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Add Skill: autonomous-web-research-agent

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Extracted from: https://github.com/DennisDRX/Faraday-Web-Researcher-Agent.git
Score: 1.0
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---
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