web-research

name	web-research
description	Use this skill for requests related to web research; it provides a structured approach to conducting comprehensive web research

Web Research Skill

This skill provides a structured approach to conducting comprehensive web research using the task tool to spawn research subagents. It emphasizes planning, efficient delegation, and systematic synthesis of findings.

When to Use This Skill

Use this skill when you need to:

• Research complex topics requiring multiple information sources
• Gather and synthesize current information from the web
• Conduct comparative analysis across multiple subjects
• Produce well-sourced research reports with clear citations

Research Process

Step 1: Create and Save Research Plan

Before delegating to subagents, you MUST:

1. Create a research folder - Organize all research files in a dedicated folder relative to the current working directory:

   mkdir research_[topic_name]
   

   This keeps files organized and prevents clutter in the working directory.

2. Analyze the research question - Break it down into distinct, non-overlapping subtopics

3. Write a research plan file - Use the write_file tool to create research_[topic_name]/research_plan.md containing:

   • The main research question
   • 2-5 specific subtopics to investigate
   • Expected information from each subtopic
   • How results will be synthesized

Planning Guidelines:

• Simple fact-finding: 1-2 subtopics
• Comparative analysis: 1 subtopic per comparison element (max 3)
• Complex investigations: 3-5 subtopics

Step 2: Delegate to Research Subagents

For each subtopic in your plan:

1. Use the task tool to spawn a research subagent with:

   • Clear, specific research question (no acronyms)
   • Instructions to write findings to a file: research_[topic_name]/findings_[subtopic].md
   • Budget: 3-5 web searches maximum

2. Run up to 3 subagents in parallel for efficient research

Subagent Instructions Template:

Research [SPECIFIC TOPIC]. Use the web_search tool to gather information.
After completing your research, use write_file to save your findings to research_[topic_name]/findings_[subtopic].md.
Include key facts, relevant quotes, and source URLs.
Use 3-5 web searches maximum.

Step 3: Synthesize Findings

After all subagents complete:

1. Review the findings files that were saved locally:

   • First run list_files research_[topic_name] to see what files were created
   • Then use read_file with the file paths (e.g., research_[topic_name]/findings_*.md)
   • Important: Use read_file for LOCAL files only, not URLs

2. Synthesize the information - Create a comprehensive response that:

   • Directly answers the original question
   • Integrates insights from all subtopics
   • Cites specific sources with URLs (from the findings files)
   • Identifies any gaps or limitations

3. Write final report (optional) - Use write_file to create research_[topic_name]/research_report.md if requested

Note: If you need to fetch additional information from URLs, use the fetch_url tool, not read_file.

Available Tools

You have access to:

• write_file: Save research plans and findings to local files
• read_file: Read local files (e.g., findings saved by subagents)
• list_files: See what local files exist in a directory
• fetch_url: Fetch content from URLs and convert to markdown (use this for web pages, not read_file)
• fetch4ai: Advanced web fetching with filtering (use for cleaner content extraction)
• task: Spawn research subagents with web_search access

fetch4ai for Advanced Content Extraction

When you need cleaner content with noise removal, use fetch4ai instead of fetch_url:

# Basic fetch with pruning (removes boilerplate)
python ~/.claude/skills/fetch4ai/scripts/fetch4ai.py \
  --url "https://example.com/article" \
  --format md --quiet

# Query-focused extraction (only content matching query)
python ~/.claude/skills/fetch4ai/scripts/fetch4ai.py \
  --url "https://example.com/paper" \
  --strategy bm25 \
  --query "methodology results" \
  --format md --quiet

When to use fetch4ai over fetch_url:

• Pages with heavy navigation/ads (use pruning)
• Long pages where you need specific sections (use bm25)
• When you need metadata like word count, reduction stats (use json format)

Research Subagent Configuration

Each subagent you spawn will have access to:

• web_search: Search the web using Tavily (parameters: query, max_results, topic, include_raw_content)
• write_file: Save their findings to the filesystem

Best Practices

• Plan before delegating - Always write research_plan.md first
• Clear subtopics - Ensure each subagent has distinct, non-overlapping scope
• File-based communication - Have subagents save findings to files, not return them directly
• Systematic synthesis - Read all findings files before creating final response
• Stop appropriately - Don't over-research; 3-5 searches per subtopic is usually sufficient