research-topic

research-topic

Drive wet-mcp's extract(action="agent") to answer a research question end to end: one search round + concurrent extracts of the top hits + a single LLM synthesis pass that preserves numbered [N] citations matching the returned sources.

Use this skill when:

• The user asks an open-ended question that needs multiple sources.
• "Summarise the current state of X."
• "What's the latest on Y?"
• "Compare approaches to Z."
• The user needs a quoted, cited answer (the citations are first-class output, not an afterthought).

Do NOT use this skill when:

• The user already gave you a specific URL -- call extract(action="extract").
• The user wants a single search result list -- call search(action="web").
• The question is about library API documentation -- call search(action="docs_query") against a Tier 1 / locked stack.

Steps

1. Restate the question to the user in 1-2 sentences (calibration: confirm scope before spending tokens).

2. Pick max_urls based on breadth:

   • 3-5 for a tight question (single technology, single timeframe).
   • 6-10 for a broad survey (multiple competitors, multi-year window).
   • Hard ceiling is 20 (cost guard).

3. Pick synthesis_model only if the user asked for a specific model. Otherwise omit and let wet auto-detect from LLM_MODELS / GEMINI_API_KEY / OPENAI_API_KEY / XAI_API_KEY.

4. Call

   extract(action="agent", query="<question>", max_urls=<N>)
   

   Optional knobs: synthesis_model="...", token_budget=<int> (default 10000; raise for long-form questions, lower for tight cost control).

5. Quote the synthesised Markdown verbatim to the user, then list the sources from the sources array as clickable URLs. If per_url_metadata shows any error, mention which URL failed and that the synthesis used the remaining N-K sources.

6. If wet returns Error: no LLM provider detected, surface the exact error to the user (do not silently retry against search(action="research")); they need to set one of the supported API keys before agent works.