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 comet-ml · Codex Skill · ★ 725
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is opik-openclaw safe to install? View the security audit →
🔭 OpenClaw Opik Observability Plugin Official plugin for OpenClaw that exports agent traces to Opik for observability and monitoring. Why This Plugin Opik is a leading open-source LLM and age
| Stars | 725 |
| Forks | 88 |
| Language | TypeScript |
| Category | Codex Skill |
| License | Apache-2.0 |
| Quality Score | 73.7258780782926/100 |
| Open Issues | 14 |
| Last Updated | 2026-09-15 |
| Created | 2026-03-02 |
| Platforms | node |
| Est. Tokens | ~15k |
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opik-openclaw is 🦞 Official plugin for OpenClaw that exports agent traces to Opik. See and monitor agent behaviour, cost, tokens, errors and more.. It is categorized as a Codex Skill with 725 GitHub stars.
opik-openclaw is primarily written in TypeScript. It covers topics such as ai-agents, clawdbot, evaluation.
You can find installation instructions and usage details in the opik-openclaw GitHub repository at github.com/comet-ml/opik-openclaw. The project has 725 stars and 88 forks, indicating an active community.
opik-openclaw is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to opik-openclaw on Agent Skills Hub include clawmetry, lmnr, trpc-agent-go. 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: