genesis: Markdown that steers an LLM is code

by danielmeppiel · Codex Skill · ★ 61

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

How to install genesis

npx skills add danielmeppiel/genesis

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-orchestrationagent-skillsagentic-aiai-coding-agentsapmclaude-codecodexcursorgbb-ip-atlasgithub-copilot

Quick Facts

Stars61
Forks9
LanguagePython
CategoryCodex Skill
LicenseApache-2.0
Quality Score68/100
Open Issues2
Last Updated2026-05-30
Created2026-04-25
Platformsclaude-code, codex, python
Est. Tokens~2277k

About genesis

genesis Markdown that steers an LLM is code. Design it before you write it. Genesis ports the software architect's role to agentic systems -- decomposition, contracts, and cross-cutting concerns applied to workflows where LLMs are the runtime. Software engineering needed architecture, not just style guides, the moment systems crossed a complexity threshold.

Compatible Skills

These tools work well together with genesis for enhanced workflows:

  • Carmack-Council — semantic(0.25)+complementary+rare_topics+same_lang+similar_pop+shared_platform (58%)
  • prompt-architect — semantic(0.21)+complementary+rare_topics+same_lang+similar_pop+shared_platform (57%)
  • reposwarm — semantic(0.19)+complementary+same_lang+similar_pop+shared_platform (57%)
  • robotics-agent-skills — semantic(0.19)+complementary+same_lang+similar_pop+shared_platform (57%)

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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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