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 covibes · Codex Skill · ★ 1.5k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is zeroshot safe to install? View the security audit →
zeroshot CLI 🎉 New in v5.4: Now supports OpenCode CLI! Use Claude, Codex, Gemini, or OpenCode as your AI provider. Also supports GitHub, GitLab, Jira, and Azure DevOps as issue backends. See Providers and Multi-Platform Issue Support. npm install -g @covibes/zeroshot Demo (100x speed, 90-minute run, 5 iterations to approval) Zeroshot is an open-source AI coding agent orchestration CLI that runs multi-agent workflows to autonomously implement, review, test, and verify code changes. It runs a planner, an implementer, and independent validators in isolated
| Stars | 1,506 |
| Forks | 128 |
| Language | JavaScript |
| Category | Codex Skill |
| License | MIT |
| Quality Score | 72.8036377755728/100 |
| Open Issues | 83 |
| Last Updated | 2026-06-14 |
| Created | 2025-12-25 |
| Platforms | claude-code, cli, codex, gemini, node |
| Est. Tokens | ~18k |
These tools work well together with zeroshot for enhanced workflows:
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zeroshot is Your autonomous engineering team in a CLI. Point Zeroshot at an issue, walk away, and return to production-grade code. Supports Claude Code, OpenAI Codex, OpenCode, and Gemini CLI.. It is categorized as a Codex Skill with 1.5k GitHub stars.
zeroshot is primarily written in JavaScript. It covers topics such as agent-orchestration, agentic-workflow, ai-agent.
You can find installation instructions and usage details in the zeroshot GitHub repository at github.com/covibes/zeroshot. The project has 1.5k stars and 128 forks, indicating an active community.
zeroshot is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to zeroshot on Agent Skills Hub include happier, Overture, SWE-AF. 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: