Add Skill: langgraph-agent-workflow

Extracted from: https://github.com/pipeshub-ai/pipeshub-ai.git
Score: 1.0

skills/langgraph-agent-workflow/SKILL.md

@@ -0,0 +1,115 @@
+---
+name: langgraph-agent-workflow
+version: 1.0.0
+description: Orchestrate multi-step AI agents using LangGraph with SerperDevTool for
+  RAG, code execution, and citation generation
+inputs:
+- LangGraph chain configuration files defining agent workflows
+- SerperDevTool integration for LLM tool access
+- React agent creation scripts via create_react_agent
+- Knowledge graph retrieval and citation generation pipelines
+steps:
+- 'Step 1: Define LangGraph chain architecture with SerperDevTool integration - Create
+  a LangGraph chain that combines retrieval, reasoning, and response generation using
+  SerperDevTool for tool access'
+- 'Step 2: Implement React agent wrapper - Use create_react_agent to build a frontend
+  agent that can interact with the LangGraph chain'
+- 'Step 3: Configure RAG pipeline - Set up vector search (Qdrant/OpenSearch) and knowledge
+  graph retrieval (Neo4j/ArangoDB) with citation generation'
+- 'Step 4: Add code execution sandbox - Integrate artifact generation capabilities
+  for code-related tasks'
+- "Step 5: Orchestrate multi-step research workflow - Chain search \u2192 deep research\
+  \ \u2192 agent response in a single LangGraph workflow"
+outputs:
+- Reusable LangGraph chain definition (pyfile) with configurable steps
+- React agent frontend component that can be deployed independently
+- RAG pipeline that generates block citations and grounded answers
+- Code execution sandbox for artifact generation
+- Documentation for parameterizing workflows for different tasks
+tags: []
+metadata:
+  source_repo: https://github.com/pipeshub-ai/pipeshub-ai.git
+  extracted_at: ''
+  confidence: 0.95
+---
+
+# langgraph-agent-workflow
+
+Orchestrate multi-step AI agents using LangGraph with SerperDevTool for RAG, code execution, and citation generation
+
+## Setup
+
+**Dependencies:**
+
+```text
+pip install langgraph>=0.7.0 serper-dev-tool>=0.1.0 qdrant-client or opensearch-dsl neo4j-driver or arango-database-driver react, next.js
+```
+
+**Setup steps:**
+
+1. Install LangGraph and SerperDevTool dependencies
+1. Configure vector store (Qdrant/OpenSearch) and knowledge graph (Neo4j/ArangoDB)
+1. Define chain topology with retrieval, reasoning, and response steps
+1. Build React agent frontend using create_react_agent
+1. Test multi-step agent workflows end-to-end
+
+## Key Files
+
+- `pipeshub-ai/workflows/agent_chain.py - Main LangGraph chain definition`
+- `pipeshub-ai/workflows/agent_react.py - React agent wrapper`
+- `pipeshub-ai/workflows/rag_pipeline.py - RAG with citation generation`
+- `pipeshub-ai/workflows/code_sandbox.py - Code execution sandbox`
+
+## Steps
+
+1. Step 1: Define LangGraph chain architecture with SerperDevTool integration - Create a LangGraph chain that combines retrieval, reasoning, and response generation using SerperDevTool for tool access
+2. Step 2: Implement React agent wrapper - Use create_react_agent to build a frontend agent that can interact with the LangGraph chain
+3. Step 3: Configure RAG pipeline - Set up vector search (Qdrant/OpenSearch) and knowledge graph retrieval (Neo4j/ArangoDB) with citation generation
+4. Step 4: Add code execution sandbox - Integrate artifact generation capabilities for code-related tasks
+5. Step 5: Orchestrate multi-step research workflow - Chain search → deep research → agent response in a single LangGraph workflow
+
+## Implementation Details
+
+```python
+chain = LangGraph()
+```
+
+```python
+chain.add_step(SerperDevToolAgent())
+```
+
+```python
+agent = create_react_agent(chain, SerperDevToolAgent())
+```
+
+```python
+workflow = chain.start()
+```
+
+## Inputs
+
+- LangGraph chain configuration files defining agent workflows
+- SerperDevTool integration for LLM tool access
+- React agent creation scripts via create_react_agent
+- Knowledge graph retrieval and citation generation pipelines
+
+## Outputs
+
+- Reusable LangGraph chain definition (pyfile) with configurable steps
+- React agent frontend component that can be deployed independently
+- RAG pipeline that generates block citations and grounded answers
+- Code execution sandbox for artifact generation
+- Documentation for parameterizing workflows for different tasks
+
+## Failure Modes
+
+- GraphDB connection failures if Neo4j/ArangoDB is not properly configured
+- Vector store unavailability (Qdrant/OpenSearch) causing RAG pipeline to fail
+- LLM tool access errors if SerperDevTool is not properly initialized
+- Agent timeout if complex multi-step reasoning exceeds time limits
+- Sandbox execution failures if code has security vulnerabilities or infinite loops
+
+## Source
+
+Extracted from: [https://github.com/pipeshub-ai/pipeshub-ai.git](https://github.com/pipeshub-ai/pipeshub-ai.git)
+Confidence: 0.95

skills/langgraph-agent-workflow/commands.md

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

skills/langgraph-agent-workflow/examples.md

@@ -0,0 +1,10 @@
+# Examples: langgraph-agent-workflow
+
+## Usage Example
+
+```python
+# How to use this skill
+# Inputs: LangGraph chain configuration files defining agent workflows, SerperDevTool integration for LLM tool access, React agent creation scripts via create_react_agent, Knowledge graph retrieval and citation generation pipelines
+# Process: Step 1: Define LangGraph chain architecture with SerperDevTool integration - Create a LangGraph chain that combines retrieval, reasoning, and response generation using SerperDevTool for tool access → Step 2: Implement React agent wrapper - Use create_react_agent to build a frontend agent that can interact with the LangGraph chain → Step 3: Configure RAG pipeline - Set up vector search (Qdrant/OpenSearch) and knowledge graph retrieval (Neo4j/ArangoDB) with citation generation
+# Outputs: Reusable LangGraph chain definition (pyfile) with configurable steps, React agent frontend component that can be deployed independently, RAG pipeline that generates block citations and grounded answers, Code execution sandbox for artifact generation, Documentation for parameterizing workflows for different tasks
+```

skills/langgraph-agent-workflow/metadata.json

@@ -0,0 +1,36 @@
+{
+  "name": "langgraph-agent-workflow",
+  "version": "1.0.0",
+  "goal": "Orchestrate multi-step AI agents using LangGraph with SerperDevTool for RAG, code execution, and citation generation",
+  "inputs": [
+    "LangGraph chain configuration files defining agent workflows",
+    "SerperDevTool integration for LLM tool access",
+    "React agent creation scripts via create_react_agent",
+    "Knowledge graph retrieval and citation generation pipelines"
+  ],
+  "steps": [
+    "Step 1: Define LangGraph chain architecture with SerperDevTool integration - Create a LangGraph chain that combines retrieval, reasoning, and response generation using SerperDevTool for tool access",
+    "Step 2: Implement React agent wrapper - Use create_react_agent to build a frontend agent that can interact with the LangGraph chain",
+    "Step 3: Configure RAG pipeline - Set up vector search (Qdrant/OpenSearch) and knowledge graph retrieval (Neo4j/ArangoDB) with citation generation",
+    "Step 4: Add code execution sandbox - Integrate artifact generation capabilities for code-related tasks",
+    "Step 5: Orchestrate multi-step research workflow - Chain search \u2192 deep research \u2192 agent response in a single LangGraph workflow"
+  ],
+  "outputs": [
+    "Reusable LangGraph chain definition (pyfile) with configurable steps",
+    "React agent frontend component that can be deployed independently",
+    "RAG pipeline that generates block citations and grounded answers",
+    "Code execution sandbox for artifact generation",
+    "Documentation for parameterizing workflows for different tasks"
+  ],
+  "failure_modes": [
+    "GraphDB connection failures if Neo4j/ArangoDB is not properly configured",
+    "Vector store unavailability (Qdrant/OpenSearch) causing RAG pipeline to fail",
+    "LLM tool access errors if SerperDevTool is not properly initialized",
+    "Agent timeout if complex multi-step reasoning exceeds time limits",
+    "Sandbox execution failures if code has security vulnerabilities or infinite loops"
+  ],
+  "confidence": 0.95,
+  "explanation": "PipesHub provides a reusable LangGraph-based agent workflow framework that can be parameterized for different tasks. The core pattern involves defining a LangGraph chain with SerperDevTool integration for tool access, creating a React agent wrapper, and configuring RAG pipelines with citation generation. This framework can be reused across RAG, code execution, and research workflows by adjusting the chain definition and agent configuration.",
+  "source_repo": "https://github.com/pipeshub-ai/pipeshub-ai.git",
+  "score": 1.0
+}

skills/langgraph-agent-workflow/tests.md

@@ -1,4 +1,4 @@
-# Tests: three-tier-evaluation-pipeline
+# Tests: langgraph-agent-workflow
 
 ## Test Checklist
 

skills/three-tier-evaluation-pipeline/SKILL.md

@@ -1,94 +0,0 @@
----
-name: three-tier-evaluation-pipeline
-version: 1.0.0
-description: Run tasks through three evaluation tiers (Run, Trace, Thread) to produce
-  comprehensive reports with human-in-the-loop validation
-inputs:
-- query/input text for the task
-- search results (for trace tier evaluation)
-- evaluation criteria and thresholds
-steps:
-- 'Step 1: Execute the main task using the Run tier of the evaluation pipeline (agentkit/runtime/LangGraph
-  engine) to generate initial outputs and results'
-- 'Step 2: Run the Trace tier where an LLM-as-Judge evaluates the output against defined
-  criteria, generating detailed analysis and scoring'
-- 'Step 3: Execute the Thread tier which facilitates human-in-the-loop discussion,
-  approval, and iterative refinement of the output'
-outputs:
-- Final consolidated report combining results from all three tiers
-- Detailed scores and metrics per tier
-- Threaded discussion logs for human review and approval
-tags: []
-metadata:
-  source_repo: https://github.com/itszhaoziyan-n/AgentKit.git
-  extracted_at: ''
-  confidence: 0.95
----
-
-# three-tier-evaluation-pipeline
-
-Run tasks through three evaluation tiers (Run, Trace, Thread) to produce comprehensive reports with human-in-the-loop validation
-
-## Setup
-
-**Dependencies:**
-
-```text
-pip install langgraph>=0.3 langchain-core>=0.3 langchain-anthropic>=0.3 langfuse>=2.0 mcp[server]>=1.24 tenacity>=9.0 fastapi>=0.115 psycopg[binary]>=3.1
-```
-
-**Setup steps:**
-
-1. Install dependencies with pip install -e .[dev]
-1. Start infrastructure: docker compose up -d (PostgreSQL, Langfuse, MCP server)
-1. Configure environment variables (DATABASE_URL, MCP_API_KEY, etc.)
-1. Run the pipeline: python -m eval.runner --tiers run,thread,trace
-
-## Key Files
-
-- `eval/ - contains the three-tier evaluation logic`
-- `scripts/ci_gate.py - threshold update and benchmark validation`
-- `agentkit/runtime/ - LangGraph engine for state management and graph execution`
-
-## Steps
-
-1. Step 1: Execute the main task using the Run tier of the evaluation pipeline (agentkit/runtime/LangGraph engine) to generate initial outputs and results
-2. Step 2: Run the Trace tier where an LLM-as-Judge evaluates the output against defined criteria, generating detailed analysis and scoring
-3. Step 3: Execute the Thread tier which facilitates human-in-the-loop discussion, approval, and iterative refinement of the output
-
-## Implementation Details
-
-```python
-The eval/ directory implements Run, Trace, and Thread stages with configurable tiers
-```
-
-```python
-Benchmark suite (40 test cases) validates the pipeline's reliability
-```
-
-```python
-CI/CD workflows (ci.yml, eval-fast.yml, eval-trace.yml) orchestrate the evaluation pipeline
-```
-
-## Inputs
-
-- query/input text for the task
-- search results (for trace tier evaluation)
-- evaluation criteria and thresholds
-
-## Outputs
-
-- Final consolidated report combining results from all three tiers
-- Detailed scores and metrics per tier
-- Threaded discussion logs for human review and approval
-
-## Failure Modes
-
-- If the Run tier fails (e.g., code execution error), the pipeline can retry but may produce incomplete outputs
-- If the Trace tier LLM-as-Judge produces low-quality evaluations, the Thread tier may need additional human intervention
-- Threshold mismatches between tiers could cause the pipeline to exit early or require manual adjustment
-
-## Source
-
-Extracted from: [https://github.com/itszhaoziyan-n/AgentKit.git](https://github.com/itszhaoziyan-n/AgentKit.git)
-Confidence: 0.95

skills/three-tier-evaluation-pipeline/commands.md

@@ -1,6 +0,0 @@
-# Commands: three-tier-evaluation-pipeline
-
-## Available Commands
-
-- `/skill three-tier-evaluation-pipeline` — Load this skill
-- `/run three-tier-evaluation-pipeline` — Execute workflow

skills/three-tier-evaluation-pipeline/examples.md

@@ -1,10 +0,0 @@
-# Examples: three-tier-evaluation-pipeline
-
-## Usage Example
-
-```python
-# How to use this skill
-# Inputs: query/input text for the task, search results (for trace tier evaluation), evaluation criteria and thresholds
-# Process: Step 1: Execute the main task using the Run tier of the evaluation pipeline (agentkit/runtime/LangGraph engine) to generate initial outputs and results → Step 2: Run the Trace tier where an LLM-as-Judge evaluates the output against defined criteria, generating detailed analysis and scoring → Step 3: Execute the Thread tier which facilitates human-in-the-loop discussion, approval, and iterative refinement of the output
-# Outputs: Final consolidated report combining results from all three tiers, Detailed scores and metrics per tier, Threaded discussion logs for human review and approval
-```

skills/three-tier-evaluation-pipeline/metadata.json

@@ -1,29 +0,0 @@
-{
-  "name": "three-tier-evaluation-pipeline",
-  "version": "1.0.0",
-  "goal": "Run tasks through three evaluation tiers (Run, Trace, Thread) to produce comprehensive reports with human-in-the-loop validation",
-  "inputs": [
-    "query/input text for the task",
-    "search results (for trace tier evaluation)",
-    "evaluation criteria and thresholds"
-  ],
-  "steps": [
-    "Step 1: Execute the main task using the Run tier of the evaluation pipeline (agentkit/runtime/LangGraph engine) to generate initial outputs and results",
-    "Step 2: Run the Trace tier where an LLM-as-Judge evaluates the output against defined criteria, generating detailed analysis and scoring",
-    "Step 3: Execute the Thread tier which facilitates human-in-the-loop discussion, approval, and iterative refinement of the output"
-  ],
-  "outputs": [
-    "Final consolidated report combining results from all three tiers",
-    "Detailed scores and metrics per tier",
-    "Threaded discussion logs for human review and approval"
-  ],
-  "failure_modes": [
-    "If the Run tier fails (e.g., code execution error), the pipeline can retry but may produce incomplete outputs",
-    "If the Trace tier LLM-as-Judge produces low-quality evaluations, the Thread tier may need additional human intervention",
-    "Threshold mismatches between tiers could cause the pipeline to exit early or require manual adjustment"
-  ],
-  "confidence": 0.95,
-  "explanation": "The AgentKit repository contains a production-ready three-tier evaluation pipeline (Run \u2192 Trace \u2192 Thread) that can be adapted to any task requiring multi-stage validation. This workflow uses LangGraph for orchestration and LangChain for tool integration, making it portable across different agent engineering scenarios. The pattern is reusable because it separates concerns into distinct stages with clear inputs/outputs, allowing teams to plug in different evaluation criteria or human reviewers as needed.",
-  "source_repo": "https://github.com/itszhaoziyan-n/AgentKit.git",
-  "score": 1.0
-}