Add Skill: conditional-request-review

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

skills/branching-agent-pattern/SKILL.md

@@ -1,101 +0,0 @@
----
-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

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

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

@@ -1,31 +0,0 @@
-{
-  "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/conditional-request-review/SKILL.md

@@ -0,0 +1,52 @@
+---
+name: conditional-request-review
+version: 1.0.0
+description: Capture a user request, classify it via branching, and produce an approval
+  or rejection result.
+inputs:
+- request
+steps:
+- 'Start: input agent prompts user for request and stores it in state field ''request'''
+- 'Classify: branching agent evaluates ''request'' and routes to Approve on success
+  or Reject on failure, storing ''decision'''
+- 'Approve: default agent formats approval message using ''request'' and stores it
+  in ''result'''
+- 'Reject: default agent formats rejection message using ''request'' and stores it
+  in ''result'''
+outputs:
+- result (string message indicating approval or rejection)
+tags: []
+metadata:
+  source_repo: https://github.com/jwwelbor/AgentMap.git
+  extracted_at: ''
+  confidence: 0.85
+---
+
+# conditional-request-review
+
+Capture a user request, classify it via branching, and produce an approval or rejection result.
+
+## Steps
+
+1. Start: input agent prompts user for request and stores it in state field 'request'
+2. Classify: branching agent evaluates 'request' and routes to Approve on success or Reject on failure, storing 'decision'
+3. Approve: default agent formats approval message using 'request' and stores it in 'result'
+4. Reject: default agent formats rejection message using 'request' and stores it in 'result'
+
+## Inputs
+
+- request
+
+## Outputs
+
+- result (string message indicating approval or rejection)
+
+## Failure Modes
+
+- If branching classification fails, workflow routes to Reject (on_failure)
+- If input not provided, workflow may hang or error depending on runtime
+
+## Source
+
+Extracted from: [https://github.com/jwwelbor/AgentMap.git](https://github.com/jwwelbor/AgentMap.git)
+Confidence: 0.85

skills/conditional-request-review/commands.md

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

skills/conditional-request-review/examples.md

@@ -0,0 +1,10 @@
+# Examples: conditional-request-review
+
+## Usage Example
+
+```python
+# How to use this skill
+# Inputs: request
+# Process: Start: input agent prompts user for request and stores it in state field 'request' → Classify: branching agent evaluates 'request' and routes to Approve on success or Reject on failure, storing 'decision' → Approve: default agent formats approval message using 'request' and stores it in 'result'
+# Outputs: result (string message indicating approval or rejection)
+```

skills/conditional-request-review/metadata.json

@@ -0,0 +1,25 @@
+{
+  "name": "conditional-request-review",
+  "version": "1.0.0",
+  "goal": "Capture a user request, classify it via branching, and produce an approval or rejection result.",
+  "inputs": [
+    "request"
+  ],
+  "steps": [
+    "Start: input agent prompts user for request and stores it in state field 'request'",
+    "Classify: branching agent evaluates 'request' and routes to Approve on success or Reject on failure, storing 'decision'",
+    "Approve: default agent formats approval message using 'request' and stores it in 'result'",
+    "Reject: default agent formats rejection message using 'request' and stores it in 'result'"
+  ],
+  "outputs": [
+    "result (string message indicating approval or rejection)"
+  ],
+  "failure_modes": [
+    "If branching classification fails, workflow routes to Reject (on_failure)",
+    "If input not provided, workflow may hang or error depending on runtime"
+  ],
+  "confidence": 0.85,
+  "explanation": "Workflow extracted from AgentMap README example 'ReviewFlow' CSV. It is a generic conditional routing pattern usable for any binary decision process.",
+  "source_repo": "https://github.com/jwwelbor/AgentMap.git",
+  "score": 1.0
+}

skills/conditional-request-review/tests.md

@@ -1,4 +1,4 @@
-# Tests: branching-agent-pattern
+# Tests: conditional-request-review
 
 ## Test Checklist