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AI EngineeringHTML

Bear Scoop

by xuzhougeng

Bear Scoop is an AI Engineering skill for Claude Code, published by xuzhougeng in wisp-science.

1K stars106 forkson xuzhougeng/wisp-scienceAdded 2026/08/21+1% in starsRepository updated 2026/08/21
agent-skillsai-agentai-assistantai-for-scienceai4sciencebioinformaticscomputational-biologydesktop-appllmlocal-firstmcpmodel-context-protocolpythonreproducible-researchresearch-assistantrstatsrustscientific-computingscientific-workflowtauri
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Install Bear Scoop
Copy Bear Scoop into your Claude Code skills folder. Run the command in your terminal, or review the source on GitHub before installing.
terminal
npx degit https://github.com/xuzhougeng/wisp-science/tree/main/skills/bear-scoop ~/.claude/skills/bear-scoop

Requires Node.js. Downloads this skill only — not the rest of the repository — into your Claude Code skills folder.

Without Node.js

git clone https://github.com/xuzhougeng/wisp-science.git

Clones the whole repository, then copy the skill’s own directory into your skills folder yourself.

In this catalog

Source file
skills/bear-scoop/SKILL.md in xuzhougeng/wisp-science
Installs to
~/.claude/skills/bear-scoop
Collection
One of 25 skills cataloged from this repository
Category
AI Engineering2451 skills

What Bear Scoop does

Bear Scoop checks whether an idea is already being pursued by searching from several angles and ranking nearby papers by collision risk. Use it before submitting or writing to see the closest competing work, especially with the -r flag for recent preprints.

Bear Scoop is cataloged under AI Engineering on DirSkills. Bear Scoop comes from a repository tagged agent-skills, ai-agent, ai-assistant, ai-for-science and ai4science.

Documentation

README

bear-scoop · 选题撞车检测

一个任务:在你投入之前,看看谁坐在离你最近的位置。抢发你的那组人,用的词往往不是你会用的词——这里从你不会想到的角度去搜。

先读 references/sci-cli.md——CLI 检测步骤、用法和铁律。 再读 references/output-system.md——三层输出体系和 HTML 外壳规范。

Flags

  • 默认:全角度扫描
  • -r:只看近一两年和预印本(撞车风险最高的地方)

步骤(固定顺序)

Step 0 — 确认 CLI 可用 运行 sci --version。未安装就给安装命令并停止;认证错误就提示 sci init 并停止。

Step 1 — 压缩 idea 把 idea 提炼成一句话并确认。模糊的 idea 搜出来的是噪音,不是信号。

Step 2 — 分解成 5–6 个正交角度 同一项工作,从不同立场描述:

  1. 字面表述(用户自己的说法)
  2. 方法中心(技术 / 工具,剥离应用场景)
  3. 问题中心(要解决的问题,剥离方法)
  4. 结论中心(预期会声称的具体结果)
  5. 相邻领域的表达方式(另一个学科会怎么叫这件事)
  6. 抢发者的标题(如果有人比你快发了,那篇论文会叫什么)

在输出里明确列出这 5–6 个角度,每个附一行说明和实际使用的查询词。 用户需要看见你从哪些方向搜了,才能判断覆盖是否够全,也能自己补充遗漏的角度。列完后直接开始检索,不等待用户确认(除非 idea 本身太模糊)。

Step 3 — 分角度检索 宽泛角度用 ultra_low(免费),有收获的两三个用 low 复跑。加 -r 时偏向近期结果。每个角度用独立的 --prefix

This is the opening of the README. Read the full README on GitHub.

Frequently asked about Bear Scoop

  • What else does xuzhougeng publish alongside Bear Scoop?

    Bear Scoop is one of 25 skills that DirSkills catalogs from xuzhougeng/wisp-science, the repository it ships in. Its siblings there include Agent Infini, Bear Counter and Bear Map. Each one is a separate skill with its own page in this directory, installs the same way Bear Scoop does, and is maintained by xuzhougeng in that same repository. The rest of the collection is listed on the xuzhougeng/wisp-science page.

  • How does Bear Scoop compare to other AI Engineering skills?

    Bear Scoop ranks #1355 by stars among the 2451 AI Engineering skills in this catalog. The most-starred ones next to it are Architecture Decision Records, AI-First Engineering and Agentic OS. DirSkills ranks by the star count of the repository each skill ships in, so that order reflects how popular those repositories are rather than any review of Bear Scoop against them. Open each page to compare what they document and how they install.

More from xuzhougeng/wisp-science

Bear Scoop is one of 25 skills cataloged on DirSkills from xuzhougeng/wisp-science.

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Bear Counter

Bear Counter finds real academic papers that challenge a claim with opposing results, boundary conditions, alternative explanations, method critiques, or replication failures. Use it when you want to stress-test a conclusion or prepare for reviewer objections.
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Bear Map

Bear Map builds a concept map around a term using real paper abstracts, so each node is anchored to retrieved literature. It also outputs Mermaid and standalone HTML for screenshots, plus 3–6 starter papers.
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Bear Onboard

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Bear Propose

Bear Propose evaluates a research idea before you commit to it by checking collision risk, support in the quiet zone, and likely challenges. It runs real literature searches and summarizes the evidence without inventing citations.
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Bear Review

Bear Review compares evidence supporting and opposing a claim, then summarizes the strongest support, the most serious counterarguments, and gaps where one side is missing. It is used for balanced, full-scope assessment of a viewpoint with real literature search.
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