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 asklokesh · Codex Skill · ★ 1.0k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is loki-mode safe to install? View the security audit →
Featured in: Vibe Coding Stack
Loki Mode Build the future, faster. Describe what you want. Get production-ready code. Website Purple Lab Web UI How it works: Drop a spec -- a PRD, GitHub issue, OpenAPI/JSON/YAML, or one-line brief. Loki Mode classifies complexity (), assembles an agent team from 41 specialized types across 8 swarms, and runs autonomous RARV cycles (Reason - Act - Reflect - Verify, see ) with 11 quality gates (see ). Code is not "done" until it passes automated
| Stars | 1,032 |
| Forks | 200 |
| Language | Python |
| Category | Codex Skill |
| Quality Score | 69.2366996454525/100 |
| Open Issues | 6 |
| Last Updated | 2026-08-09 |
| Created | 2025-12-26 |
| Platforms | claude-code, cli, codex, gemini, python |
| Est. Tokens | ~3273k |
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loki-mode is Multi-agent autonomous SDLC framework. Spec to deployed app. PRD, GitHub issue, OpenAPI/JSON/YAML, or one-line brief. 5 AI providers, 11 quality gates.. It is categorized as a Codex Skill with 1.0k GitHub stars.
loki-mode is primarily written in Python. It covers topics such as ai-agents, aider, anthropic.
You can find installation instructions and usage details in the loki-mode GitHub repository at github.com/asklokesh/loki-mode. The project has 1.0k stars and 200 forks, indicating an active community.
The top alternatives to loki-mode on Agent Skills Hub include meridian, flow-next, agtx. 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: