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Everything your AI needs, in one place.
Ready-made agents, skills, personas, prompts, templates and tools. Each one is checked before it goes live, works with any model, and installs in a click. Rate what you use so the best rises to the top.
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A service that does a whole job for you - research, coding, support - and reports back.
Step-by-step instructions an AI follows for one kind of task. Install once, reuse everywhere.
A voice and set of rules layered onto any model: tone, audience, do's and don'ts.
A ready-to-use prompt with fill-in-the-blank variables and notes on when it works best.
A complete routing setup - models, rules and settings - in one file you can apply in a minute.
A single function an AI can call: a calculator, a search, a database lookup.
A language model endpoint with its price, speed and quality declared so the router can compare it.
twinmind-observability
Monitor TwinMind transcription quality, meeting coverage, action item extraction rates, and memory vault health. Use when implementing observability, or managing TwinMind meeting AI operations. Trigge
vastai-observability
Monitor Vast.ai GPU instance health, utilization, and costs. Use when setting up monitoring dashboards, configuring alerts, or tracking GPU utilization and spending. Trigger with phrases like "vastai
vercel-cost-tuning
Optimize Vercel costs through plan selection, function efficiency, and usage monitoring. Use when analyzing Vercel billing, reducing function execution costs, or implementing spend management and budg
vercel-observability
Set up Vercel observability with runtime logs, analytics, log drains, and OpenTelemetry tracing. Use when implementing monitoring for Vercel deployments, setting up log drains, or configuring alerting
database-migrations-migration-observability
Migration monitoring, CDC, and observability infrastructure
langsmith-observability
LLM observability platform for tracing, evaluation, and monitoring. Use when debugging LLM applications, evaluating model outputs against datasets, monitoring production systems, or building systemati
phoenix-observability
Open-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 A
railway-metrics
Query resource usage metrics for Railway services. Use when user asks about resource usage, CPU, memory, network, disk, or service performance like "how much memory is my service using" or "is my serv
mlops-workflows
Comprehensive MLOps workflows for the complete ML lifecycle - experiment tracking, model registry, deployment patterns, monitoring, A/B testing, and production best practices with MLflow
langsmith-observability
LLM observability platform for tracing, evaluation, and monitoring. Use when debugging LLM applications, evaluating model outputs against datasets, monitoring production systems, or building systemati
phoenix-observability
Open-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 A
monitoring-setup
Sets up production monitoring for NestJS and Next.js apps — Sentry error tracking, Google Analytics, and operational signals (BullMQ queue depth, Postgres slow queries, connection saturation) with ale
observability-testing-patterns
Observability and monitoring validation patterns for dashboards, alerting, log aggregation, APM traces, and SLA/SLO verification. Use when testing monitoring infrastructure, dashboard accuracy, alert
shift-right-testing
Testing in production with feature flags, canary deployments, synthetic monitoring, and chaos engineering. Use when implementing production observability or progressive delivery.
qe-observability-testing-patterns
Observability and monitoring validation patterns for dashboards, alerting, log aggregation, APM traces, and SLA/SLO verification. Use when testing monitoring infrastructure, dashboard accuracy, alert
qe-shift-right-testing
Testing in production with feature flags, canary deployments, synthetic monitoring, and chaos engineering. Use when implementing production observability or progressive delivery.
monitoring
监控与告警
system-admin
Linux system administration and monitoring
log-analysis
日志分析与处理
integrate
Add Olakai monitoring to existing AI code — wrap your LLM client, configure custom KPIs, and validate the integration end-to-end
new-project
Build a new AI agent with Olakai monitoring from scratch — project setup, SDK integration, KPI configuration, and end-to-end validation
monitoring-observability
Monitoring and observability patterns for Prometheus metrics, Grafana dashboards, Langfuse v4 LLM tracing (as_type, score_current_span, should_export_span, LangfuseMedia), and drift detection. Use whe
web-research-workflow
Unified decision tree for web research and competitive monitoring. Auto-selects WebFetch, Tavily, or agent-browser based on target site characteristics and available API keys. Includes competitor page
behavioral-loop-detection
Detect when agents are stuck in repetitive action loops using a rolling 20-action window with escalating nudges — replan after 3 similar actions, explore after 5, force-done after 8.
Find
Search or browse by kind. Every card shows who made it, how many people installed it and what they think.
Install
One click. You get a manifest the router understands, plus copy-paste snippets for the CLI, Python and YAML.
Rate and publish
Leave a star rating after you have used it. Made something useful? Publish it - free listings go live immediately.
Prefer the terminal? osr stack apply registry://starter installs the starter template.