Add Skill: langgraph-csv-workflow

Extracted from: 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

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+# 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

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+# 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

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+{
+  "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: text-concatenation
+# Tests: langgraph-csv-workflow
 
 ## Test Checklist
 

skills/text-concatenation/SKILL.md

@@ -1,61 +0,0 @@
----
-name: text-concatenation
-version: 1.0.0
-description: Concatenate two text inputs using Blacknode node graph
-inputs:
-- text_a
-- text_b
-steps:
-- Create a Blacknode Graph instance
-- Add a Text node with param value set to text_a
-- Add a second Text node with param value set to text_b
-- Add a Concat node that accepts inputs 'a' and 'b' and outputs 'value'
-- Add an Output node with input port 'value'
-- Connect first Text node 'value' port to Concat 'a' port
-- Connect second Text node 'value' port to Concat 'b' port
-- Connect Concat 'value' port to Output 'value' port
-- Invoke graph cook on Output 'value' to evaluate and return result
-outputs:
-- concatenated_text
-tags: []
-metadata:
-  source_repo: https://github.com/temiroff/Blacknode.git
-  extracted_at: ''
-  confidence: 0.9
----
-
-# text-concatenation
-
-Concatenate two text inputs using Blacknode node graph
-
-## Steps
-
-1. Create a Blacknode Graph instance
-2. Add a Text node with param value set to text_a
-3. Add a second Text node with param value set to text_b
-4. Add a Concat node that accepts inputs 'a' and 'b' and outputs 'value'
-5. Add an Output node with input port 'value'
-6. Connect first Text node 'value' port to Concat 'a' port
-7. Connect second Text node 'value' port to Concat 'b' port
-8. Connect Concat 'value' port to Output 'value' port
-9. Invoke graph cook on Output 'value' to evaluate and return result
-
-## Inputs
-
-- text_a
-- text_b
-
-## Outputs
-
-- concatenated_text
-
-## Failure Modes
-
-- Port name mismatch causes connection error
-- Missing runtime or graph not initialized
-- Cook on nonexistent node returns error
-
-## Source
-
-Extracted from: [https://github.com/temiroff/Blacknode.git](https://github.com/temiroff/Blacknode.git)
-Confidence: 0.9

skills/text-concatenation/commands.md

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

skills/text-concatenation/examples.md

@@ -1,10 +0,0 @@
-# Examples: text-concatenation
-
-## Usage Example
-
-```python
-# How to use this skill
-# Inputs: text_a, text_b
-# Process: Create a Blacknode Graph instance → Add a Text node with param value set to text_a → Add a second Text node with param value set to text_b
-# Outputs: concatenated_text
-```

skills/text-concatenation/metadata.json

@@ -1,32 +0,0 @@
-{
-  "name": "text-concatenation",
-  "version": "1.0.0",
-  "goal": "Concatenate two text inputs using Blacknode node graph",
-  "inputs": [
-    "text_a",
-    "text_b"
-  ],
-  "steps": [
-    "Create a Blacknode Graph instance",
-    "Add a Text node with param value set to text_a",
-    "Add a second Text node with param value set to text_b",
-    "Add a Concat node that accepts inputs 'a' and 'b' and outputs 'value'",
-    "Add an Output node with input port 'value'",
-    "Connect first Text node 'value' port to Concat 'a' port",
-    "Connect second Text node 'value' port to Concat 'b' port",
-    "Connect Concat 'value' port to Output 'value' port",
-    "Invoke graph cook on Output 'value' to evaluate and return result"
-  ],
-  "outputs": [
-    "concatenated_text"
-  ],
-  "failure_modes": [
-    "Port name mismatch causes connection error",
-    "Missing runtime or graph not initialized",
-    "Cook on nonexistent node returns error"
-  ],
-  "confidence": 0.9,
-  "explanation": "The repo contains example converted workflows; converted_text_pipeline.py demonstrates a simple reusable pattern of two source nodes feeding a concatenation node into an output, applicable to any string joining task in Blacknode.",
-  "source_repo": "https://github.com/temiroff/Blacknode.git",
-  "score": 1.0
-}