Add Skill: langgraph-csv-workflow

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

skills/langgraph-csv-workflow/SKILL.md

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+---
+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

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-# Tests: langgraph-explainable-agent
+# Tests: langgraph-csv-workflow
 
 ## Test Checklist
 

skills/langgraph-explainable-agent/SKILL.md

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----
-name: langgraph-explainable-agent
-version: 1.0.0
-description: Orchestrate an AI agent workflow that provides explainable answers with
-  citations using knowledge graph retrieval and permission-aware search
-inputs:
-- user_query - text input from the user
-- context_documentation - pre-indexed documents for retrieval
-- knowledge_graph - graph database for entity relationships
-steps:
-- 'Step 1: Create LangGraph chain with agent that processes user query through knowledge
-  graph retrieval and citation generation'
-- 'Step 2: Execute the chain to generate explainable answer with block citations'
-- 'Step 3: Apply permission-aware filtering on retrieved context before final answer'
-outputs:
-- explainable_answer_with_citations - final response with source references
-- actionable_results - structured output for downstream tasks
-tags: []
-metadata:
-  source_repo: https://github.com/pipeshub-ai/pipeshub-ai.git
-  extracted_at: ''
-  confidence: 0.95
----
-
-# langgraph-explainable-agent
-
-Orchestrate an AI agent workflow that provides explainable answers with citations using knowledge graph retrieval and permission-aware search
-
-## Setup
-
-**Dependencies:**
-
-```text
-pip install langchain langgraph neoelephant pydantic
-```
-
-**Setup steps:**
-
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-
-## Key Files
-
-- `agent.py - main LangGraph chain definition`
-- `workflow_config.yaml - chain configuration`
-
-## Steps
-
-1. Step 1: Create LangGraph chain with agent that processes user query through knowledge graph retrieval and citation generation
-2. Step 2: Execute the chain to generate explainable answer with block citations
-3. Step 3: Apply permission-aware filtering on retrieved context before final answer
-
-## Implementation Details
-
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-
-## Inputs
-
-- user_query - text input from the user
-- context_documentation - pre-indexed documents for retrieval
-- knowledge_graph - graph database for entity relationships
-
-## Outputs
-
-- explainable_answer_with_citations - final response with source references
-- actionable_results - structured output for downstream tasks
-
-## Failure Modes
-
-- Empty knowledge graph causes missing citations
-- Permission denied on source documents blocks retrieval
-- LangGraph chain execution fails due to missing dependencies
-
-## Source
-
-Extracted from: [https://github.com/pipeshub-ai/pipeshub-ai.git](https://github.com/pipeshub-ai/pipeshub-ai.git)
-Confidence: 0.95

skills/langgraph-explainable-agent/commands.md

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-# Commands: langgraph-explainable-agent
-
-## Available Commands
-
-- `/skill langgraph-explainable-agent` — Load this skill
-- `/run langgraph-explainable-agent` — Execute workflow

skills/langgraph-explainable-agent/examples.md

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-# Examples: langgraph-explainable-agent
-
-## Usage Example
-
-```python
-# How to use this skill
-# Inputs: user_query - text input from the user, context_documentation - pre-indexed documents for retrieval, knowledge_graph - graph database for entity relationships
-# Process: Step 1: Create LangGraph chain with agent that processes user query through knowledge graph retrieval and citation generation → Step 2: Execute the chain to generate explainable answer with block citations → Step 3: Apply permission-aware filtering on retrieved context before final answer
-# Outputs: explainable_answer_with_citations - final response with source references, actionable_results - structured output for downstream tasks
-```

skills/langgraph-explainable-agent/metadata.json

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-{
-  "name": "langgraph-explainable-agent",
-  "version": "1.0.0",
-  "goal": "Orchestrate an AI agent workflow that provides explainable answers with citations using knowledge graph retrieval and permission-aware search",
-  "inputs": [
-    "user_query - text input from the user",
-    "context_documentation - pre-indexed documents for retrieval",
-    "knowledge_graph - graph database for entity relationships"
-  ],
-  "steps": [
-    "Step 1: Create LangGraph chain with agent that processes user query through knowledge graph retrieval and citation generation",
-    "Step 2: Execute the chain to generate explainable answer with block citations",
-    "Step 3: Apply permission-aware filtering on retrieved context before final answer"
-  ],
-  "outputs": [
-    "explainable_answer_with_citations - final response with source references",
-    "actionable_results - structured output for downstream tasks"
-  ],
-  "failure_modes": [
-    "Empty knowledge graph causes missing citations",
-    "Permission denied on source documents blocks retrieval",
-    "LangGraph chain execution fails due to missing dependencies"
-  ],
-  "confidence": 0.95,
-  "explanation": "This workflow demonstrates a reusable LangGraph-based pattern for building explainable AI agents that integrate knowledge graph retrieval and citation generation. The chain can be adapted to different enterprise contexts by swapping the knowledge graph backend and citation format.",
-  "source_repo": "https://github.com/pipeshub-ai/pipeshub-ai.git",
-  "score": 1.0
-}