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 kiyoakii · Codex Skill · ★ 131
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is is-gpt-nerfed safe to install? View the security audit →
English · 简体中文 You pick a model in Codex. This tells you whether that model is actually the one answering. If it isn't, you get this: 🎉 Congrats! You've been nerfed! You asked for gpt-6-astra; the fingerprint says gpt-5.6-luna (91%). Enjoy the discount you didn't ask for. Example output, not a real verdict. The panel, and a session opened in place. Sample data, not real sessions. All clear · Suspicious · Nerfed What it checks Everything runs on your Mac. Nothing is uploaded. Codex records, for every turn, which model and reasoning effort it asked for. The plugin reads those records after each turn and flags what changed without you changing it: a model swap, a lower reasoning effort, a hidden internal model such
| Stars | 131 |
| Forks | 4 |
| Language | Python |
| Category | Codex Skill |
| License | MIT |
| Quality Score | 65.8091536228756/100 |
| Last Updated | 2026-09-18 |
| Created | 2026-09-15 |
| Platforms | codex, python |
| Est. Tokens | ~15k |
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Python version of 📦 repopack (https://github.com/yamadashy/repopack) by yamadashy (https://github.com/yamadas
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is-gpt-nerfed is Shrinkflation detector for Codex. It is categorized as a Codex Skill with 131 GitHub stars.
is-gpt-nerfed is primarily written in Python. It covers topics such as codex, degradation, gpt.
You can find installation instructions and usage details in the is-gpt-nerfed GitHub repository at github.com/kiyoakii/is-gpt-nerfed. The project has 131 stars and 4 forks, indicating an active community.
is-gpt-nerfed is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to is-gpt-nerfed on Agent Skills Hub include Overture, repopack-py, ai-microcore. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
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: