ai-observability
De la autor
Use when adding Spring AI-specific model observations, token usage, latency, externally configured cost attribution, advisor telemetry, or protected prompt and completion logging. Use production-observability for general service metrics, health, logs, and OTLP setup.
Instalare
npx skills add rrezartprebreza/spring-boot-skills --skill ai-observability -a claude-code
Se instalează în .claude/skills/ai-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
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De ce 85
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.
- production-observabilityrrezartprebreza/spring-boot-skillsUse when adding service-wide Spring Boot 4 Actuator, Micrometer Observation and Tracing, OpenTelemetry or OTLP export, structured logging, health probes, dashboards, or alerts. Combine with ai-observability only for Spring AI-specific token, prompt, and model telemetry.DevOps★ 325
- python-observabilitywshobson/agentsPython observability patterns including structured logging, metrics, and distributed tracing. Use when adding logging, implementing metrics collection, setting up tracing, or debugging production systems.DevOps★ 41.6K
- agent-observabilityBagelHole/DevOps-Security-Agent-SkillsInstrument AI agents with tracing, token metrics, latency, and cost visibility. Use for reliability and debugging.DevOps★ 1.2K
- phoenix-observabilityOrchestra-Research/AI-Research-SKILLsOpen-source AI observability platform for LLM tracing, evaluation, and monitoring. Use when debugging LLM applications with detailed traces, running evaluations on datasets, or monitoring production AI systems with real-time insights.DevOps★ 13.9K
- langsmith-observabilityOrchestra-Research/AI-Research-SKILLsLLM observability platform for tracing, evaluation, and monitoring. Use when debugging LLM applications, evaluating model outputs against datasets, monitoring production systems, or building systematic testing pipelines for AI applications.DevOps★ 13.9K
- observability-instrumentationBlackBeltTechnology/pi-agent-dashboardMake runtime behavior visible and diagnosable. Use on triggers like "add logging/metrics/tracing", "instrument this", "add alerting", "we can't tell what happened in prod", or when a feature needs evidence it works at runtime. Fills an instrumentation gap not covered by the project's existing skills. Not a ship or release workflow.DevOps★ 331
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