Add Skill: langgraph-multi-agent-sequential

Extracted from: https://github.com/omerbsezer/Fast-LLM-Agent-MCP.git
Score: 1.0

skills/conditional-review-workflow/SKILL.md

@@ -1,56 +0,0 @@
----
-name: conditional-review-workflow
-version: 1.0.0
-description: Process a user request through branching logic to approve or reject it,
-  producing a routed decision result
-inputs:
-- 'request: string - The user''s request or input collected at runtime via the input
-  agent'
-steps:
-- 'Start: Input agent collects the user request and stores it in the ''request'' state
-  field, then routes to Classify node'
-- 'Classify: Branching agent evaluates the ''request'' field and routes to Approve
-  node on success or Reject node on failure (on_failure)'
-- 'Approve: Default agent formats an approval message using the request and stores
-  it in the ''result'' output field'
-- 'Reject: Default agent formats a rejection message using the request and stores
-  it in the ''result'' output field'
-outputs:
-- 'result: string - Final message indicating whether the request was approved or rejected,
-  containing the original request'
-tags: []
-metadata:
-  source_repo: https://github.com/jwwelbor/AgentMap.git
-  extracted_at: ''
-  confidence: 0.85
----
-
-# conditional-review-workflow
-
-Process a user request through branching logic to approve or reject it, producing a routed decision result
-
-## Steps
-
-1. Start: Input agent collects the user request and stores it in the 'request' state field, then routes to Classify node
-2. Classify: Branching agent evaluates the 'request' field and routes to Approve node on success or Reject node on failure (on_failure)
-3. Approve: Default agent formats an approval message using the request and stores it in the 'result' output field
-4. Reject: Default agent formats a rejection message using the request and stores it in the 'result' output field
-
-## Inputs
-
-- request: string - The user's request or input collected at runtime via the input agent
-
-## Outputs
-
-- result: string - Final message indicating whether the request was approved or rejected, containing the original request
-
-## Failure Modes
-
-- Input collection failure - user provides no or invalid input (no explicit on_failure defined for Start node in example)
-- Branching classification failure - if branching agent cannot evaluate, it routes to Reject via on_failure configuration
-- LLM provider unavailability - if branching or agents rely on LLM backends that are misconfigured or offline
-
-## Source
-
-Extracted from: [https://github.com/jwwelbor/AgentMap.git](https://github.com/jwwelbor/AgentMap.git)
-Confidence: 0.85

skills/conditional-review-workflow/commands.md

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

skills/conditional-review-workflow/examples.md

@@ -1,10 +0,0 @@
-# Examples: conditional-review-workflow
-
-## Usage Example
-
-```python
-# How to use this skill
-# Inputs: request: string - The user's request or input collected at runtime via the input agent
-# Process: Start: Input agent collects the user request and stores it in the 'request' state field, then routes to Classify node → Classify: Branching agent evaluates the 'request' field and routes to Approve node on success or Reject node on failure (on_failure) → Approve: Default agent formats an approval message using the request and stores it in the 'result' output field
-# Outputs: result: string - Final message indicating whether the request was approved or rejected, containing the original request
-```

skills/conditional-review-workflow/metadata.json

@@ -1,26 +0,0 @@
-{
-  "name": "conditional-review-workflow",
-  "version": "1.0.0",
-  "goal": "Process a user request through branching logic to approve or reject it, producing a routed decision result",
-  "inputs": [
-    "request: string - The user's request or input collected at runtime via the input agent"
-  ],
-  "steps": [
-    "Start: Input agent collects the user request and stores it in the 'request' state field, then routes to Classify node",
-    "Classify: Branching agent evaluates the 'request' field and routes to Approve node on success or Reject node on failure (on_failure)",
-    "Approve: Default agent formats an approval message using the request and stores it in the 'result' output field",
-    "Reject: Default agent formats a rejection message using the request and stores it in the 'result' output field"
-  ],
-  "outputs": [
-    "result: string - Final message indicating whether the request was approved or rejected, containing the original request"
-  ],
-  "failure_modes": [
-    "Input collection failure - user provides no or invalid input (no explicit on_failure defined for Start node in example)",
-    "Branching classification failure - if branching agent cannot evaluate, it routes to Reject via on_failure configuration",
-    "LLM provider unavailability - if branching or agents rely on LLM backends that are misconfigured or offline"
-  ],
-  "confidence": 0.85,
-  "explanation": "Extracted from AgentMap's documented CSV workflow example (ReviewFlow). This is a reusable conditional routing pattern that can be adapted for any approval/rejection, triage, or binary-decision scenario by modifying the branching prompt and agent types. The CSV-based declarative format makes it portable across the AgentMap framework.",
-  "source_repo": "https://github.com/jwwelbor/AgentMap.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: conditional-review-workflow
+# Tests: langgraph-multi-agent-sequential
 
 ## Test Checklist