Add Skill: branching-agent-pattern

Extracted from: https://github.com/jwwelbor/AgentMap.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: langgraph-csv-workflow
+# Tests: branching-agent-pattern
 
 ## Test Checklist
 

skills/langgraph-csv-workflow/SKILL.md

@@ -1,89 +0,0 @@
----
-name: langgraph-csv-workflow
-version: 1.0.0
-description: Transform simple CSV files into powerful AI agent workflows using LangGraph
-  orchestration
-inputs:
-- 'CSV workflow files 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 configuration for CSV/JSON/Vector DBs in agentmap_config_storage.yaml
-steps:
-- Define workflow in CSV format specifying graph nodes, agent types, and data flow
-  between them
-- Configure LLM providers and storage backends in the agentmap configuration files
-- Execute the workflow using the agentmap CLI or Python API
-outputs:
-- Executed workflow with agent decisions and state transitions
-- Traced execution path through the graph nodes
-- Logged agent interactions and output fields populated
-tags: []
-metadata:
-  source_repo: https://github.com/jwwelbor/AgentMap.git
-  extracted_at: ''
-  confidence: 0.95
----
-
-# langgraph-csv-workflow
-
-Transform simple CSV files into powerful AI agent workflows using LangGraph orchestration
-
-## Setup
-
-**Dependencies:**
-
-```text
-pip install langgraph>=1.0.0 langchain-core>=0.3.0 pyyaml fastapi uvicorn
-```
-
-**Setup steps:**
-
-1. Install agentmap: pip install agentmap[all]
-1. Initialize configuration: agentmap init-config
-1. Configure LLM providers in agentmap_config.yaml
-1. Run workflow: agentmap run workflow.csv
-
-## Key Files
-
-- `agentmap_config.yaml - Main configuration for LLM providers and paths`
-- `agentmap_config_storage.yaml - Storage configuration for CSV/JSON/Vector DBs`
-- `hello_world.csv - Sample workflow definition`
-
-## Steps
-
-1. Define workflow in CSV format specifying graph nodes, agent types, and data flow between them
-2. Configure LLM providers and storage backends in the agentmap configuration files
-3. Execute the workflow using the agentmap CLI or Python API
-
-## Implementation Details
-
-```python
-CSV format: graph_name,node_name,agent_type,next_node,on_failure,prompt,input_fields,output_field
-```
-
-```python
-CLI command: agentmap run hello_world.csv --pretty
-```
-
-## Inputs
-
-- CSV workflow files 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 configuration for CSV/JSON/Vector DBs in agentmap_config_storage.yaml
-
-## Outputs
-
-- Executed workflow with agent decisions and state transitions
-- Traced execution path through the graph nodes
-- Logged agent interactions and output fields populated
-
-## Failure Modes
-
-- Invalid CSV format causing parse errors during workflow loading
-- Missing or misconfigured LLM provider credentials leading to runtime failures
-- Incorrect agent configuration (e.g., missing input_fields) causing processing errors
-
-## Source
-
-Extracted from: [https://github.com/jwwelbor/AgentMap.git](https://github.com/jwwelbor/AgentMap.git)
-Confidence: 0.95

skills/langgraph-csv-workflow/commands.md

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

skills/langgraph-csv-workflow/examples.md

@@ -1,10 +0,0 @@
-# Examples: langgraph-csv-workflow
-
-## Usage Example
-
-```python
-# How to use this skill
-# Inputs: CSV workflow files 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 configuration for CSV/JSON/Vector DBs in agentmap_config_storage.yaml
-# Process: Define workflow in CSV format specifying graph nodes, agent types, and data flow between them → Configure LLM providers and storage backends in the agentmap configuration files → Execute the workflow using the agentmap CLI or Python API
-# Outputs: Executed workflow with agent decisions and state transitions, Traced execution path through the graph nodes, Logged agent interactions and output fields populated
-```

skills/langgraph-csv-workflow/metadata.json

@@ -1,29 +0,0 @@
-{
-  "name": "langgraph-csv-workflow",
-  "version": "1.0.0",
-  "goal": "Transform simple CSV files into powerful AI agent workflows using LangGraph orchestration",
-  "inputs": [
-    "CSV workflow files 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 configuration for CSV/JSON/Vector DBs in agentmap_config_storage.yaml"
-  ],
-  "steps": [
-    "Define workflow in CSV format specifying graph nodes, agent types, and data flow between them",
-    "Configure LLM providers and storage backends in the agentmap configuration files",
-    "Execute the workflow using the agentmap CLI or Python API"
-  ],
-  "outputs": [
-    "Executed workflow with agent decisions and state transitions",
-    "Traced execution path through the graph nodes",
-    "Logged agent interactions and output fields populated"
-  ],
-  "failure_modes": [
-    "Invalid CSV format causing parse errors during workflow loading",
-    "Missing or misconfigured LLM provider credentials leading to runtime failures",
-    "Incorrect agent configuration (e.g., missing input_fields) causing processing errors"
-  ],
-  "confidence": 0.95,
-  "explanation": "AgentMap provides a declarative pattern where workflows are defined in CSV files with specific columns describing graph nodes, agent types, and data flow. This pattern can be adapted to create multi-agent systems with LangGraph, supporting various LLM providers and storage backends. The workflow is reusable across different use cases by simply modifying the CSV definition.",
-  "source_repo": "https://github.com/jwwelbor/AgentMap.git",
-  "score": 1.0
-}