Add Skill: autonomous-web-research-agent

Extracted from: https://github.com/DennisDRX/Faraday-Web-Researcher-Agent.git Score: 1.0
2026-08-10 17:05:01 +00:00
9 changed files with 130 additions and 147 deletions
@@ -0,0 +1,77 @@
+---
+name: autonomous-web-research-agent
+version: 1.0.0
+description: Autonomously research a given query on the web using multiple search
+  tools and generate a structured report with summary, detailed sections, source tracking,
+  and bias analysis.
+inputs:
+- 'query (string): the research question or topic to investigate'
+- 'tools (list, optional): selected web search/tools to use (e.g., Tavily, Google,
+  NewsAPI, DuckDuckGo)'
+- 'api_keys (dict, optional): credentials for LLM and external search APIs'
+- 'model_config (dict, optional): LLM provider and parameters'
+steps:
+- 1. Accept user query and optional tool selections.
+- '2. Initialize agent framework (e.g., LangGraph) with integrated tools: web search
+  (Tavily, Google, DuckDuckGo), news API, web scraping.'
+- 3. Decompose query into sub-questions if needed and iteratively call tools to gather
+  relevant information.
+- 4. Extract and deduplicate content from retrieved sources, tracking source metadata
+  (URL, tool used).
+- 5. Use a large language model to synthesize findings into an executive summary and
+  detailed sections.
+- 6. Analyze potential biases or limitations of gathered sources.
+- 7. Compile a structured report object (ResearchReport) containing query, summary,
+  sections, sources, biases.
+- 8. Optionally present report via a UI (e.g., Streamlit) or return as JSON.
+outputs:
+- 'ResearchReport (JSON/dict) with fields: query (string), summary (string), sections
+  (list of {heading, content}), sources (list of {url, tool_used, title}), potential_biases
+  (string)'
+- Optional UI rendering of report with source badges and expandable sections
+tags: []
+metadata:
+  source_repo: https://github.com/DennisDRX/Faraday-Web-Researcher-Agent.git
+  extracted_at: ''
+  confidence: 0.85
+---
+
+# autonomous-web-research-agent
+
+Autonomously research a given query on the web using multiple search tools and generate a structured report with summary, detailed sections, source tracking, and bias analysis.
+
+## Steps
+
+1. 1. Accept user query and optional tool selections.
+2. 2. Initialize agent framework (e.g., LangGraph) with integrated tools: web search (Tavily, Google, DuckDuckGo), news API, web scraping.
+3. 3. Decompose query into sub-questions if needed and iteratively call tools to gather relevant information.
+4. 4. Extract and deduplicate content from retrieved sources, tracking source metadata (URL, tool used).
+5. 5. Use a large language model to synthesize findings into an executive summary and detailed sections.
+6. 6. Analyze potential biases or limitations of gathered sources.
+7. 7. Compile a structured report object (ResearchReport) containing query, summary, sections, sources, biases.
+8. 8. Optionally present report via a UI (e.g., Streamlit) or return as JSON.
+
+## Inputs
+
+- query (string): the research question or topic to investigate
+- tools (list, optional): selected web search/tools to use (e.g., Tavily, Google, NewsAPI, DuckDuckGo)
+- api_keys (dict, optional): credentials for LLM and external search APIs
+- model_config (dict, optional): LLM provider and parameters
+
+## Outputs
+
+- ResearchReport (JSON/dict) with fields: query (string), summary (string), sections (list of {heading, content}), sources (list of {url, tool_used, title}), potential_biases (string)
+- Optional UI rendering of report with source badges and expandable sections
+
+## Failure Modes
+
+- Missing or invalid API keys causing tool authentication failures
+- Rate limits or network errors from search APIs
+- Insufficient or low-quality search results leading to incomplete report
+- LLM hallucination or mis-summarization despite source tracking
+- Parsing errors in HTML/scraped content
+
+## Source
+
+Extracted from: [https://github.com/DennisDRX/Faraday-Web-Researcher-Agent.git](https://github.com/DennisDRX/Faraday-Web-Researcher-Agent.git)
+Confidence: 0.85
@@ -0,0 +1,6 @@
+# Commands: autonomous-web-research-agent
+
+## Available Commands
+
+- `/skill autonomous-web-research-agent` — Load this skill
+- `/run autonomous-web-research-agent` — Execute workflow
@@ -0,0 +1,10 @@
+# Examples: autonomous-web-research-agent
+
+## Usage Example
+
+```python
+# How to use this skill
+# Inputs: query (string): the research question or topic to investigate, tools (list, optional): selected web search/tools to use (e.g., Tavily, Google, NewsAPI, DuckDuckGo), api_keys (dict, optional): credentials for LLM and external search APIs, model_config (dict, optional): LLM provider and parameters
+# Process: 1. Accept user query and optional tool selections. → 2. Initialize agent framework (e.g., LangGraph) with integrated tools: web search (Tavily, Google, DuckDuckGo), news API, web scraping. → 3. Decompose query into sub-questions if needed and iteratively call tools to gather relevant information.
+# Outputs: ResearchReport (JSON/dict) with fields: query (string), summary (string), sections (list of {heading, content}), sources (list of {url, tool_used, title}), potential_biases (string), Optional UI rendering of report with source badges and expandable sections
+```
@@ -0,0 +1,36 @@
+{
+  "name": "autonomous-web-research-agent",
+  "version": "1.0.0",
+  "goal": "Autonomously research a given query on the web using multiple search tools and generate a structured report with summary, detailed sections, source tracking, and bias analysis.",
+  "inputs": [
+    "query (string): the research question or topic to investigate",
+    "tools (list, optional): selected web search/tools to use (e.g., Tavily, Google, NewsAPI, DuckDuckGo)",
+    "api_keys (dict, optional): credentials for LLM and external search APIs",
+    "model_config (dict, optional): LLM provider and parameters"
+  ],
+  "steps": [
+    "1. Accept user query and optional tool selections.",
+    "2. Initialize agent framework (e.g., LangGraph) with integrated tools: web search (Tavily, Google, DuckDuckGo), news API, web scraping.",
+    "3. Decompose query into sub-questions if needed and iteratively call tools to gather relevant information.",
+    "4. Extract and deduplicate content from retrieved sources, tracking source metadata (URL, tool used).",
+    "5. Use a large language model to synthesize findings into an executive summary and detailed sections.",
+    "6. Analyze potential biases or limitations of gathered sources.",
+    "7. Compile a structured report object (ResearchReport) containing query, summary, sections, sources, biases.",
+    "8. Optionally present report via a UI (e.g., Streamlit) or return as JSON."
+  ],
+  "outputs": [
+    "ResearchReport (JSON/dict) with fields: query (string), summary (string), sections (list of {heading, content}), sources (list of {url, tool_used, title}), potential_biases (string)",
+    "Optional UI rendering of report with source badges and expandable sections"
+  ],
+  "failure_modes": [
+    "Missing or invalid API keys causing tool authentication failures",
+    "Rate limits or network errors from search APIs",
+    "Insufficient or low-quality search results leading to incomplete report",
+    "LLM hallucination or mis-summarization despite source tracking",
+    "Parsing errors in HTML/scraped content"
+  ],
+  "confidence": 0.85,
+  "explanation": "The repository implements a generic autonomous web research agent that can be reused for any topical query. The workflow of querying, multi-tool retrieval, synthesis, and structured reporting is not domain-specific and can be extracted as a reusable skill.",
+  "source_repo": "https://github.com/DennisDRX/Faraday-Web-Researcher-Agent.git",
+  "score": 1.0
+}
@@ -1,4 +1,4 @@
-# Tests: langgraph-multi-agent-sequential
+# Tests: autonomous-web-research-agent

 ## Test Checklist

@@ -1,99 +0,0 @@
---
-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
@@ -1,6 +0,0 @@
-# Commands: langgraph-multi-agent-sequential
-
-## Available Commands
-
- `/skill langgraph-multi-agent-sequential` — Load this skill
- `/run langgraph-multi-agent-sequential` — Execute workflow
@@ -1,10 +0,0 @@
-# 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)
-```
@@ -1,31 +0,0 @@
-{
-  "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
-}