| 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:
- Lead with the answer
- Cite sources inline using the URLs from
claims[].sources
- Mention confidence: "Confidence: 78% based on 5 sources"
- If contradictions exist, surface them: "Note: sources disagree on X"
- 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
