Bear Propose

Bear Propose 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 Propose

Copy Bear Propose 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-propose ~/.claude/skills/bear-propose

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-propose/SKILL.md in xuzhougeng/wisp-science
Installs to: ~/.claude/skills/bear-propose
Collection: One of 25 skills cataloged from this repository
Category: AI Engineering — 2451 skills

What Bear Propose does

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.

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

Documentation

README

Excerpt

bear-propose · 研究立项前评估

一个工作流：在你正式投入之前，把撞车风险、安静区的证据基础、潜在挑战这三件事同时查清楚。这三步不是简单串联——撞车检测的结果会影响后两步的检索方向：支持和挑战的检索聚焦在安静区，而不是整个 idea 的全貌。这让 bear-propose 比分别跑三个 skill 更有价值。

先读 references/sci-cli.md——CLI 检测步骤、用法和铁律。再读 references/output-system.md——HTML 外壳规范和设计 token。

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，执行撞车检测 把 idea 提炼成一句话。从 5–6 个正交角度检索（字面表述 / 方法中心 / 问题中心 / 结论中心 / 相邻领域 / 抢发者标题），宽泛角度用 ultra_low，有收获的角度用 low 复跑，合并去重，按四层分类（直接撞车 / 方法孪生 / 问题孪生 / 邻居）。

告知用户本次将执行约 15–20 次检索，然后直接开始，不等待确认。

Step 2 — 识别安静区 根据撞车结果，明确哪些角度拥挤、哪些角度安静。安静区是后续检索的重点——在安静区里找支撑，才是真正有意义的文献基础。

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

Frequently asked about Bear Propose

What else does xuzhougeng publish alongside Bear Propose?
Bear Propose 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 Propose 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 Propose compare to other AI Engineering skills?
Bear Propose ranks #1353 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 Propose against them. Open each page to compare what they document and how they install.