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: conditional-input-routing
+# Tests: branching-agent-pattern
 
 ## Test Checklist
 

skills/conditional-input-routing/SKILL.md

@@ -1,73 +0,0 @@
----
-name: conditional-input-routing
-version: 1.0.0
-description: Collect user input, classify or branch on its content, and route to appropriate
-  success or failure handling paths to produce a final result
-inputs:
-- name: request
-  type: string
-  description: User-provided input or request to be evaluated
-steps:
-- name: Start
-  action: input agent captures initial request into state
-  agent_type: input
-  output_field: request
-- name: Classify
-  action: branching agent evaluates request and routes to next_node or on_failure
-  agent_type: branching
-  input_fields: request
-  output_field: decision
-  next_node: Approve
-  on_failure: Reject
-- name: Approve
-  action: default agent processes approved request and sets result
-  agent_type: default
-  input_fields: request
-  output_field: result
-  prompt: 'Request approved: {request}'
-- name: Reject
-  action: default agent processes rejected request and sets result
-  agent_type: default
-  input_fields: request
-  output_field: result
-  prompt: 'Request rejected: {request}'
-outputs:
-- name: result
-  type: string
-  description: Final output from either the approve or reject branch
-tags: []
-metadata:
-  source_repo: https://github.com/jwwelbor/AgentMap.git
-  extracted_at: ''
-  confidence: 0.9
----
-
-# conditional-input-routing
-
-Collect user input, classify or branch on its content, and route to appropriate success or failure handling paths to produce a final result
-
-## Steps
-
-1. {'name': 'Start', 'action': 'input agent captures initial request into state', 'agent_type': 'input', 'output_field': 'request'}
-2. {'name': 'Classify', 'action': 'branching agent evaluates request and routes to next_node or on_failure', 'agent_type': 'branching', 'input_fields': 'request', 'output_field': 'decision', 'next_node': 'Approve', 'on_failure': 'Reject'}
-3. {'name': 'Approve', 'action': 'default agent processes approved request and sets result', 'agent_type': 'default', 'input_fields': 'request', 'output_field': 'result', 'prompt': 'Request approved: {request}'}
-4. {'name': 'Reject', 'action': 'default agent processes rejected request and sets result', 'agent_type': 'default', 'input_fields': 'request', 'output_field': 'result', 'prompt': 'Request rejected: {request}'}
-
-## Inputs
-
-- {'name': 'request', 'type': 'string', 'description': 'User-provided input or request to be evaluated'}
-
-## Outputs
-
-- {'name': 'result', 'type': 'string', 'description': 'Final output from either the approve or reject branch'}
-
-## Failure Modes
-
-- Input node fails to capture request (handled by on_failure if defined)
-- Branching condition not met and no on_failure path defined
-- Missing input_fields in state causing agent execution error
-
-## Source
-
-Extracted from: [https://github.com/jwwelbor/AgentMap.git](https://github.com/jwwelbor/AgentMap.git)
-Confidence: 0.9

skills/conditional-input-routing/commands.md

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

skills/conditional-input-routing/examples.md

@@ -1,10 +0,0 @@
-# Examples: conditional-input-routing
-
-## Usage Example
-
-```python
-# How to use this skill
-# Inputs: {'name': 'request', 'type': 'string', 'description': 'User-provided input or request to be evaluated'}
-# Process: {'name': 'Start', 'action': 'input agent captures initial request into state', 'agent_type': 'input', 'output_field': 'request'} → {'name': 'Classify', 'action': 'branching agent evaluates request and routes to next_node or on_failure', 'agent_type': 'branching', 'input_fields': 'request', 'output_field': 'decision', 'next_node': 'Approve', 'on_failure': 'Reject'} → {'name': 'Approve', 'action': 'default agent processes approved request and sets result', 'agent_type': 'default', 'input_fields': 'request', 'output_field': 'result', 'prompt': 'Request approved: {request}'}
-# Outputs: {'name': 'result', 'type': 'string', 'description': 'Final output from either the approve or reject branch'}
-```

skills/conditional-input-routing/metadata.json

@@ -1,61 +0,0 @@
-{
-  "name": "conditional-input-routing",
-  "version": "1.0.0",
-  "goal": "Collect user input, classify or branch on its content, and route to appropriate success or failure handling paths to produce a final result",
-  "inputs": [
-    {
-      "name": "request",
-      "type": "string",
-      "description": "User-provided input or request to be evaluated"
-    }
-  ],
-  "steps": [
-    {
-      "name": "Start",
-      "action": "input agent captures initial request into state",
-      "agent_type": "input",
-      "output_field": "request"
-    },
-    {
-      "name": "Classify",
-      "action": "branching agent evaluates request and routes to next_node or on_failure",
-      "agent_type": "branching",
-      "input_fields": "request",
-      "output_field": "decision",
-      "next_node": "Approve",
-      "on_failure": "Reject"
-    },
-    {
-      "name": "Approve",
-      "action": "default agent processes approved request and sets result",
-      "agent_type": "default",
-      "input_fields": "request",
-      "output_field": "result",
-      "prompt": "Request approved: {request}"
-    },
-    {
-      "name": "Reject",
-      "action": "default agent processes rejected request and sets result",
-      "agent_type": "default",
-      "input_fields": "request",
-      "output_field": "result",
-      "prompt": "Request rejected: {request}"
-    }
-  ],
-  "outputs": [
-    {
-      "name": "result",
-      "type": "string",
-      "description": "Final output from either the approve or reject branch"
-    }
-  ],
-  "failure_modes": [
-    "Input node fails to capture request (handled by on_failure if defined)",
-    "Branching condition not met and no on_failure path defined",
-    "Missing input_fields in state causing agent execution error"
-  ],
-  "confidence": 0.9,
-  "explanation": "Extracted from AgentMap's documented conditional workflow example (ReviewFlow). This CSV-declared pattern of input to branching to dual-path handling is reusable for any approval, triage, or routing scenario without writing orchestration code.",
-  "source_repo": "https://github.com/jwwelbor/AgentMap.git",
-  "score": 1.0
-}