Research

Run a real multi-source web/social research pass with the crew's economics: fan out across the internet (Agent-Reach channels when installed — web/Jina, Exa search, GitHub, YouTube/Bilibili transcripts, Twitter, Reddit, RSS, plus the Chinese platforms — else your own web tools), push the cheap per-source reading to Scrooge, and synthesize a cited answer yourself. Use when the user wants to research / deep-dive / "search the web" / "see what people say about X" / look something up across many sources. Trigger: /trantor:researchFrom its SKILL.md

Install

npx -y skills add sashabogi/trantor --skill 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

1 stars1 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

SKILL.md

3.1 KB, as published. Nobody here has run it

Trantor research — fetch wide, read cheap, synthesize sharp

You are the ARCHITECT of a research pass. The shape mirrors the crew playbook: fan out to fetch, delegate the cheap reading, keep the synthesis. Never burn frontier tokens scrolling raw pages.

1. Scope the question

Restate the question in one line and list the angles that need coverage (e.g. official source, practitioner discourse, dissent, recency). Underspecified question → ask 1–2 sharp clarifiers first.

2. Pick sources (capability, not habit)

Run agent-reach doctor --json if available to see which backends are live, then choose per angle:

Semantic web search → mcporter call 'exa.web_search_exa(query: "…", numResults: 5)' (free, no key)
Read a page/article → curl -s "https://r.jina.ai/<URL>" (clean markdown, no HTML soup)
Code / repos → gh search repos "…" --sort stars · gh repo view owner/repo
Video → yt-dlp --write-sub --skip-download -o /tmp/%(id)s "<URL>" (NEVER yt-dlp for Bilibili → bili …)
Discussion → Twitter (twitter search), Reddit (opencli reddit search / rdt search), V2EX
Chinese platforms (often weeks ahead on AI) → Bilibili, XiaoHongShu, Zhihu, Weibo, Xueqiu via Agent-Reach
No Agent-Reach? Fall back to your own WebSearch/WebFetch — the method below still holds.

If Agent-Reach isn't installed and the user wants the full reach, tell them once: 帮我安装 Agent Reach：https://raw.githubusercontent.com/Panniantong/agent-reach/main/docs/install.md

3. Fan out + read cheap

Collect candidate sources in parallel across angles. For each non-trivial source, push the read-and-extract to a cheap model instead of reading it yourself: scrooge -t summarize -d easy "<extract the claims + numbers + date relevant to: …>" < source.md (or relay_scrooge from inside the crew). Many independent small reads → add --spread N. The point: frontier tokens go to judgment, cheap tokens go to reading. Announce it (scrooge watch shows the feed).

4. Verify before you trust

Cross-check any load-bearing claim against a second independent source. Flag what's a single-source claim, what's dated, what's contested. Translate non-English sources and keep the original link.

5. Synthesize (this part is yours)

Write a tight, cited answer: the finding, the evidence (with links + dates), the disagreements, and the confidence. Lead with what the user didn't already know. End with the open questions a follow-up pass should chase.