football-prediction-avatar-video — security grade SAFE, quality 56/100

Security audit verdict: SAFE · quality 56/100

No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →

by zlong6943-commits · Codex Skill · ★ 9

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

🔒 Is football-prediction-avatar-video safe to install? View the security audit →

About football-prediction-avatar-video

Codex skill for verified Chinese football prediction avatar videos with official logos, captions, motion graphics, and approval gates.

Quick Facts

Stars9
Forks0
LanguagePython
CategoryCodex Skill
Quality Score56.2463047202207/100
Last Updated2026-09-05
Created2026-08-13
Platformscodex, python
Est. Tokens~14k

Compatible Skills

These tools work well together with football-prediction-avatar-video for enhanced workflows:

  • podcli — semantic(0.18)+complementary+same_lang+similar_pop+shared_platform (56%)
  • prediction-market-agent-tooling — semantic(0.17)+complementary+same_lang+similar_pop+shared_platform (56%)

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Frequently Asked Questions

What is football-prediction-avatar-video?

football-prediction-avatar-video is Codex skill for verified Chinese football prediction avatar videos with official logos, captions, motion graphics, and approval gates.. It is categorized as a Codex Skill with 9 GitHub stars.

What programming language is football-prediction-avatar-video written in?

football-prediction-avatar-video is primarily written in Python.

How do I install or use football-prediction-avatar-video?

You can find installation instructions and usage details in the football-prediction-avatar-video GitHub repository at github.com/zlong6943-commits/football-prediction-avatar-video. The project has 9 stars and 0 forks, indicating an active community.

How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. 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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