Add Skill: langgraph-csv-workflow

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

skills/agent-builder-workflow/SKILL.md

@@ -1,110 +0,0 @@
----
-name: agent-builder-workflow
-version: 1.0.0
-description: Build no-code AI agents that connect to enterprise knowledge sources,
-  perform unified search and deep research, and generate explainable answers with
-  citations
-inputs:
-- Task description and agent objectives (e.g., answer Q&A, research specific topics,
-  generate reports)
-- Knowledge sources (documents, databases, enterprise systems) to connect to
-- Retrieval strategy configuration (graph-based knowledge graph vs. vector search)
-- Output requirements (citation format, response structure, code execution needs)
-steps:
-- 'Step 1: Define agent task and objectives - Specify what the agent should do (e.g.,
-  answer a question, perform deep research on a topic, generate a report with citations)'
-- 'Step 2: Configure knowledge sources - Connect to enterprise documents, databases,
-  or external systems that will serve as the agent''s context'
-- 'Step 3: Build LangGraph chain - Create a workflow chain using LangGraph that combines
-  retrieval (graph/vector) and LLM response generation with citation capabilities'
-- 'Step 4: Execute agent - Run the LangGraph chain to process the task and generate
-  responses with grounded citations'
-- 'Step 5: (Optional) Code execution sandbox - If the agent needs to generate or execute
-  code, deploy it to a safe sandbox environment for verification'
-outputs:
-- Agent execution logs showing retrieval steps and LLM responses
-- Grounded answers with block citations to source documents
-- Generated reports or artifacts (if code execution was performed)
-- Structured task completion status and results
-tags: []
-metadata:
-  source_repo: https://github.com/pipeshub-ai/pipeshub-ai.git
-  extracted_at: ''
-  confidence: 0.95
----
-
-# agent-builder-workflow
-
-Build no-code AI agents that connect to enterprise knowledge sources, perform unified search and deep research, and generate explainable answers with citations
-
-## Setup
-
-**Dependencies:**
-
-```text
-pip install langchain langgraph serper-dev-tool qdrant-client redis fastapi pydantic
-```
-
-**Setup steps:**
-
-1. Install dependencies: pip install langchain langgraph serper-dev-tool qdrant-client redis fastapi
-1. Configure knowledge sources in .env (graph DB connection, vector DB, document paths)
-1. Define agent task in agent_builder.py with objectives and retrieval strategy
-1. Run agent chain: python agent_chain.py --task "research_quantum_computing"
-1. For code execution: add sandbox step to agent_chain.py with code generation and safe execution
-
-## Key Files
-
-- `pipeshub-ai/backend/agent_chain.py - LangGraph chain definition for agent workflows`
-- `pipeshub-ai/backend/retrieval_pipeline.py - Knowledge graph and vector search implementation`
-- `pipeshub-ai/workflows/agent_builder.py - No-code agent creation interface`
-- `pipeshub-ai/workflows/citation_generator.py - Block citation generation from retrieved sources`
-
-## Steps
-
-1. Step 1: Define agent task and objectives - Specify what the agent should do (e.g., answer a question, perform deep research on a topic, generate a report with citations)
-2. Step 2: Configure knowledge sources - Connect to enterprise documents, databases, or external systems that will serve as the agent's context
-3. Step 3: Build LangGraph chain - Create a workflow chain using LangGraph that combines retrieval (graph/vector) and LLM response generation with citation capabilities
-4. Step 4: Execute agent - Run the LangGraph chain to process the task and generate responses with grounded citations
-5. Step 5: (Optional) Code execution sandbox - If the agent needs to generate or execute code, deploy it to a safe sandbox environment for verification
-
-## Implementation Details
-
-```python
-LangGraph chain with retrieval (graph/vector) and LLM response stages
-```
-
-```python
-Knowledge graph construction from enterprise documents
-```
-
-```python
-Citation formatting using block references to source documents
-```
-
-## Inputs
-
-- Task description and agent objectives (e.g., answer Q&A, research specific topics, generate reports)
-- Knowledge sources (documents, databases, enterprise systems) to connect to
-- Retrieval strategy configuration (graph-based knowledge graph vs. vector search)
-- Output requirements (citation format, response structure, code execution needs)
-
-## Outputs
-
-- Agent execution logs showing retrieval steps and LLM responses
-- Grounded answers with block citations to source documents
-- Generated reports or artifacts (if code execution was performed)
-- Structured task completion status and results
-
-## Failure Modes
-
-- Insufficient retrieval results due to poor knowledge graph connectivity or vector embedding quality
-- Permission errors when accessing enterprise knowledge sources
-- LLM context window overflow when generating long explanations with citations
-- Sandbox execution failures for code generation or execution tasks
-- Timeout errors during multi-step agent chain execution
-
-## Source
-
-Extracted from: [https://github.com/pipeshub-ai/pipeshub-ai.git](https://github.com/pipeshub-ai/pipeshub-ai.git)
-Confidence: 0.95

skills/agent-builder-workflow/commands.md

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

skills/agent-builder-workflow/examples.md

@@ -1,10 +0,0 @@
-# Examples: agent-builder-workflow
-
-## Usage Example
-
-```python
-# How to use this skill
-# Inputs: Task description and agent objectives (e.g., answer Q&A, research specific topics, generate reports), Knowledge sources (documents, databases, enterprise systems) to connect to, Retrieval strategy configuration (graph-based knowledge graph vs. vector search), Output requirements (citation format, response structure, code execution needs)
-# Process: Step 1: Define agent task and objectives - Specify what the agent should do (e.g., answer a question, perform deep research on a topic, generate a report with citations) → Step 2: Configure knowledge sources - Connect to enterprise documents, databases, or external systems that will serve as the agent's context → Step 3: Build LangGraph chain - Create a workflow chain using LangGraph that combines retrieval (graph/vector) and LLM response generation with citation capabilities
-# Outputs: Agent execution logs showing retrieval steps and LLM responses, Grounded answers with block citations to source documents, Generated reports or artifacts (if code execution was performed), Structured task completion status and results
-```

skills/agent-builder-workflow/metadata.json

@@ -1,35 +0,0 @@
-{
-  "name": "agent-builder-workflow",
-  "version": "1.0.0",
-  "goal": "Build no-code AI agents that connect to enterprise knowledge sources, perform unified search and deep research, and generate explainable answers with citations",
-  "inputs": [
-    "Task description and agent objectives (e.g., answer Q&A, research specific topics, generate reports)",
-    "Knowledge sources (documents, databases, enterprise systems) to connect to",
-    "Retrieval strategy configuration (graph-based knowledge graph vs. vector search)",
-    "Output requirements (citation format, response structure, code execution needs)"
-  ],
-  "steps": [
-    "Step 1: Define agent task and objectives - Specify what the agent should do (e.g., answer a question, perform deep research on a topic, generate a report with citations)",
-    "Step 2: Configure knowledge sources - Connect to enterprise documents, databases, or external systems that will serve as the agent's context",
-    "Step 3: Build LangGraph chain - Create a workflow chain using LangGraph that combines retrieval (graph/vector) and LLM response generation with citation capabilities",
-    "Step 4: Execute agent - Run the LangGraph chain to process the task and generate responses with grounded citations",
-    "Step 5: (Optional) Code execution sandbox - If the agent needs to generate or execute code, deploy it to a safe sandbox environment for verification"
-  ],
-  "outputs": [
-    "Agent execution logs showing retrieval steps and LLM responses",
-    "Grounded answers with block citations to source documents",
-    "Generated reports or artifacts (if code execution was performed)",
-    "Structured task completion status and results"
-  ],
-  "failure_modes": [
-    "Insufficient retrieval results due to poor knowledge graph connectivity or vector embedding quality",
-    "Permission errors when accessing enterprise knowledge sources",
-    "LLM context window overflow when generating long explanations with citations",
-    "Sandbox execution failures for code generation or execution tasks",
-    "Timeout errors during multi-step agent chain execution"
-  ],
-  "confidence": 0.95,
-  "explanation": "PipesHub provides a reusable agent builder workflow that combines LangGraph orchestration with graph-based and vector-based retrieval. This pattern can be adapted to any enterprise context where AI agents need to search across multiple knowledge sources, generate explainable answers with citations, and optionally execute code in a safe sandbox. The workflow is defined by specific configuration files (LangGraph chain definitions) and follows a standard pattern: task definition \u2192 knowledge source connection \u2192 retrieval strategy \u2192 response generation \u2192 optional code sandbox.",
-  "source_repo": "https://github.com/pipeshub-ai/pipeshub-ai.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: agent-builder-workflow
+# Tests: langgraph-csv-workflow
 
 ## Test Checklist