agent-observability
De la autor
Design privacy-aware observability for AI agents using traces, spans, structured events, metrics, cost attribution, dashboards, alerts, and investigation workflows. Use when instrumenting an agent, debugging intermittent tool or model failures, defining service-level objectives, analyzing latency or spend, auditing agent decisions, or preparing production monitoring.
Instalare
npx skills add seb1n/awesome-ai-agent-skills --skill agent-observability -a claude-code
Se instalează în .claude/skills/agent-observability/ · Fiecare comandă descarcă skill-ul direct de pe GitHub cu utilitarul open source skills.
Identic octet cu octet cu commit-ul scanat, cu licența depozitului și un fișier SOURCE.txt.
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Verificări de siguranță pe SKILL.md
- Include scripturi pe care nu le-am verificatScăzut
De ce 76
aiqbench calculează scorul de încredere din semnale publice. Nu este un audit de securitate, așa că citește un skill înainte să-l instalezi.
Skill-uri similare
Cele mai apropiate de acesta prin ce fac și când sunt folosite.
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- llm-observabilitynahid-sparktales/agent-dispatcherSee what an agent actually did — one trace per run with nested model, tool and retrieval spans, token and latency accounted per step, and failures clustered by mechanism instead of read one at a time. Use when an agent misbehaves in ways you cannot reproduce, when cost or latency is unexplained, when "it sometimes fails" is the whole bug report, or before writing evals when you do not yet know which failures exist. Not for judging whether an output is correct (agent-evals), and not a replacement for the application's own monitoring.DevOps★ 58
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