Loading...
Loading...
Found 102 Skills
INVOKE THIS SKILL when auditing an AI agent or LLM app for regulatory compliance. Covers EU AI Act, GPAI Code of Practice, GDPR, NIST AI RMF, Colorado AI Act, HIPAA, and ISO 42001. Scans the codebase for compliance gaps, cross-references Arize instrumentation for audit trail coverage, and produces an actionable remediation checklist tailored to the selected frameworks.
This skill should be used when adding error tracking and performance monitoring with Sentry and OpenTelemetry tracing to Next.js applications. Apply when setting up error monitoring, configuring tracing for Server Actions and routes, implementing logging wrappers, adding performance instrumentation, or establishing observability for debugging production issues.
INVOKE THIS SKILL when adding Arize AX tracing to an application. Follow the Agent-Assisted Tracing two-phase flow: analyze the codebase (read-only), then implement instrumentation after user confirmation. When the app uses LLM tool/function calling, add manual CHAIN + TOOL spans so traces show each tool's input and output. Leverages https://arize.com/docs/ax/alyx/tracing-assistant and https://arize.com/docs/PROMPT.md.
Create a Product-Led Sales Motion Pack (PQL/PQA definition, usage-signal spec + routing/SLA, sales outreach playbook, instrumentation plan, and pilot/scale plan). Use for product-led sales, sales-assist, PLG-to-sales handoffs, and converting self-serve usage into sales opportunities. Category: Sales & GTM.
See exactly what your AI did on a specific request. Use when you need to debug a wrong answer, trace a specific AI request, profile slow AI pipelines, find which step failed, inspect LM calls, view token usage per request, build audit trails, or understand why a customer got a bad response. Covers DSPy inspection, per-step tracing, OpenTelemetry instrumentation, and trace viewer setup.
Use when an existing agent needs Prefactor resources created via the Prefactor CLI before SDK instrumentation is added.
Use this skill when working with Sentry - error monitoring, performance tracing, session replay, cron monitoring, alerts, or source maps. Triggers on any Sentry-related task including SDK initialization, issue triage, custom instrumentation, uploading source maps, configuring alerts, and integrating Sentry into JavaScript, Python, Next.js, or other supported frameworks.
Build FastAPI services with JWT auth, structlog, and Prometheus metrics. Use when creating or modifying a Python HTTP server, adding authentication, structured logging, or instrumentation to a FastAPI app.
OpenTelemetry with Grafana stack. Covers OTel SDK instrumentation for Go/Java/Python/Node.js/.NET, OTLP protocol and endpoint configuration, sending telemetry to Grafana Cloud via OTLP endpoint, Grafana Alloy as OTel collector, sampling strategies, Kubernetes OTel Operator, and migration from other observability tools. Use when instrumenting apps with OTel, configuring OTLP endpoints, setting up collectors, or migrating to OpenTelemetry.
Instrument LLM applications with Langfuse tracing. Use when setting up Langfuse, adding observability to LLM calls, or auditing existing instrumentation.
Monitoring and observability strategy, implementation, and troubleshooting. Use for designing metrics/logs/traces systems, setting up Prometheus/Grafana/Loki, creating alerts and dashboards, calculating SLOs and error budgets, analyzing performance issues, and comparing monitoring tools (Datadog, ELK, CloudWatch). Covers the Four Golden Signals, RED/USE methods, OpenTelemetry instrumentation, log aggregation patterns, and distributed tracing.
Set up Kafka-based event-driven microservices with Platformatic Watt. Use when users ask about: - "kafka", "event-driven", "messaging" - "kafka hooks", "kafka webhooks" - "kafka producer", "kafka consumer" - "dead letter queue", "DLQ" - "request response pattern" with Kafka - "migrate from kafkajs", "kafkajs migration", "replace kafkajs" Covers @platformatic/kafka, @platformatic/kafka-hooks, consumer lag monitoring, and OpenTelemetry instrumentation.