From e47f51563f3d8aec70439cee6020096af37197e3 Mon Sep 17 00:00:00 2001 From: Hermes Pipeline Date: Wed, 5 Aug 2026 15:14:18 +0000 Subject: [PATCH] Add Skill: multi-agent-sequential-workflow Extracted from: https://github.com/omerbsezer/Fast-LLM-Agent-MCP.git Score: 1.0 --- .../multi-agent-sequential-workflow/SKILL.md | 84 +++++++++++++++++++ .../commands.md | 6 ++ .../examples.md | 10 +++ .../metadata.json | 24 ++++++ .../multi-agent-sequential-workflow/tests.md | 9 ++ 5 files changed, 133 insertions(+) create mode 100644 skills/multi-agent-sequential-workflow/SKILL.md create mode 100644 skills/multi-agent-sequential-workflow/commands.md create mode 100644 skills/multi-agent-sequential-workflow/examples.md create mode 100644 skills/multi-agent-sequential-workflow/metadata.json create mode 100644 skills/multi-agent-sequential-workflow/tests.md diff --git a/skills/multi-agent-sequential-workflow/SKILL.md b/skills/multi-agent-sequential-workflow/SKILL.md new file mode 100644 index 0000000..fd52bbb --- /dev/null +++ b/skills/multi-agent-sequential-workflow/SKILL.md @@ -0,0 +1,84 @@ +--- +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 diff --git a/skills/multi-agent-sequential-workflow/commands.md b/skills/multi-agent-sequential-workflow/commands.md new file mode 100644 index 0000000..2031619 --- /dev/null +++ b/skills/multi-agent-sequential-workflow/commands.md @@ -0,0 +1,6 @@ +# Commands: multi-agent-sequential-workflow + +## Available Commands + +- `/skill multi-agent-sequential-workflow` — Load this skill +- `/run multi-agent-sequential-workflow` — Execute workflow diff --git a/skills/multi-agent-sequential-workflow/examples.md b/skills/multi-agent-sequential-workflow/examples.md new file mode 100644 index 0000000..15dae32 --- /dev/null +++ b/skills/multi-agent-sequential-workflow/examples.md @@ -0,0 +1,10 @@ +# Examples: multi-agent-sequential-workflow + +## Usage Example + +```python +# How to use this skill +# Inputs: User query with location +# Process: 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 +``` diff --git a/skills/multi-agent-sequential-workflow/metadata.json b/skills/multi-agent-sequential-workflow/metadata.json new file mode 100644 index 0000000..5128937 --- /dev/null +++ b/skills/multi-agent-sequential-workflow/metadata.json @@ -0,0 +1,24 @@ +{ + "name": "multi-agent-sequential-workflow", + "version": "1.0.0", + "goal": "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" + ], + "failure_modes": [ + "Network issues during API calls could lead to incomplete data collection or processing failures" + ], + "confidence": 0.95, + "explanation": "This workflow is specific but can be adapted for other types of guides or information gathering tasks.", + "source_repo": "https://github.com/omerbsezer/Fast-LLM-Agent-MCP.git", + "score": 1.0 +} \ No newline at end of file diff --git a/skills/multi-agent-sequential-workflow/tests.md b/skills/multi-agent-sequential-workflow/tests.md new file mode 100644 index 0000000..16dc7cc --- /dev/null +++ b/skills/multi-agent-sequential-workflow/tests.md @@ -0,0 +1,9 @@ +# Tests: multi-agent-sequential-workflow + +## Test Checklist + +- [ ] Workflow has at least 3 steps +- [ ] All inputs are defined +- [ ] All outputs are defined +- [ ] Failure modes are documented +- [ ] Skill can be loaded without errors -- 2.43.0