hehe-industry-research-skill-pack: 盒盒原创中文行业与企业研究技能包：1 个总入口协同 13 个专项 Skill，支持完整行业研究或企业研究、以及市场格局

by hexiaofeier · Agent Tool · ★ 297

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How to install hehe-industry-research-skill-pack

git clone https://github.com/hexiaofeier/hehe-industry-research-skill-pack.git

Taken from the project's README on GitHub. Check the security audit before running it.

Security audit verdict: SAFE · quality 65/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

agent-skillscompany-researchfinancial-analysisindustry-analysisindustry-chain-mapindustry-researchmarket-analysismarket-researchmarket-sizing

Quick Facts

Stars297
Forks19
LanguagePython
CategoryAgent Tool
LicenseMIT
Quality Score65/100
Last Updated2026-10-06
Created2026-09-15
Platformspython
Est. Tokens~15k

About hehe-industry-research-skill-pack

盒盒行业研究技能包 一套由总入口统筹、13 个专项 Skill 协同执行的中文行业与企业研究工作流。 Hehe Industry Research Skill Pack 面向使用公开信息开展行业研究、企业研究、商业尽调和投资研究的 AI Agent。它由 管理项目，再按任务调用市场规模、产业链、商业模式、竞争格局、财务、估值等 13 个专项 Skill。 “盒盒”是这套原创研究方法的识别前缀，因此公开版本统一使用 名称。每个专项都能独立调用；完整研究则由总入口负责开题、搜索组织、材料审核、专项验收和最终整合。 主要更新：交付 HTML 报告 2026-09-21 · rc7：完整行业研究、企业研究和行业＋企业研究，现在默认同时交付 Markdown 主报告＋同内容 HTML 阅读版。 Markdown 用于编辑，HTML 用于阅读。...

Compatible Skills

These tools work well together with hehe-industry-research-skill-pack for enhanced workflows:

  • senior-analyst — semantic(0.42)+complementary+rare_topics+same_lang+similar_pop+shared_platform (74%)
  • Lobster-Research — semantic(0.32)+complementary+rare_topics+same_lang+similar_pop+shared_platform (66%)
  • Mira — semantic(0.31)+complementary+rare_topics+same_lang+similar_pop+shared_platform (65%)

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. It is a first-layer scan, not a manual audit — we say so rather than overstate it.

The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.

Sources & who's responsible:

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