Anakin research

Skill Anakin-Inc/agent-skills/skills/anakin-research

Use when answering a question from the web with Anakin rather than fetching a known URL — web search for relevant pages, multi-source deep research producing a structured answer, or checking what AI answer engines say about a brand or topic. Covers search, agentic_search, ai_visibility_search, ai_visibility_sources. Triggers on researching a topic, comparing options across sources, market or competitive intelligence, AI-SEO and brand visibility checks, finding sources for a claim, or gathering data that spans many sites.From its SKILL.md

Install

npx -y skills add Anakin-Inc/agent-skills --skill anakin-research

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

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SKILL.md

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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.

Need	Tool	Latency
Find URLs relevant to a query	`search`	immediate
A researched answer synthesized from many sources	`agentic_search`	1–5 min
What AI engines say about a topic or brand	`ai_visibility_search`	1–2 min

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.