market-research-skills: Turn Claude into a disciplined research analyst — verify facts against primary sources, brief any topic, draft

by genli-ai · Claude Skill · ★ 59

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

How to install market-research-skills

/plugin marketplace add https://github.com/genli-ai/market-research-skills.git
/plugin install market-research-skills@market-research-skills

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

Security audit verdict: SAFE · quality 68/100

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-skillsclaude-codeclaude-skillskills-sh

Quick Facts

Stars59
Forks6
LanguagePython
CategoryClaude Skill
LicenseMIT
Quality Score68/100
Open Issues2
Last Updated2026-06-09
Created2026-05-12
Platformsclaude-code, python
Est. Tokens~34k

About market-research-skills

Market Research Skills 中文版本见本文件下半部分 / Chinese version below Turn Claude — or any LLM terminal — into a disciplined research analyst. A collection of Claude Skills covering the full market / equity / industry research workflow: verify a fact against primary sources, spin up a topic briefing, or draft a flagship report — each step is its own skill.

Compatible Skills

These tools work well together with market-research-skills for enhanced workflows:

  • Lobster-Research — semantic(0.32)+complementary+same_lang+similar_pop+shared_platform (56%)
  • BITE — semantic(0.28)+complementary+same_lang+similar_pop+shared_platform (55%)
  • senior-analyst — semantic(0.26)+complementary+same_lang+similar_pop+shared_platform (54%)

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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:

View on GitHub → Browse Claude Skill tools