search-topic

name	search-topic
description	Do deep research on a topic and return a curated list of sources relevant to the book. Use whenever the user wants to find or research sources on a subject — "find papers on X", "what's the research on Y", "search for sources about Z", "any good sources on ...". Prioritises arXiv and other academic/primary sources but also returns Wikipedia (definitions/history), analyst and industry reports, and reputable blog/news articles. Returns a ranked list (topic, title, type, year, URL, why-relevant) — it does NOT download; pair with download-source next.
argument-hint	The topic or claim to research for the book.
user-invocable	true

search-topic

Given a topic, find the sources worth pulling into the book and hand back a clean, actionable list. This is the discovery stage — no downloading, no writing. The output is a shortlist a human (or the research-topic orchestrator) can act on.

The book is AI-dō, a practical, evidence-led guide to working with AI; it favours primary sources (it has deliberately replaced encyclopedia citations with primary ones) and values balance over cheerleading. Search with that in mind.

What to return

A Markdown table, most-relevant first:

Topic	Title	Type	Year	URL	Why relevant

• Topic — the sources/ topic folder it maps to (an existing one where it fits; otherwise propose a new kebab-case topic name and say it's new). Existing topics include llm-foundations, ai-landscape, agent-architecture, agent-security, spec-vs-vibe, ai-coding, code-quality, memory-context, knowledge-work, personal-productivity, ambient-agents, agent-disciplines, governance-law, human-ai-future, mastery-improvement, philosophy-ethics, software-engineering.
• Type — arXiv, journal/conference, analyst report, wikipedia, blog/news, docs.
• URL — the canonical link: arXiv abs page, DOI, or the publisher/original page. For arXiv, note the id.
• Why relevant — one line: the claim it supports or the balance it adds.

Below the table, add a short note on coverage and balance: which angle each cluster of sources takes, whether you found a genuine counterweight, and any gap the search did not fill.

How to search

1. Frame the topic against the book: which chapter/theme does it serve, and what claim is it meant to support or challenge? A sharper frame gives sharper results.
2. Search broadly, then rank by quality and fit. Priority order:
   • arXiv and peer-reviewed / conference papers — primary evidence; prefer these. Capture the arXiv id.
   • Analyst and industry reports (Stanford HAI AI Index, McKinsey, Deloitte, State-of-AI) — for adoption/economics claims.
   • Primary/official pages (lab blogs, standards bodies, product docs) — for what a lab/tool actually says.
   • Wikipedia — only for definitions, history, or orientation, not as evidence for a contested claim.
   • Reputable blog/news — practitioner writing, journalism; use with care and prefer the primary it cites.
3. Verify the essentials at the source: exact title, author/org, year, canonical URL. Never rely on a search snippet or a guessed author list.
4. Seek a counterweight. If the topic is contested (most are), deliberately look for a source that complicates or opposes the majority view — balance beats a one-sided pile.
5. Filter hard. Drop SEO filler, content farms, and thin restatements. Prefer the primary over anything that merely quotes it. Flag paywalled or hard-to-download items so download-source knows.
6. Note likely overlap. If a candidate looks already covered in the book (the point is already cited), say so — the reader may not need it.

Rules

• Never invent a title, author, year, or URL. If you cannot confirm it, fetch the page or leave it flagged.
• Prefer the published venue where one exists; fall back to the arXiv/preprint link.
• Relevance to this book is the bar, not general interest — a great paper off-topic for AI-dō is a no.