HERO-Anti-OverDefense: HERO = Hashing · Edge cases · Rubrics · Overbuild — the four shapes coding agents over-defend in

by wanshuiyin · Codex Skill · ★ 741

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

Security audit verdict: SAFE · quality 60/100

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.

agents-mdai-agentsantigravityclaude-codeclaude-mdcodexcoding-agentcursorgemini-cligithub-copilot

Quick Facts

Stars741
Forks26
LanguagePython
CategoryCodex Skill
LicenseMIT
Quality Score60/100
Last Updated2026-11-02
Created2026-08-11
Platformsclaude-code, cli, codex, gemini, python
Est. Tokens~17k

About HERO-Anti-OverDefense

HERO — Anti-OverDefense 🧱 · · · · 🧱 You asked

Compatible Skills

These tools work well together with HERO-Anti-OverDefense for enhanced workflows:

  • agentrules-architect — semantic(0.46)+complementary+similar_pop+shared_platform (56%)
  • mda — semantic(0.24)+complementary+rare_topics+similar_pop+shared_platform (53%)

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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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