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: langgraph-multi-agent-sequential
+# Tests: langgraph-csv-workflow
 
 ## Test Checklist
 

skills/langgraph-multi-agent-sequential/SKILL.md

@@ -1,99 +0,0 @@
----
-name: langgraph-multi-agent-sequential
-version: 1.0.0
-description: Orchestrate a sequence of specialized agents to perform multi-step tasks
-  like research, data processing, and final output generation
-inputs:
-- BedrockModel with temperature=0.3, top_p=0.8
-- Researcher agent with system prompt for destination research (places, history, accommodations,
-  food, web pages)
-- Travel Guide Generator agent with system prompt for structuring travel guides into
-  labeled sections
-- Writer agent with system prompt for formatting professional client responses
-steps:
-- Researcher agent gathers raw destination facts (top 5 attractions, historical facts,
-  best areas, local foods, suggested web pages) using BedrockModel
-- Travel Guide Generator agent structures the raw facts into a comprehensive travel
-  guide with clearly labeled sections
-- Writer agent formats the structured guide into a professional client-facing response
-  with the full guide and highlighted web pages
-outputs:
-- Raw research data (JSON string containing destination facts and categories)
-- Structured travel guide content (markdown with sections for attractions, history,
-  accommodations, cuisine, and web pages)
-- Final client response (formatted travel guide ready for delivery)
-tags: []
-metadata:
-  source_repo: https://github.com/omerbsezer/Fast-LLM-Agent-MCP.git
-  extracted_at: ''
-  confidence: 0.95
----
-
-# langgraph-multi-agent-sequential
-
-Orchestrate a sequence of specialized agents to perform multi-step tasks like research, data processing, and final output generation
-
-## Setup
-
-**Dependencies:**
-
-```text
-pip install langchain langgraph bedrock-model pydantic
-```
-
-**Setup steps:**
-
-1. Install langchain and langgraph packages
-1. Configure BedrockModel with temperature=0.3 and top_p=0.8
-1. Create three Agent instances with appropriate system prompts and tools
-1. Deploy the FastAPI server with the LangGraph application
-
-## Key Files
-
-- `agents/langchain_langgraph/08-langgraph-multi-agents-sequential-pattern/agent.py`
-- `agents/langchain_langgraph/08-langgraph-multi-agents-sequential-pattern/app.py`
-
-## Steps
-
-1. Researcher agent gathers raw destination facts (top 5 attractions, historical facts, best areas, local foods, suggested web pages) using BedrockModel
-2. Travel Guide Generator agent structures the raw facts into a comprehensive travel guide with clearly labeled sections
-3. Writer agent formats the structured guide into a professional client-facing response with the full guide and highlighted web pages
-
-## Implementation Details
-
-```python
-Researcher agent with system_prompt for destination research and tools=[calculator, current_time]
-```
-
-```python
-Travel Guide Generator agent with system_prompt requiring structured sections (attractions, history, accommodations, cuisine, web pages)
-```
-
-```python
-Writer agent with system_prompt for client-facing response formatting
-```
-
-## Inputs
-
-- BedrockModel with temperature=0.3, top_p=0.8
-- Researcher agent with system prompt for destination research (places, history, accommodations, food, web pages)
-- Travel Guide Generator agent with system prompt for structuring travel guides into labeled sections
-- Writer agent with system prompt for formatting professional client responses
-
-## Outputs
-
-- Raw research data (JSON string containing destination facts and categories)
-- Structured travel guide content (markdown with sections for attractions, history, accommodations, cuisine, and web pages)
-- Final client response (formatted travel guide ready for delivery)
-
-## Failure Modes
-
-- Researcher agent fails to retrieve sufficient information, causing downstream agents to receive incomplete or empty data
-- Travel Guide Generator fails to structure data properly, resulting in unorganized or malformed output
-- Writer agent fails to format the final response correctly, producing garbled or incomplete output
-- Model timeouts or errors in any agent step causing the entire pipeline to fail
-
-## Source
-
-Extracted from: [https://github.com/omerbsezer/Fast-LLM-Agent-MCP.git](https://github.com/omerbsezer/Fast-LLM-Agent-MCP.git)
-Confidence: 0.95

skills/langgraph-multi-agent-sequential/commands.md

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

skills/langgraph-multi-agent-sequential/examples.md

@@ -1,10 +0,0 @@
-# Examples: langgraph-multi-agent-sequential
-
-## Usage Example
-
-```python
-# How to use this skill
-# Inputs: BedrockModel with temperature=0.3, top_p=0.8, Researcher agent with system prompt for destination research (places, history, accommodations, food, web pages), Travel Guide Generator agent with system prompt for structuring travel guides into labeled sections, Writer agent with system prompt for formatting professional client responses
-# Process: Researcher agent gathers raw destination facts (top 5 attractions, historical facts, best areas, local foods, suggested web pages) using BedrockModel → Travel Guide Generator agent structures the raw facts into a comprehensive travel guide with clearly labeled sections → Writer agent formats the structured guide into a professional client-facing response with the full guide and highlighted web pages
-# Outputs: Raw research data (JSON string containing destination facts and categories), Structured travel guide content (markdown with sections for attractions, history, accommodations, cuisine, and web pages), Final client response (formatted travel guide ready for delivery)
-```

skills/langgraph-multi-agent-sequential/metadata.json

@@ -1,31 +0,0 @@
-{
-  "name": "langgraph-multi-agent-sequential",
-  "version": "1.0.0",
-  "goal": "Orchestrate a sequence of specialized agents to perform multi-step tasks like research, data processing, and final output generation",
-  "inputs": [
-    "BedrockModel with temperature=0.3, top_p=0.8",
-    "Researcher agent with system prompt for destination research (places, history, accommodations, food, web pages)",
-    "Travel Guide Generator agent with system prompt for structuring travel guides into labeled sections",
-    "Writer agent with system prompt for formatting professional client responses"
-  ],
-  "steps": [
-    "Researcher agent gathers raw destination facts (top 5 attractions, historical facts, best areas, local foods, suggested web pages) using BedrockModel",
-    "Travel Guide Generator agent structures the raw facts into a comprehensive travel guide with clearly labeled sections",
-    "Writer agent formats the structured guide into a professional client-facing response with the full guide and highlighted web pages"
-  ],
-  "outputs": [
-    "Raw research data (JSON string containing destination facts and categories)",
-    "Structured travel guide content (markdown with sections for attractions, history, accommodations, cuisine, and web pages)",
-    "Final client response (formatted travel guide ready for delivery)"
-  ],
-  "failure_modes": [
-    "Researcher agent fails to retrieve sufficient information, causing downstream agents to receive incomplete or empty data",
-    "Travel Guide Generator fails to structure data properly, resulting in unorganized or malformed output",
-    "Writer agent fails to format the final response correctly, producing garbled or incomplete output",
-    "Model timeouts or errors in any agent step causing the entire pipeline to fail"
-  ],
-  "confidence": 0.95,
-  "explanation": "This workflow demonstrates a reusable LangGraph pattern where three specialized agents work sequentially: a Researcher agent gathers raw destination facts, a Travel Guide Generator agent structures those facts into a travel guide, and a Writer agent formats the final output for clients. The pattern is modular and can be adapted to other multi-step tasks by swapping agent roles and prompts while maintaining the same pipeline structure.",
-  "source_repo": "https://github.com/omerbsezer/Fast-LLM-Agent-MCP.git",
-  "score": 1.0
-}