llm-observability
De la autor
See 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.
Instalare
npx skills add nahid-sparktales/agent-dispatcher --skill llm-observability -a claude-code
Se instalează în .claude/skills/llm-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.
Vrei ca agentul să-ți propună skill-uri în timp ce scrii? Vezi cum folosești skill-urile din IDE.
Verificări de siguranță pe SKILL.md
Niciun avertisment.
De ce 82
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.
- agent-observabilityBagelHole/DevOps-Security-Agent-SkillsInstrument AI agents with tracing, token metrics, latency, and cost visibility. Use for reliability and debugging.DevOps★ 1.2K
- observabilityDevelopersGlobal/ai-agent-skillsStructured logging, distributed tracing, and alerting for AI systems and traditional services. You can't fix what you can't see.DevOps★ 213
- observability-llm-obselastic/agent-skillsAnswer questions about LLM and agentic-application behavior from data already ingested into Elastic: latency and error rate, token and cost utilization, response quality and guardrail events, and agentic call-chain orchestration. Use when the user asks about LLM monitoring, GenAI observability, token spend or AI cost, model latency, prompt or guardrail failures, or how an agent's tool-call chain executed.OficialDevOps★ 600
- agent-evals-and-observabilitymagnus919/agent-skillsDesign, run, review, or release framework- and vendor-neutral evaluations and observability for AI agents. Use when defining agent evals, datasets, graders, trajectory review, regression analysis, release gates, production traces, or privacy-aware telemetry. Covers task and trajectory contracts, statistical comparisons, and incident-to-case learning; route framework implementation to pydanticai or langgraph when needed. Do not use this skill for unrelated requests; route to the nearest named specialist.DevOps★ 147
- trace-recontonone-ai/tononeAudit LLM observability coverage — trace gaps, logging completeness, cost attribution accuracy. Use when asked to "audit our LLM observability", "find trace coverage gaps", or "check cost attribution accuracy".DevOps★ 76
- langsmith-observabilityKalarisLabs/research-agent-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★ 61
Note și recenzii
—
nicio recenzie
Ai încercat acest skill? Notează-l ca să-i ajuți pe alții. Autentifică-te
Discuție
Intră în discuție. Autentifică-te