Web Research

Conduct structured web research by searching, fetching, and synthesizing information into reports with citations and source verification.

Security Scan

The skill is internally coherent with its stated purpose (structured web research): it requires no external credentials, installs nothing, and its code and instructions align with the described pipeline, though there are some privacy/operational notes to consider around follow-up queries and saved outputs.

Purpose & Capability

Name/description, SKILL.md, and the included Python pipeline all describe a web-research pipeline (search → fetch → synthesize → report). Declared dependencies (web_search, web_fetch, write, exec) match the task. There are no unrelated required binaries or environment variables.

Instruction Scope

Instructions stay within the research scope (query generation, searching, fetching, deduplication, scoring, reporting). One operational note: follow-up query generation uses text drawn from initial findings (titles/summaries). That means content fetched from pages may be re-submitted to search backends as part of follow-ups, which can re-expose sensitive or proprietary text to external search services. Reports and fetched content are written into workspace/research — audit that location if you have sensitive data concerns.

Install Mechanism

No install spec; this is an instruction-only skill with an included script. That is the lowest-risk install model: nothing is downloaded or executed automatically beyond the provided script.

Credentials

The skill requests no environment variables or credentials and does not reference system config paths. This is proportionate for a web-research tool. The only external interactions are via web_search/web_fetch, which are appropriate for the stated purpose.

Persistence & Privilege

always:false (default) and the skill is user-invocable. It does not request persistent elevated privileges or modify other skills. It writes outputs into a workspace subdirectory (normal for report generation).

Assessment

This skill appears to do what it says: run searches, fetch pages, synthesize, and save reports. Before installing or running, consider: 1) Privacy: follow-up queries are generated from fetched content (titles/summaries) and may send scraped text back to search providers — avoid feeding proprietary or sensitive documents into the pipeline. 2) Data at rest: reports and fetched page text are saved to workspace/research; secure or clean that folder if it may contain sensitive info. 3) Trust of search/fetch backends: SKILL.md mentions SearXNG (self-hostable) but actual web_search/web_fetch implementations determine where queries and fetched content go — confirm those tool endpoints are ones you trust. 4) Operational controls: you can limit max follow-ups and source counts (script supports flags) to reduce exposure and cost. If you need stronger assurances, review the full script in your environment and run it in a sandboxed account before giving it access to sensitive data.

Web Research Skill

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

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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

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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

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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

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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.

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Output Files

Reports saved to: workspace/research/web-research-YYYY-MM-DD-<topic>.md

JSON reports: workspace/research/web-research-YYYY-MM-DD-<topic>.json

HTML reports: workspace/research/web-research-YYYY-MM-DD-<topic>.html

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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

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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

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Pricing

Tier	Price	Description
Single report	€25-50	One research question, full pipeline
Batch research	€50-100	Multiple questions (up to 5)
Deep dive	€75-150	Extended follow-ups, expert sources
Retainer	€100-300/mo	Ongoing research, weekly reports

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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

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Version History

Version	Date	Changes
1.0.0	2026-04-19	Initial release
2.0.0	2026-04-27	Follow-up queries, quality scoring, batch mode, multiple formats
2.1.0	2026-04-27	HTML output, improved topic extraction, deduplication