llm_evaluation
Implement comprehensive evaluation strategies for LLM applications using automated metrics, human feedback, and benchmarking. Use when testing LLM performance, measuring AI application quality, or establishing evaluation frameworks.
skill_evaluator
Evaluates agent skills against Anthropic's best practices. Use when asked to review, evaluate, assess, or audit a skill for quality. Analyzes SKILL.md structure, naming conventions, description quality, content organization, and identifies anti-patterns. Produces actionable improvement recommendations.
cto-advisor
Advises engineering leadership on direction — architecture decisions recorded as ADRs, technology and vendor evaluation, team scaling ratios, and DORA/engineering-metric targets. Reasons from org-level indicators and frameworks, never from a repository scan. Use when the user asks which technology to adopt, how to structure or scale the engineering team, whether to write an ADR, what DORA targets to hold, or mentions CTO, technical leadership, technology strategy, vendor selection, or engineering metrics. To inventory and rank the debt already sitting in a codebase, use `tech-debt`.
monitoring-observability
Set up monitoring, logging, and observability for applications and infrastructure. Use when implementing health checks, metrics collection, log aggregation, or alerting systems. Handles Prometheus, Grafana, ELK Stack, Datadog, and monitoring best practices.
product-analytics
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