Scope what quality should be measured, convert it into one or more actionable binary evaluations, deploy those evaluations through Truesight MCP, and generate a companion skill that applies them correctly. Use when a user wants to create new evals, quality checks, guardrails, or pass/fail criteria for AI outputs.
Generate synthetic test data for LLM evaluations using dimension-based tuple expansion. Use when the user needs synthetic traces, test cases, eval datasets, or when create-evaluation needs synthetic fallback data.
Orchestrator for Truesight MCP skills. Use this when the user needs help choosing the right Truesight workflow or when intent is ambiguous across LLM evaluate, error analysis, review, templates, or evaluation creation.
Fastest route to a deployed live evaluation using a pre-built Truesight template. Use when the user wants a quick start without building judgment configs from scratch.
Build a custom web interface for trace annotation and review. Use when users need a bespoke review surface for their workflow.
Systematically identify and categorize failure modes in evaluated traces using Truesight datasets and error-analysis tools. Use when quality issues are unclear, after major pipeline changes, or when incidents indicate drift.
Audit an existing evaluation workflow and produce severity-ranked findings with concrete next actions. Use when inheriting an eval setup, diagnosing quality regressions, or checking LLM evaluation process maturity.
Evaluate one or more traces against an existing Truesight live evaluation. Use when a deployed live evaluation already exists and the user wants run outputs with optional handoff to review and promotion.