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 William-Lu-stack · MCP Server · ★ 815
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is Flawless safe to install? View the security audit →
Flawless Your infrastructure can explain itself, heal safely, and prove it recovered. 让基础设施自己解释故障、安全完成修复，并证明它真的恢复了。 Flawless is an AI-native SRE control plane for Kubernetes and cloud infrastructure. It connects alerts, evidence, topology, human approval, controlled remediation, and recovery verification in one auditable AgenticOps loop. 它不是另一个只会给建议的运维聊天框。Flawless 将“发现问题、收集证据、生成预演、人工授权、执行变更、恢复验证、经验沉淀”连接成一个可审计闭环。 Created in Shanghai by 陆宣宇 (Xuanyu Lu). Product Preview / 产品实景 These are real console captures from the running platform, not conceptual mockups. | ![Flawless AI inspection produces an evidence-backed remediation pr
| Stars | 815 |
| Forks | 197 |
| Language | Python |
| Category | MCP Server |
| Quality Score | 63.5970615001635/100 |
| Open Issues | 2 |
| Last Updated | 2026-08-14 |
| Created | 2026-07-10 |
| Platforms | k8s, mcp, python |
| Est. Tokens | ~19k |
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Flawless is AI SRE AgenticOps for Kubernetes and cloud infrastructure.. It is categorized as a MCP Server with 815 GitHub stars.
Flawless is primarily written in Python. It covers topics such as agenticops, ai, aiops.
You can find installation instructions and usage details in the Flawless GitHub repository at github.com/William-Lu-stack/Flawless. The project has 815 stars and 197 forks, indicating an active community.
The top alternatives to Flawless on Agent Skills Hub include ongrid, holmesgpt, aura. 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: