Pick an LLM observability stack (development platform + gateway + optional scale layer) given stack, scale, budget, and license posture, and define the OpenTelemetry GenAI attribute set. Use when you need help with observability stack.
Scanned 9/8/2026
Install to Claude Code
npx -y skills add anubhavg-icpl/vibe --skill observability-stack --agent claude-codeInstalls into .claude/skills of the current project.
Are you the author of Observability Stack?
Add the live security badge to your README — it updates automatically with every re-scan.
[](https://www.skillsdirectory.com/skills/anubhavg-icpl-observability-stack)More formats (shields.io, HTML) on the badges page.
---
name: observability-stack
description: Pick an LLM observability stack (development platform + gateway + optional scale layer) given stack, scale, budget, and license posture, and define the OpenTelemetry GenAI attribute set. Use when you need help with observability stack.
license: CC-BY-NC-SA-4.0
phase: 17
lesson: 13
metadata:
version: 1.0.0
tags: [observability, langfuse, langsmith, phoenix, arize, helicone, opik, opentelemetry, genai-conventions]
---
Given stack (LangChain / DSPy / raw SDK), scale (traces/day), budget, license posture (MIT-only vs commercial OK), and self-host requirement, produce an observability plan.
Produce:
1. Development platform choice. Langfuse (OSS), LangSmith (LangChain-first commercial), Opik (Comet OSS), or none. Justify with stack and license.
2. Gateway/telemetry choice. Helicone (proxy + gateway), SigNoz (full APM), OpenLLMetry (pure OTel). If already using an AI gateway (Phase 17 · 19), name the integration.
3. Scale/lake layer. Optional; Arize AX or raw Iceberg for long-term analytics, Phoenix for RAG drift.
4. OTel GenAI conventions. Specify the minimum attribute set: `gen_ai.system`, `gen_ai.request.model`, `gen_ai.usage.input_tokens`, `gen_ai.usage.output_tokens`, `gen_ai.request.temperature`, `gen_ai.response.finish_reasons`, plus org-specific (tenant_id, user_id, task).
5. Sampling policy. 100% errors, 100% high-cost (>$0.10/call), N% success sampling rate. Raw-retention window (14d / 30d / 90d). Aggregates retained longer.
6. Alerting. Five metrics that must have alerts: error rate, P99 TTFT, cost/request, prompt-cache hit rate, refusal rate.
Hard rejects:
- Instrumenting inside framework-specific SDK without an OTel fallback. Refuse — framework lock-in.
- Keeping 100% of traces at Datadog-class pricing >$500/mo for a non-regulated workload. Refuse — recommend sampling.
- Ignoring OpenTelemetry GenAI conventions. Refuse — 2026 interop requires them.
Refusal rules:
- If traces/day > 5M and the team insists on full Datadog retention, refuse without a cost forecast.
- If the team is MIT-only and picks LangSmith, refuse — Langfuse is the MIT equivalent.
- If the team has no AI gateway and picks Helicone as gateway AND observability, accept — the proxy doubles as gateway up to ~500 RPS (Phase 17 · 19 covers gateway scale).
Output: a one-page plan naming dev platform, gateway, scale layer (if any), OTel attribute set, sampling rule, five alerts. End with the single metric that signals stack drift: percentage of LLM calls with complete OTel GenAI attributes over last 7 days.
Is this your skill, or is something wrong with this listing? Request removal or report an issue. Author removals are honored within 72 hours.
No comments yet. Be the first to comment!