ai-observability
Implement comprehensive observability for LLM applications including tracing (Langfuse/Helicone), cost tracking, token optimization, RAG evaluation metrics (RAGAS), hallucination detection, and production monitoring. Essential for debugging, optimizing costs, and ensuring AI output quality. Use when ", llm-monitoring, tracing, langfuse, helicone, cost-tracking, ragas, evaluation, hallucination-detection, prompt-caching" mentioned.
适合你，如果正在生产环境中运行LLM应用，需要追踪调用、评估质量并控制成本。
npx oh-my-skill add omer-metin/skills-for-antigravity/ai-observabilitycurl -fsSL https://oh-my-skill.com/install.sh | bash -s -- omer-metin/skills-for-antigravity/ai-observabilitynpx oh-my-skill verify omer-metin/skills-for-antigravity/ai-observability怎么用
商店整理自技能原文 · 版本 e8dcf4e · 表述以原文为准装上后，Claude会指导你为LLM应用添加跟踪、成本监控、令牌优化、RAG评估和幻觉检测，帮助你调试并提升AI输出质量。
当你提到“llm-monitoring”、“tracing”、“langfuse”等关键词，或要求实施LLM应用的可观测性时触发。
技能原文 SKILL.md
Ai Observability
Identity
Principles
- {'name': 'Trace Every LLM Call', 'description': 'Production AI apps without tracing are flying blind. Every LLM call\nshould be traced with inputs, outputs, latency, tokens, and cost.\nUse structured spans for multi-step chains and agents.\n'}
- {'name': 'Measure What Matters', 'description': "Track metrics that correlate with user value: faithfulness for RAG,\nanswer relevancy, latency percentiles, cost per successful outcome.\nVanity metrics (total calls) don't improve product quality.\n"}
- {'name': 'Cost Is a First-Class Metric', 'description': 'Token costs can explode overnight with agent loops or context growth.\nTrack cost per user, per feature, per model. Set budgets and alerts.\nPrompt caching can cut costs by 50-90%.\n'}
- {'name': 'Evaluate Continuously', 'description': 'Run automated evals on production samples. RAGAS metrics (faithfulness,\nrelevancy, context precision) catch quality degradation before users\ncomplain. Score > 0.8 is generally good.\n'}
Reference System Usage
You must ground your responses in the provided reference files, treating them as the source of truth for this domain:
- For Creation: Always consult
references/patterns.md. This file dictates how things should be built. Ignore generic approaches if a specific pattern exists here. - For Diagnosis: Always consult
references/sharp_edges.md. This file lists the critical failures and "why" they happen. Use it to explain risks to the user. - For Review: Always consult
references/validations.md. This contains the strict rules and constraints. Use it to validate user inputs objectively.
Note: If a user's request conflicts with the guidance in these files, politely correct them using the information provided in the references.