browse-research

name	browse-research
description	Evidence-backed web research with citations and confidence scores. Use when the user needs researched, verified answers backed by real sources — not LLM hallucinations.

BrowseAI Dev — Evidence-Backed Research

Use this skill when the user needs researched, cited answers backed by real web sources — not LLM hallucinations.

When to Use

• User asks a factual question and wants verified, sourced answers
• User says "research this", "find out", "what does the evidence say", "look this up"
• User needs citations, confidence scores, or source verification
• User wants to know if something is true or needs fact-checking
• Any question where accuracy matters more than speed

Prerequisites

Install BrowseAI Dev MCP server:

{
  "mcpServers": {
    "browseai-dev": {
      "command": "npx",
      "args": ["-y", "browseai-dev"]
    }
  }
}

Or set BROWSE_API_KEY=bai_xxx for full features (sessions, sharing, knowledge export).

Workflow

Step 1: Research the Question

Use browse_answer to get a cited, evidence-backed answer:

browse_answer({ query: "How do mRNA vaccines work?", depth: "fast" })

Use depth: "thorough" when:

• The topic is nuanced or controversial
• You need high confidence (thorough auto-retries with rephrased queries if confidence < 60%)
• The user explicitly asks for deep research

Step 2: Interpret the Response

The response contains:

• answer: The synthesized answer from real sources
• claims[]: Individual claims, each with source URLs, verification status, consensus level
• sources[]: Each source with URL, title, domain, quote, authority score
• confidence: 0-1 score computed from 7 real factors (NOT LLM self-assessed)
• contradictions[]: Conflicting claims found across sources
• trace[]: Pipeline timing (search, fetch, extract, verify, answer)

Step 3: Present to the User

When presenting results:

1. Lead with the answer
2. Cite sources inline using the URLs from claims[].sources
3. Mention confidence: "Confidence: 78% based on 5 sources"
4. If contradictions exist, surface them: "Note: sources disagree on X"
5. If confidence < 50%, caveat: "Limited evidence available — treat with caution"

Confidence Score Guide

Range	Meaning
80-100%	Strong evidence, multiple corroborating sources
60-79%	Good evidence, some gaps
40-59%	Mixed evidence, contradictions possible
0-39%	Weak evidence, few or low-quality sources

Example

User: "Is intermittent fasting effective for weight loss?"

browse_answer({
  query: "Is intermittent fasting effective for weight loss? What does the research say?",
  depth: "thorough"
})

Present the answer with inline citations, highlight any contradictions between studies, and note the confidence score.

Clarity: Anti-Hallucination Answer Engine

Use browse_clarity to get answers with reduced hallucinations — two modes:

Fast (no internet) — LLM-only answer with anti-hallucination grounding techniques:

browse_clarity({ prompt: "Explain the causes of the 2008 financial crisis" })

Verified (with internet) — LLM answer + web pipeline, fused into one answer with source-backed claims:

browse_clarity({ prompt: "Explain the causes of the 2008 financial crisis", verify: true })

The response includes: answer, claims[] (each with origin: "llm", "source", or "confirmed"), confidence, techniques, risks, and verified (whether web sources were consulted).

Use fast mode when speed matters. Use verified mode when accuracy matters.

Tips

• Frame queries as specific questions, not keywords ("What causes aurora borealis?" not "aurora borealis")
• Include temporal context for time-sensitive topics ("latest AI regulations 2025")
• For controversial topics, expect contradictions — surface them rather than hiding them
• Use browse_search first if you just need URLs, not a full researched answer
• Use browse_harden before generating content to reduce hallucinations in the output

Links

• BrowseAI Dev
• Documentation
• MCP Server
• GitHub