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 scitix · Codex Skill · ★ 233
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is siclaw safe to install? View the security audit →
Siclaw Read-only investigation copilot for DevOps and SRE teams Website Slack Siclaw is an open-source AI agent for DevOps and SRE teams. It is built for read-only infrastructure diagnostics: gather evidence, form hypotheses, validate them, and return a clear root-cause analysis without changing your environment directly. Describe a problem in plain language and Siclaw investigates it from the terminal, the web UI, or your te
| Stars | 233 |
| Forks | 29 |
| Language | TypeScript |
| Category | Codex Skill |
| License | Apache-2.0 |
| Quality Score | 71.6667993049335/100 |
| Open Issues | 30 |
| Last Updated | 2026-09-14 |
| Created | 2026-03-02 |
| Platforms | k8s, node |
| Est. Tokens | ~16k |
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siclaw is AI-powered SRE platform — read-only infrastructure diagnostics with deep investigation, security governance, and team collaboration. It is categorized as a Codex Skill with 233 GitHub stars.
siclaw is primarily written in TypeScript. It covers topics such as agent, ai-agent, ai-agents.
You can find installation instructions and usage details in the siclaw GitHub repository at github.com/scitix/siclaw. The project has 233 stars and 29 forks, indicating an active community.
siclaw is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to siclaw on Agent Skills Hub include ongrid, clanker, awesome-ai-sre. 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: