Web Research Skill

Version: 2.1.0 Author: Claw 🦾 Purpose: Generate structured research reports with source citations, quality scoring, and automated follow-ups.

Overview

The web-research skill automates end-to-end research: parse question → generate diverse queries → search → fetch → follow-up → deduplicate → synthesize → report.

Key improvements over v1:

• Automated follow-up queries — 2 rounds of follow-ups based on initial findings
• Quality scoring — each source scored (0-1) on content depth, URL, title, date
• Source deduplication — remove duplicate sources, keep the most detailed
• Batch research mode — process multiple topics in one session
• Multiple output formats — markdown (default), JSON, HTML
• Topic extraction — intelligent keyword extraction from natural language questions

How to Use

Basic Usage

# Single research question
python3 scripts/research.py "What is the state of AI regulation in the EU for 2026?"

# With more follow-up rounds
python3 scripts/research.py --followups 5 "Market analysis for renewable energy in Czech Republic"

# JSON output
python3 scripts/research.py --format json "Cryptocurrency regulation 2026"

# HTML output
python3 scripts/research.py --format html "Competition in cloud computing market"

# Custom source limit
python3 scripts/research.py --sources 15 "Best pricing for SaaS tools small business"

Batch Mode

Create a JSON file (questions.json):

{
  "questions": [
    "State of AI regulation in the EU for 2026",
    "Best SaaS tools for small business automation",
    "Cryptocurrency regulation trends 2026"
  ]
}

Then run:

python3 scripts/research.py --batch questions.json

Pipeline Steps

Step 1: Parse Question

Extract meaningful topic keywords from natural language question. Removes stop words, keeps entities and key terms.

Step 2: Generate Queries

Create 5 diverse query variants:

• Exact match
• Broad match
• Time-aware (2025/2026)
• Analytical
• Market data focused

Step 3: Execute Searches

Run web_search for each query variant. Collect results with title, URL, snippet.

Step 4: Fetch Content

Use web_fetch to extract content from top URLs. Store full text for synthesis.

Step 5: Follow-up Queries (v2)

Based on initial findings, generate 2 rounds of follow-up searches:

• Look for emerging themes in findings
• Add time-aware follow-ups
• Fill information gaps
• Increase coverage and accuracy

Step 6: Deduplicate & Score

Remove duplicate sources by URL. Score each source (0-1) based on:

• Has URL (+0.2), has title (+0.15), has details (+0.3)
• Content length > 100 chars (+0.2), has date (+0.15)

Step 7: Synthesize & Report

Combine findings into structured report with:

• Executive summary
• Numbered key findings with quality tags
• Quality assessment table
• Limitations and methodology
• Source citations

Report Formats

Markdown (default)

Rich text with headings, tables, bullet lists. Suitable for reading and sharing.

JSON

Structured data output. Suitable for programmatic processing, APIs, dashboards.

HTML

Self-contained styled report. Suitable for web viewing, email attachments.

Output Files

Reports saved to: workspace/research/web-research-YYYY-MM-DD-\x3Ctopic>.md

JSON reports: workspace/research/web-research-YYYY-MM-DD-\x3Ctopic>.json

HTML reports: workspace/research/web-research-YYYY-MM-DD-\x3Ctopic>.html

Quality Rules

1. Cross-reference — at least 2 sources per major claim
2. Flag outdated info — >2 years old for fast-moving topics
3. Distinguish opinion vs data — clearly mark analytical content
4. Cite every source — URL for every factual claim
5. Note conflicts — when sources disagree, document both views
6. Score sources — low-quality sources flagged in report

Skill Dependencies

• web_search — search the web via SearXNG
• web_fetch — fetch and extract content from URLs
• write — generate and save reports
• exec — run pipeline scripts

Pricing

File Structure

web-research/
  SKILL.md                              — This file
  scripts/
    research.py                         — Research pipeline v2.1.0
  references/
    synthesis-framework.md              — How to synthesize findings
    report_template.md                  — Standard report structure
    search-strategies.md                — Query generation best practices

Version History