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
5 changed files with 138 additions and 0 deletions
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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
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+# Commands: autonomous-web-research-agent
+
+## Available Commands
+
+- `/skill autonomous-web-research-agent` — Load this skill
+- `/run autonomous-web-research-agent` — Execute workflow
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+# 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
+```
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+{
+  "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
+}
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+# Tests: autonomous-web-research-agent
+
+## 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