agent-skills/skills/agent-builder-workflow/SKILL.md

name, version, description, inputs, steps, outputs, tags, metadata

name	version	description	inputs	steps	outputs	tags	metadata
agent-builder-workflow	1.0.0	Build no-code AI agents that connect to enterprise knowledge sources, perform unified search and deep research, and generate explainable answers with citations	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)	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	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		source_repo extracted_at confidence https://github.com/pipeshub-ai/pipeshub-ai.git 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:

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
2. Configure knowledge sources in .env (graph DB connection, vector DB, document paths)
3. Define agent task in agent_builder.py with objectives and retrieval strategy
4. Run agent chain: python agent_chain.py --task "research_quantum_computing"
5. 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

LangGraph chain with retrieval (graph/vector) and LLM response stages

Knowledge graph construction from enterprise documents

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 Confidence: 0.95