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Found 12 Skills
Setup Sentry AI Agent Monitoring in any project. Use when asked to monitor LLM calls, track AI agents, or instrument OpenAI/Anthropic/Vercel AI/LangChain/Google GenAI. Detects installed AI SDKs and configures appropriate integrations.
Know when your AI breaks in production. Use when you need to monitor AI quality, track accuracy over time, detect model degradation, set up alerts for AI failures, log predictions, measure production quality, catch when a model provider changes behavior, build an AI monitoring dashboard, or prove your AI is still working for compliance. Covers DSPy evaluation for ongoing monitoring, prediction logging, drift detection, and alerting.
Monitors AI Gateway traffic, costs, latency, errors, and token usage by querying request traces via the spans query API.
Instrument an existing codebase with LaunchDarkly AI Config tracking. Walks the four-tier ladder (managed runner → provider package → custom extractor + trackMetricsOf → raw manual) and picks the lowest-ceremony option that still captures duration, tokens, and success/error.
Real-time monitoring and detection of adversarial attacks and model drift in production
Migrate an application with hardcoded LLM prompts to a full LaunchDarkly AI Configs implementation in five stages: extract prompts, wrap in the AI SDK, add tools, add tracking, add evals/judges. Use when the user wants to externalize model/prompt configuration, move from direct provider calls (OpenAI, Anthropic, Bedrock, Gemini) to a managed AI Config, or stage a full hardcoded-to-LaunchDarkly migration.
Configure specific Sentry features beyond basic SDK setup. Use when asked to monitor AI/LLM calls, set up OpenTelemetry pipelines, or create alerts and notifications.
Add LangWatch tracing and observability to your code. Use for both onboarding (instrument an entire codebase) and targeted operations (add tracing to a specific function or module). Supports Python and TypeScript with all major frameworks.
LLM 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.
Detect and respond to ideological drift, sycophantic patterns, and alignment threats using the Antidote Protocol.
Comprehensive TUI/Web dashboard for Claude Code monitoring
Monitor LLMs and agentic apps: performance, token/cost, response quality, and workflow orchestration. Use when the user asks about LLM monitoring, GenAI observability, or AI cost/quality.