Add Publisher v2 + 5 extracted skills

Publisher fixes: - Checkout new branch before push (was pushing main ref) - Verify files staged before commit - Handle duplicate files gracefully - Clean error reporting per stage Skills merged to main: - mcp-server-setup (from pipeshub-ai) - research-pipeline (from Blacknode) - multi-agent-sequential-workflow (from Fast-LLM-Agent-MCP) - unifai-workflow-execution (from UnifAI) - code-review-agent-workflow (from AgentKit)
2026-08-05 15:15:09 +00:00
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+---
+name: multi-agent-sequential-workflow
+version: 1.0.0
+description: Gather and process information from multiple agents to generate a comprehensive
+  travel guide.
+inputs:
+- User query with location
+steps:
+- 'Step 1: Researcher agent (agent.py) uses LangGraph create_react_agent with BedrockModel
+  to gather raw facts about the destination.'
+- 'Step 2: Travel Guide Generator agent (agent.py) synthesizes the gathered data into
+  a structured travel guide based on the user''s request and raw information provided
+  by the researcher.'
+- 'Step 3: Writer agent (agent.py) formats the final response, including the structured
+  guide content and prominently features the ''Suggested Web Pages'' section.'
+outputs:
+- Structured travel guide with key sections
+- Final client response
+tags: []
+metadata:
+  source_repo: https://github.com/omerbsezer/Fast-LLM-Agent-MCP.git
+  extracted_at: ''
+  confidence: 0.95
+---
+
+# multi-agent-sequential-workflow
+
+Gather and process information from multiple agents to generate a comprehensive travel guide.
+
+## Setup
+
+**Dependencies:**
+
+```text
+pip install python3 fastapi uvicorn strands bedrock-model
+```
+
+**Setup steps:**
+
+1. Install required dependencies using pip
+1. Set up environment variables for API keys and model IDs
+
+## Key Files
+
+- `agents/aws_strands/05-agent-strands-multiagent-workflow-sequential/agent.py - Contains the multi-agent workflow logic.`
+- `agents/aws_strands/05-agent-strands-multiagent-workflow-sequential/app.py - FastAPI app to handle user queries.`
+
+## Steps
+
+1. Step 1: Researcher agent (agent.py) uses LangGraph create_react_agent with BedrockModel to gather raw facts about the destination.
+2. Step 2: Travel Guide Generator agent (agent.py) synthesizes the gathered data into a structured travel guide based on the user's request and raw information provided by the researcher.
+3. Step 3: Writer agent (agent.py) formats the final response, including the structured guide content and prominently features the 'Suggested Web Pages' section.
+
+## Implementation Details
+
+```python
+research_output = researcher_agent(query, stream=False)
+```
+
+```python
+guide_output = travel_guide_agent(planner_prompt, stream=False)
+```
+
+```python
+final_response = writer_agent(writer_prompt, stream=False)
+```
+
+## Inputs
+
+- User query with location
+
+## Outputs
+
+- Structured travel guide with key sections
+- Final client response
+
+## Failure Modes
+
+- Network issues during API calls could lead to incomplete data collection or processing failures
+
+## Source
+
+Extracted from: [https://github.com/omerbsezer/Fast-LLM-Agent-MCP.git](https://github.com/omerbsezer/Fast-LLM-Agent-MCP.git)
+Confidence: 0.95