research
Skill1 starsChanged 32 days agoMITView on GitHub
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:research
189 other projects use a version of this.
Adopted by
Most recently taken up by
- dialog-db/dialog-dbtheir own version, first seen Nov 9, 2026
- howells/skillstheir own version, first seen Nov 8, 2026
- xiaoxiaosrm/dsh-mattpocock-skillstheir own version, first seen Nov 8, 2026
- Suge8/friedbuntheir own version, first seen Nov 8, 2026
- wowyuarm/dsh-agent-teamtheir own version, first seen Nov 8, 2026
- Contexera/dsh-agent-teamtheir own version, first seen Nov 8, 2026
Used with
Projects with it also use daily-arxiv, exp-eval and 10 more.
Other files people use alongside this one.
Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
LambenthanSkilldaily-arxiv
每日从 arXiv 拉取新论文,过滤相关性,auto-ingest 高优先级论文,检测 SOTA 更新
Also in 5 projects that use this
LambenthanSkillexp-eval
实验判决门:Review LLM 独立评判实验结果 → 4 种判决路径 → 自动更新 claims confidence、ideas status、graph edges
Also in 5 projects that use this
truongtv22SkillProblem-Solving Techniques
Apply systematic problem-solving techniques for complexity spirals (simplification cascades), innovation blocks (collision-zone thinking), recurring patterns (meta-pattern recognition), assumption constraints (inversion exercise), scale uncertainty (scale game), and dispatch when stuck. Techniques derived from Microsoft Amplifier project patterns adapted for immediate application.
Also in 5 projects that use this
liufeng1976Skillsetup-ts-deep-modules
Wire dependency-cruiser into a TypeScript repo so each package is a deep module — implementation hidden in subfolders, reachable only through its entry-point files. User-invoked.
Also in 43 projects that use this
skyllwtSkillexp-pilot-eval
Pilot result evaluation — read pilot results, apply success criteria, update idea page (pilot_result, failure_reason if failed), generate PILOT_VERDICT_REPORT. Called by /ideate Phase 5 after /exp-pilot-run.
Also in 4 projects that use this
skyllwtSkillexp-pilot-run
Pilot experiment execution — read Pilot Spec YAML, write pilot code, run experiment(Confirm with the user before operation and require the applicant to conduct manual inspection), return results. Called by /ideate Phase 5. Does NOT modify wiki pages or judge pass/fail.
Also in 4 projects that use this
- +5 more from Lambenthan
- +1 more from skyllwt
Other versions189 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 189
- mattpocock/skills★ 309ksame name
- Yeachan-Heo/oh-my-claude-sisyphus★ 41ksame name
- Yeachan-Heo/oh-my-claudecode★ 41ksame name
- QuivrHQ/quivr★ 40ksame name
- The-Vibe-Company/quivr★ 40ksame name
- udecode/plate★ 17ksame name
- openai/plugins★ 7.8ksame name
- ArcReel/ArcReel★ 6.3ksame name
- vinvcn/mattpocock-skills-zh-CN★ 5.5ksame name
- withcoral/coral★ 4.9ksame name
179 more not listed
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository3 other files it gives its agents.
- crewSkill
- handoffSkill
- prd-reviewSkill
The file
We split this skill into the rules it gives an agent
4 rules. 0 are common, 1 is distinctive, and 3 are very specific to this project.
0
Common
Most of the 169 edited versions we read keep these.
1
Distinctive
This author's own choices. Worth a look.
3
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Very specific: line 23
Run agent-reach doctor --json if available to see which backends are live, then choose per angle:
- Very specific: line 32
If Agent-Reach isn't installed and the user wants the full reach, tell them once: 帮我安装 Agent …
- Very specific: line 41
If the research feeds a build, the dispatch rule holds: the target project is confirmed from the session's badge and cwd before any …
- Distinctive: line 51
Write a tight, cited answer: the finding, the evidence (with links + dates), the disagreements, and the confidence.
“Common” is measured: kept by at least 6 in 10 of the 169 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
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).
If the research feeds a build, the dispatch rule holds: the target project is confirmed from the
session's badge and cwd before any relay_send, relay_task_add or trantor up — a research
answer ("where the answers are stored") is not a project name — and a session that is asking the
operator a question never triggers a wake; its messages batch until the answer arrives.
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.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

