Add Skill: langgraph-csv-workflow

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

skills/conditional-request-routing-workflow/SKILL.md

@@ -1,81 +0,0 @@
----
-name: conditional-request-routing-workflow
-version: 1.0.0
-description: Collect a request, classify it via branching logic, and route to an approval
-  or rejection handler to produce a final result
-inputs:
-- name: request
-  type: string
-  description: The input text or request to be evaluated and routed
-steps:
-- node: Start
-  agent_type: input
-  description: Prompt user and capture the request into state field 'request'
-  next: Classify
-- node: Classify
-  agent_type: branching
-  description: Evaluate the request and set 'decision' field, routing to Approve on
-    success or Reject on failure
-  input_fields:
-  - request
-  output_field: decision
-  next_node: Approve
-  on_failure: Reject
-- node: Approve
-  agent_type: default
-  description: Format and output an approval message containing the request
-  input_fields:
-  - request
-  output_field: result
-  prompt: 'Request approved: {request}'
-- node: Reject
-  agent_type: default
-  description: Format and output a rejection message containing the request
-  input_fields:
-  - request
-  output_field: result
-  prompt: 'Request rejected: {request}'
-outputs:
-- name: result
-  type: string
-  description: Final formatted message indicating the outcome (approved or rejected)
-- name: decision
-  type: string
-  description: Routing decision produced by the branching node
-tags: []
-metadata:
-  source_repo: https://github.com/jwwelbor/AgentMap.git
-  extracted_at: ''
-  confidence: 0.92
----
-
-# conditional-request-routing-workflow
-
-Collect a request, classify it via branching logic, and route to an approval or rejection handler to produce a final result
-
-## Steps
-
-1. {'node': 'Start', 'agent_type': 'input', 'description': "Prompt user and capture the request into state field 'request'", 'next': 'Classify'}
-2. {'node': 'Classify', 'agent_type': 'branching', 'description': "Evaluate the request and set 'decision' field, routing to Approve on success or Reject on failure", 'input_fields': ['request'], 'output_field': 'decision', 'next_node': 'Approve', 'on_failure': 'Reject'}
-3. {'node': 'Approve', 'agent_type': 'default', 'description': 'Format and output an approval message containing the request', 'input_fields': ['request'], 'output_field': 'result', 'prompt': 'Request approved: {request}'}
-4. {'node': 'Reject', 'agent_type': 'default', 'description': 'Format and output a rejection message containing the request', 'input_fields': ['request'], 'output_field': 'result', 'prompt': 'Request rejected: {request}'}
-
-## Inputs
-
-- {'name': 'request', 'type': 'string', 'description': 'The input text or request to be evaluated and routed'}
-
-## Outputs
-
-- {'name': 'result', 'type': 'string', 'description': 'Final formatted message indicating the outcome (approved or rejected)'}
-- {'name': 'decision', 'type': 'string', 'description': 'Routing decision produced by the branching node'}
-
-## Failure Modes
-
-- Empty or missing request input prevents meaningful classification
-- Branching node fails to resolve a valid route and defaults to rejection path
-- Prompt template variable missing causes malformed output
-
-## Source
-
-Extracted from: [https://github.com/jwwelbor/AgentMap.git](https://github.com/jwwelbor/AgentMap.git)
-Confidence: 0.92

skills/conditional-request-routing-workflow/commands.md

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

skills/conditional-request-routing-workflow/examples.md

@@ -1,10 +0,0 @@
-# Examples: conditional-request-routing-workflow
-
-## Usage Example
-
-```python
-# How to use this skill
-# Inputs: {'name': 'request', 'type': 'string', 'description': 'The input text or request to be evaluated and routed'}
-# Process: {'node': 'Start', 'agent_type': 'input', 'description': "Prompt user and capture the request into state field 'request'", 'next': 'Classify'} → {'node': 'Classify', 'agent_type': 'branching', 'description': "Evaluate the request and set 'decision' field, routing to Approve on success or Reject on failure", 'input_fields': ['request'], 'output_field': 'decision', 'next_node': 'Approve', 'on_failure': 'Reject'} → {'node': 'Approve', 'agent_type': 'default', 'description': 'Format and output an approval message containing the request', 'input_fields': ['request'], 'output_field': 'result', 'prompt': 'Request approved: {request}'}
-# Outputs: {'name': 'result', 'type': 'string', 'description': 'Final formatted message indicating the outcome (approved or rejected)'}, {'name': 'decision', 'type': 'string', 'description': 'Routing decision produced by the branching node'}
-```

skills/conditional-request-routing-workflow/metadata.json

@@ -1,72 +0,0 @@
-{
-  "name": "conditional-request-routing-workflow",
-  "version": "1.0.0",
-  "goal": "Collect a request, classify it via branching logic, and route to an approval or rejection handler to produce a final result",
-  "inputs": [
-    {
-      "name": "request",
-      "type": "string",
-      "description": "The input text or request to be evaluated and routed"
-    }
-  ],
-  "steps": [
-    {
-      "node": "Start",
-      "agent_type": "input",
-      "description": "Prompt user and capture the request into state field 'request'",
-      "next": "Classify"
-    },
-    {
-      "node": "Classify",
-      "agent_type": "branching",
-      "description": "Evaluate the request and set 'decision' field, routing to Approve on success or Reject on failure",
-      "input_fields": [
-        "request"
-      ],
-      "output_field": "decision",
-      "next_node": "Approve",
-      "on_failure": "Reject"
-    },
-    {
-      "node": "Approve",
-      "agent_type": "default",
-      "description": "Format and output an approval message containing the request",
-      "input_fields": [
-        "request"
-      ],
-      "output_field": "result",
-      "prompt": "Request approved: {request}"
-    },
-    {
-      "node": "Reject",
-      "agent_type": "default",
-      "description": "Format and output a rejection message containing the request",
-      "input_fields": [
-        "request"
-      ],
-      "output_field": "result",
-      "prompt": "Request rejected: {request}"
-    }
-  ],
-  "outputs": [
-    {
-      "name": "result",
-      "type": "string",
-      "description": "Final formatted message indicating the outcome (approved or rejected)"
-    },
-    {
-      "name": "decision",
-      "type": "string",
-      "description": "Routing decision produced by the branching node"
-    }
-  ],
-  "failure_modes": [
-    "Empty or missing request input prevents meaningful classification",
-    "Branching node fails to resolve a valid route and defaults to rejection path",
-    "Prompt template variable missing causes malformed output"
-  ],
-  "confidence": 0.92,
-  "explanation": "Extracted from the ReviewFlow CSV example in the AgentMap README. This is a declarative, CSV-defined LangGraph workflow demonstrating the reusable pattern of input -> branching classification -> conditional handling paths. It can be generalized for ticket triage, content moderation, or any route-by-condition use case.",
-  "source_repo": "https://github.com/jwwelbor/AgentMap.git",
-  "score": 1.0
-}

skills/langgraph-csv-workflow/SKILL.md

@@ -0,0 +1,89 @@
+---
+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

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

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

@@ -0,0 +1,29 @@
+{
+  "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
+}

skills/langgraph-csv-workflow/tests.md

@@ -1,4 +1,4 @@
-# Tests: conditional-request-routing-workflow
+# Tests: langgraph-csv-workflow
 
 ## Test Checklist