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 baskduf · Codex Skill · ★ 436
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is FableCodex safe to install? View the security audit →
FableCodex Evidence-based workflow gates for Codex. English | 한국어 | 日本語 | 简体中文 | 繁體中文（台灣） FableCodex is a Codex plugin that adds Fable-inspired operating habits to Codex work: inspect first, track goals, record evidence, close review findings, and verify before claiming completion. It is useful when the cost of a missed step is higher than the cost of a little process. FableCodex does not clone, unlock, or replace the Fable 5 model. It cannot change model weights, context length, training, or hidden safety systems.
| Stars | 436 |
| Forks | 53 |
| Language | Python |
| Category | Codex Skill |
| License | AGPL-3.0 |
| Quality Score | 76.2502458581048/100 |
| Open Issues | 9 |
| Last Updated | 2026-07-26 |
| Created | 2026-06-14 |
| Platforms | codex, python |
| Est. Tokens | ~15k |
These tools work well together with FableCodex for enhanced workflows:
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FableCodex is 🗿 FableCodex is a Codex-style coding agent workflow that plans like Fable.. It is categorized as a Codex Skill with 436 GitHub stars.
FableCodex is primarily written in Python. It covers topics such as codex, context-engineering, fable-5.
You can find installation instructions and usage details in the FableCodex GitHub repository at github.com/baskduf/FableCodex. The project has 436 stars and 53 forks, indicating an active community.
FableCodex is released under the AGPL-3.0 license, making it free to use and modify according to the license terms.
The top alternatives to FableCodex on Agent Skills Hub include claude-workflow-v2, agentsys, awesome-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: