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 umputun · Codex Skill · ★ 1.5k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is ralphex safe to install? View the security audit →
Autonomous plan execution with Claude Code ralphex is a standalone CLI tool that runs in your terminal from the root of a git repository. It orchestrates Claude Code to execute implementation plans autonomously - no IDE plugins or cloud services required, just Claude Code and a single binary. Claude Code is powerful but interactive - it requires you to watch, approve, and guide each step. For complex features spanning multiple tasks, this means hours of babysitting. Worse, as context fills up during long sessions, the model's quality degrades - it starts making mistakes, forgetting earlier decisions, and producing worse code. ralphex solves both problems. Each task executes in a fresh Claude Code session with minimal context, keeping the model sharp throughout the entire plan. Write a plan with tasks and validation commands, start ralphex, and
| Stars | 1,483 |
| Forks | 125 |
| Language | Go |
| Category | Codex Skill |
| License | MIT |
| Quality Score | 70.7506052541706/100 |
| Open Issues | 4 |
| Last Updated | 2026-09-14 |
| Created | 2026-01-19 |
| Platforms | claude-code, codex, go |
| Est. Tokens | ~27k |
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ralphex is Extended Ralph loop for autonomous AI-driven plan execution. It is categorized as a Codex Skill with 1.5k GitHub stars.
ralphex is primarily written in Go. It covers topics such as automation, claude-code, codex.
You can find installation instructions and usage details in the ralphex GitHub repository at github.com/umputun/ralphex. The project has 1.5k stars and 125 forks, indicating an active community.
ralphex is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to ralphex on Agent Skills Hub include awesome-agent-orchestrators, clay, cursor-talk-to-figma-mcp. 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: