agent-skills/skills/autonomous-web-research-agent/SKILL.md

name, version, description, inputs, steps, outputs, tags, metadata

name	version	description	inputs	steps	outputs	tags	metadata
autonomous-web-research-agent	1.0.0	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.	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	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.	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		source_repo extracted_at confidence https://github.com/DennisDRX/Faraday-Web-Researcher-Agent.git 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 Confidence: 0.85