Add Skill: langgraph-multi-agent-sequential

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

skills/conditional-request-routing/SKILL.md

@@ -1,54 +0,0 @@
----
-name: conditional-request-routing
-version: 1.0.0
-description: Classify an input request and route it to either an approval or rejection
-  path, producing a corresponding result
-inputs:
-- 'request: the input request or content to be reviewed and routed'
-steps:
-- 'Collect input: Use an input agent to capture the user''s request into state field
-  ''request'''
-- 'Branch: Use a branching agent to evaluate ''request'' and route to ''Approve''
-  node on success or ''Reject'' node on failure'
-- 'Approve path: Use a default agent to format and output an approval message with
-  the request'
-- 'Reject path: Use a default agent to format and output a rejection message with
-  the request'
-outputs:
-- 'result: the approval or rejection message containing the original request'
-tags: []
-metadata:
-  source_repo: https://github.com/jwwelbor/AgentMap.git
-  extracted_at: ''
-  confidence: 0.85
----
-
-# conditional-request-routing
-
-Classify an input request and route it to either an approval or rejection path, producing a corresponding result
-
-## Steps
-
-1. Collect input: Use an input agent to capture the user's request into state field 'request'
-2. Branch: Use a branching agent to evaluate 'request' and route to 'Approve' node on success or 'Reject' node on failure
-3. Approve path: Use a default agent to format and output an approval message with the request
-4. Reject path: Use a default agent to format and output a rejection message with the request
-
-## Inputs
-
-- request: the input request or content to be reviewed and routed
-
-## Outputs
-
-- result: the approval or rejection message containing the original request
-
-## Failure Modes
-
-- Branching agent cannot evaluate request and neither path is taken
-- Missing or empty input request
-- Prompt misconfiguration causing incorrect routing
-
-## Source
-
-Extracted from: [https://github.com/jwwelbor/AgentMap.git](https://github.com/jwwelbor/AgentMap.git)
-Confidence: 0.85

skills/conditional-request-routing/commands.md

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

skills/conditional-request-routing/examples.md

@@ -1,10 +0,0 @@
-# Examples: conditional-request-routing
-
-## Usage Example
-
-```python
-# How to use this skill
-# Inputs: request: the input request or content to be reviewed and routed
-# Process: Collect input: Use an input agent to capture the user's request into state field 'request' → Branch: Use a branching agent to evaluate 'request' and route to 'Approve' node on success or 'Reject' node on failure → Approve path: Use a default agent to format and output an approval message with the request
-# Outputs: result: the approval or rejection message containing the original request
-```

skills/conditional-request-routing/metadata.json

@@ -1,26 +0,0 @@
-{
-  "name": "conditional-request-routing",
-  "version": "1.0.0",
-  "goal": "Classify an input request and route it to either an approval or rejection path, producing a corresponding result",
-  "inputs": [
-    "request: the input request or content to be reviewed and routed"
-  ],
-  "steps": [
-    "Collect input: Use an input agent to capture the user's request into state field 'request'",
-    "Branch: Use a branching agent to evaluate 'request' and route to 'Approve' node on success or 'Reject' node on failure",
-    "Approve path: Use a default agent to format and output an approval message with the request",
-    "Reject path: Use a default agent to format and output a rejection message with the request"
-  ],
-  "outputs": [
-    "result: the approval or rejection message containing the original request"
-  ],
-  "failure_modes": [
-    "Branching agent cannot evaluate request and neither path is taken",
-    "Missing or empty input request",
-    "Prompt misconfiguration causing incorrect routing"
-  ],
-  "confidence": 0.85,
-  "explanation": "Extracted from AgentMap's README example 'ReviewFlow' CSV workflow. This is a declarative LangGraph pattern using AgentMap's CSV format that implements conditional branching - a universally reusable pattern for request triage, content moderation, approval gates, or any two-path decision flow. It can be adapted by changing agent types (e.g., using LLM agents instead of default) and prompts.",
-  "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-request-routing
+# Tests: langgraph-multi-agent-sequential
 
 ## Test Checklist