Add Skill: branching-agent-pattern

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/branching-agent-pattern/SKILL.md

@@ -0,0 +1,101 @@
+---
+name: branching-agent-pattern
+version: 1.0.0
+description: Define and execute AI agent workflows using CSV-based declarative definitions
+  with configurable branching logic
+inputs:
+- 'CSV workflow files defining agent graphs 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 backend configuration in agentmap_config_storage.yaml
+steps:
+- Define workflow graph in CSV with nodes representing agent steps and their connections
+  (next_node, on_failure)
+- Configure BranchingAgent with customizable success/failure values and fallback fields
+  in the context dictionary
+- Initialize the agent runtime with ensure_initialized() and configure execution tracking
+  and state adapter services
+- Execute the workflow using agentmap run with appropriate inputs and monitor the
+  execution trace
+outputs:
+- Executed workflow with results stored in the specified output_field
+- Detailed execution trace showing success/failure decisions at each branching point
+- Updated workflow state persisted in the configured storage backend
+tags: []
+metadata:
+  source_repo: https://github.com/jwwelbor/AgentMap.git
+  extracted_at: ''
+  confidence: 0.95
+---
+
+# branching-agent-pattern
+
+Define and execute AI agent workflows using CSV-based declarative definitions with configurable branching logic
+
+## Setup
+
+**Dependencies:**
+
+```text
+pip install langgraph>=1.0.0 langchain-core>=0.3.0 pyyaml>=6.0.0 fastapi>=0.111.0 uvicorn>=0.34.3
+```
+
+**Setup steps:**
+
+1. Install AgentMap: pip install agentmap[all]
+1. Configure llm providers in agentmap_config.yaml (OpenAI, Anthropic, Google models)
+1. Create CSV workflow files with graph definitions
+1. Initialize runtime with ensure_initialized()
+1. Run workflow with agentmap run <csv_file> --pretty
+
+## Key Files
+
+- `agentmap_config.yaml - Main configuration with LLM and storage settings`
+- `agentmap_config_storage.yaml - Storage backend configuration`
+- `hello_world.csv - Sample workflow demonstrating basic agent chain`
+- `examples/host_integration/custom_agents.py - Custom agent implementations with host service integration`
+
+## Steps
+
+1. Define workflow graph in CSV with nodes representing agent steps and their connections (next_node, on_failure)
+2. Configure BranchingAgent with customizable success/failure values and fallback fields in the context dictionary
+3. Initialize the agent runtime with ensure_initialized() and configure execution tracking and state adapter services
+4. Execute the workflow using agentmap run with appropriate inputs and monitor the execution trace
+
+## Implementation Details
+
+```python
+CSV format: graph_name,node_name,agent_type,next_node,on_failure,prompt,input_fields,output_field
+```
+
+```python
+BranchingAgent context example: {'input_fields': ['success'], 'output_field': 'result', 'success_values': ['PASSED', 'COMPLETED']}
+```
+
+```python
+Execution command: agentmap run hello_world.csv --pretty
+```
+
+## Inputs
+
+- CSV workflow files defining agent graphs 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 backend configuration in agentmap_config_storage.yaml
+
+## Outputs
+
+- Executed workflow with results stored in the specified output_field
+- Detailed execution trace showing success/failure decisions at each branching point
+- Updated workflow state persisted in the configured storage backend
+
+## Failure Modes
+
+- Invalid CSV format causing parsing errors during workflow loading
+- Missing or misconfigured LLM provider settings leading to execution failures
+- Storage backend unavailable or misconfigured preventing workflow persistence
+- Agent execution timeout due to long-running operations or infinite loops
+
+## Source
+
+Extracted from: [https://github.com/jwwelbor/AgentMap.git](https://github.com/jwwelbor/AgentMap.git)
+Confidence: 0.95

skills/branching-agent-pattern/commands.md

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

skills/branching-agent-pattern/examples.md

@@ -0,0 +1,10 @@
+# Examples: branching-agent-pattern
+
+## Usage Example
+
+```python
+# How to use this skill
+# Inputs: CSV workflow files defining agent graphs 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 backend configuration in agentmap_config_storage.yaml
+# Process: Define workflow graph in CSV with nodes representing agent steps and their connections (next_node, on_failure) → Configure BranchingAgent with customizable success/failure values and fallback fields in the context dictionary → Initialize the agent runtime with ensure_initialized() and configure execution tracking and state adapter services
+# Outputs: Executed workflow with results stored in the specified output_field, Detailed execution trace showing success/failure decisions at each branching point, Updated workflow state persisted in the configured storage backend
+```

skills/branching-agent-pattern/metadata.json

@@ -0,0 +1,31 @@
+{
+  "name": "branching-agent-pattern",
+  "version": "1.0.0",
+  "goal": "Define and execute AI agent workflows using CSV-based declarative definitions with configurable branching logic",
+  "inputs": [
+    "CSV workflow files defining agent graphs 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 backend configuration in agentmap_config_storage.yaml"
+  ],
+  "steps": [
+    "Define workflow graph in CSV with nodes representing agent steps and their connections (next_node, on_failure)",
+    "Configure BranchingAgent with customizable success/failure values and fallback fields in the context dictionary",
+    "Initialize the agent runtime with ensure_initialized() and configure execution tracking and state adapter services",
+    "Execute the workflow using agentmap run with appropriate inputs and monitor the execution trace"
+  ],
+  "outputs": [
+    "Executed workflow with results stored in the specified output_field",
+    "Detailed execution trace showing success/failure decisions at each branching point",
+    "Updated workflow state persisted in the configured storage backend"
+  ],
+  "failure_modes": [
+    "Invalid CSV format causing parsing errors during workflow loading",
+    "Missing or misconfigured LLM provider settings leading to execution failures",
+    "Storage backend unavailable or misconfigured preventing workflow persistence",
+    "Agent execution timeout due to long-running operations or infinite loops"
+  ],
+  "confidence": 0.95,
+  "explanation": "The BranchingAgent pattern provides a reusable framework for creating conditional AI workflows. The CSV-based workflow definition allows defining complex agent graphs declaratively, while the BranchingAgent handles dynamic branching based on success/failure conditions with customizable value sets. This pattern can be adapted to various use cases including task routing, error handling, and conditional execution paths across different domains.",
+  "source_repo": "https://github.com/jwwelbor/AgentMap.git",
+  "score": 1.0
+}

skills/branching-agent-pattern/tests.md

@@ -1,4 +1,4 @@
-# Tests: agent-builder-workflow
+# Tests: branching-agent-pattern
 
 ## Test Checklist