern
 
 ## Test Checklist