anakin-inc/opencode-anakin/anakin-research

Anakin: search, research, and AI visibility

Three tools with very different cost and latency profiles. Picking the wrong one either wastes minutes or returns something too shallow to use.

The third answers a different question from the other two. search and agentic_search ask what is true on the web. ai_visibility_search asks what do AI assistants tell people — which is a brand/AI-SEO question, not a factual one. Do not substitute one for the other.

search — find pages

Synchronous AI web search. Returns a results array of url, title, snippet, date. No polling.

• prompt — natural-language query.
• limit (default 5, max 20).

This is a discovery tool. It returns snippets, not page content. The normal pattern is search to find candidates, then scrape the ones worth reading (see the anakin-web-data skill).

Use search when you will decide what to read, or when you need citations you can hand back to the user.

agentic_search — deep research

An async pipeline: it searches, scrapes the most relevant citations, and uses an LLM to synthesize a unified structured answer. Typically 1–5 minutes.

• prompt — the research question. Be specific; the engine infers scope from it.
• schema — optional JSON Schema for the output shape. **Supply this whenever

you know the shape you want** — it is the difference between prose you have to re-parse and typed data you can use directly. Omit it and the engine infers a schema from the prompt.

• useBrowser — defaults to true here, unlike other tools, because cited

sources are often JS-heavy.

Returns summary, structured_data, and the data_schema actually used.

Use it when one URL or a flat list of search hits genuinely cannot answer the question: comparative analysis, multi-jurisdictional research, market intelligence, anything requiring synthesis across sources.

ai_visibility_search — what AI engines say

Asks multiple AI answer engines (ChatGPT, Gemini, Google AI Overview) the same question and compares their answers. Returns one result per engine — status, answer summary, latency, credits, and a consensus/outlier verdict — plus an AI-generated synthesis of where they agree and diverge. Async, typically 1–2 minutes, polls to completion.

• query (required) — the question to put to every engine.
• sources — engine slugs to query. Call ai_visibility_sources first to see

what is available and currently enabled.

• country — for geo-specific AI answers.
• include_full_content — leave this off by default. Each engine's raw full

answer is large; the summaries and synthesis are usually what you need.

Billed per source at that Wire action's rate. Failed sources are free.

Use it for: brand visibility ("what do AI engines say about X"), tracking how answers differ across engines, and geo-specific answer checks.

ai_visibility_sources takes no arguments and just lists slugs plus display labels. Call it when you want a subset rather than all engines.

Choosing well

Do not reach for agentic_search when:

• The answer lives on one page you can already name → scrape it.
• You need a list of links → search.
• The data is on a specific popular site → check wire_discover first

(anakin-wire skill); a pre-built action is faster and returns clean fields.

• The question is what AI engines say rather than what the web contains →

ai_visibility_search.

• The user wants to be told when something changes → a monitor, not a repeated

search (anakin-monitoring).

Do reach for it when a thorough answer would otherwise mean a dozen search + scrape round-trips. It is doing exactly that work, better scoped.

Practical notes

• Tell the user before starting an agentic_search — minutes of silence

otherwise reads as a hang.

• Define a schema up front for anything feeding a table, report, or downstream

code.

• Results can be served from cache (cached in the response); pass a more

specific prompt if you need a genuinely fresh run.