Add Skill: langgraph-multi-agent-sequential

Extracted from: https://github.com/omerbsezer/Fast-LLM-Agent-MCP.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-multi-agent-sequential/SKILL.md

@@ -0,0 +1,99 @@
+---
+name: langgraph-multi-agent-sequential
+version: 1.0.0
+description: Orchestrate a sequence of specialized agents to perform multi-step tasks
+  like research, data processing, and final output generation
+inputs:
+- BedrockModel with temperature=0.3, top_p=0.8
+- Researcher agent with system prompt for destination research (places, history, accommodations,
+  food, web pages)
+- Travel Guide Generator agent with system prompt for structuring travel guides into
+  labeled sections
+- Writer agent with system prompt for formatting professional client responses
+steps:
+- Researcher agent gathers raw destination facts (top 5 attractions, historical facts,
+  best areas, local foods, suggested web pages) using BedrockModel
+- Travel Guide Generator agent structures the raw facts into a comprehensive travel
+  guide with clearly labeled sections
+- Writer agent formats the structured guide into a professional client-facing response
+  with the full guide and highlighted web pages
+outputs:
+- Raw research data (JSON string containing destination facts and categories)
+- Structured travel guide content (markdown with sections for attractions, history,
+  accommodations, cuisine, and web pages)
+- Final client response (formatted travel guide ready for delivery)
+tags: []
+metadata:
+  source_repo: https://github.com/omerbsezer/Fast-LLM-Agent-MCP.git
+  extracted_at: ''
+  confidence: 0.95
+---
+
+# langgraph-multi-agent-sequential
+
+Orchestrate a sequence of specialized agents to perform multi-step tasks like research, data processing, and final output generation
+
+## Setup
+
+**Dependencies:**
+
+```text
+pip install langchain langgraph bedrock-model pydantic
+```
+
+**Setup steps:**
+
+1. Install langchain and langgraph packages
+1. Configure BedrockModel with temperature=0.3 and top_p=0.8
+1. Create three Agent instances with appropriate system prompts and tools
+1. Deploy the FastAPI server with the LangGraph application
+
+## Key Files
+
+- `agents/langchain_langgraph/08-langgraph-multi-agents-sequential-pattern/agent.py`
+- `agents/langchain_langgraph/08-langgraph-multi-agents-sequential-pattern/app.py`
+
+## Steps
+
+1. Researcher agent gathers raw destination facts (top 5 attractions, historical facts, best areas, local foods, suggested web pages) using BedrockModel
+2. Travel Guide Generator agent structures the raw facts into a comprehensive travel guide with clearly labeled sections
+3. Writer agent formats the structured guide into a professional client-facing response with the full guide and highlighted web pages
+
+## Implementation Details
+
+```python
+Researcher agent with system_prompt for destination research and tools=[calculator, current_time]
+```
+
+```python
+Travel Guide Generator agent with system_prompt requiring structured sections (attractions, history, accommodations, cuisine, web pages)
+```
+
+```python
+Writer agent with system_prompt for client-facing response formatting
+```
+
+## Inputs
+
+- BedrockModel with temperature=0.3, top_p=0.8
+- Researcher agent with system prompt for destination research (places, history, accommodations, food, web pages)
+- Travel Guide Generator agent with system prompt for structuring travel guides into labeled sections
+- Writer agent with system prompt for formatting professional client responses
+
+## Outputs
+
+- Raw research data (JSON string containing destination facts and categories)
+- Structured travel guide content (markdown with sections for attractions, history, accommodations, cuisine, and web pages)
+- Final client response (formatted travel guide ready for delivery)
+
+## Failure Modes
+
+- Researcher agent fails to retrieve sufficient information, causing downstream agents to receive incomplete or empty data
+- Travel Guide Generator fails to structure data properly, resulting in unorganized or malformed output
+- Writer agent fails to format the final response correctly, producing garbled or incomplete output
+- Model timeouts or errors in any agent step causing the entire pipeline to fail
+
+## Source
+
+Extracted from: [https://github.com/omerbsezer/Fast-LLM-Agent-MCP.git](https://github.com/omerbsezer/Fast-LLM-Agent-MCP.git)
+Confidence: 0.95

skills/langgraph-multi-agent-sequential/commands.md

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

skills/langgraph-multi-agent-sequential/examples.md

@@ -0,0 +1,10 @@
+# Examples: langgraph-multi-agent-sequential
+
+## Usage Example
+
+```python
+# How to use this skill
+# Inputs: BedrockModel with temperature=0.3, top_p=0.8, Researcher agent with system prompt for destination research (places, history, accommodations, food, web pages), Travel Guide Generator agent with system prompt for structuring travel guides into labeled sections, Writer agent with system prompt for formatting professional client responses
+# Process: Researcher agent gathers raw destination facts (top 5 attractions, historical facts, best areas, local foods, suggested web pages) using BedrockModel → Travel Guide Generator agent structures the raw facts into a comprehensive travel guide with clearly labeled sections → Writer agent formats the structured guide into a professional client-facing response with the full guide and highlighted web pages
+# Outputs: Raw research data (JSON string containing destination facts and categories), Structured travel guide content (markdown with sections for attractions, history, accommodations, cuisine, and web pages), Final client response (formatted travel guide ready for delivery)
+```

skills/langgraph-multi-agent-sequential/metadata.json

@@ -0,0 +1,31 @@
+{
+  "name": "langgraph-multi-agent-sequential",
+  "version": "1.0.0",
+  "goal": "Orchestrate a sequence of specialized agents to perform multi-step tasks like research, data processing, and final output generation",
+  "inputs": [
+    "BedrockModel with temperature=0.3, top_p=0.8",
+    "Researcher agent with system prompt for destination research (places, history, accommodations, food, web pages)",
+    "Travel Guide Generator agent with system prompt for structuring travel guides into labeled sections",
+    "Writer agent with system prompt for formatting professional client responses"
+  ],
+  "steps": [
+    "Researcher agent gathers raw destination facts (top 5 attractions, historical facts, best areas, local foods, suggested web pages) using BedrockModel",
+    "Travel Guide Generator agent structures the raw facts into a comprehensive travel guide with clearly labeled sections",
+    "Writer agent formats the structured guide into a professional client-facing response with the full guide and highlighted web pages"
+  ],
+  "outputs": [
+    "Raw research data (JSON string containing destination facts and categories)",
+    "Structured travel guide content (markdown with sections for attractions, history, accommodations, cuisine, and web pages)",
+    "Final client response (formatted travel guide ready for delivery)"
+  ],
+  "failure_modes": [
+    "Researcher agent fails to retrieve sufficient information, causing downstream agents to receive incomplete or empty data",
+    "Travel Guide Generator fails to structure data properly, resulting in unorganized or malformed output",
+    "Writer agent fails to format the final response correctly, producing garbled or incomplete output",
+    "Model timeouts or errors in any agent step causing the entire pipeline to fail"
+  ],
+  "confidence": 0.95,
+  "explanation": "This workflow demonstrates a reusable LangGraph pattern where three specialized agents work sequentially: a Researcher agent gathers raw destination facts, a Travel Guide Generator agent structures those facts into a travel guide, and a Writer agent formats the final output for clients. The pattern is modular and can be adapted to other multi-step tasks by swapping agent roles and prompts while maintaining the same pipeline structure.",
+  "source_repo": "https://github.com/omerbsezer/Fast-LLM-Agent-MCP.git",
+  "score": 1.0
+}

skills/langgraph-multi-agent-sequential/tests.md

@@ -1,4 +1,4 @@
-# Tests: agent-builder-workflow
+# Tests: langgraph-multi-agent-sequential
 
 ## Test Checklist