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 thiientv · Codex Skill · ★ 92
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is godmode safe to install? View the security audit →
Godmode English | 简体中文 Your coding agent already knows how to code. Godmode teaches it how to engineer. Godmode is a composable catalog of engineering workflows and expert capabilities for AI coding agents. It helps agents design before editing, test before claiming, review independently, and verify with fresh evidence. The problem Coding agents are good at producing code. Without explicit engineering behavior, they can start too early, miss existing conventions, bolt tests on at the end, and call a plausible-looking result complete. Composition, not one giant prompt Godmode equips compatible agents to discover and compose the engineering behavior require
| Stars | 92 |
| Forks | 81 |
| Language | Python |
| Category | Codex Skill |
| License | MIT |
| Quality Score | 67.6582357446733/100 |
| Last Updated | 2026-08-26 |
| Created | 2026-08-13 |
| Platforms | claude-code, codex, python |
| Est. Tokens | ~16k |
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godmode is Production-grade Agent Skills for AI coding agents—composable workflows for planning, TDD, debugging, review, UI/UX, releases, incidents, and evals.. It is categorized as a Codex Skill with 92 GitHub stars.
godmode is primarily written in Python. It covers topics such as agent-evaluation, agent-skills, ai-agents.
You can find installation instructions and usage details in the godmode GitHub repository at github.com/thiientv/godmode. The project has 92 stars and 81 forks, indicating an active community.
godmode is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to godmode on Agent Skills Hub include metis, claude-skills, agent-skills. 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: