convex-evals

Design, implement, validate, and calibrate a new eval for the convex-evals suite. Use when the user wants to add a new eval, create an eval, test a new Convex concept, or expand eval coverage.

Add a new AI model to the eval runner, update the manual eval workflow, push changes, and trigger baseline eval runs. Use when the user wants to add a new model, onboard a model, or mentions a new model name/link to add to the leaderboard.

Investigate a single failing eval from the convex-evals system. Use when the user shares a visualizer URL pointing to a specific eval, asks about a specific failing eval, or references a specific eval ID.

Analyze all failures in a convex-evals run, spawning parallel sub-agents to investigate each failure and producing a report with classifications and recommendations. Use when the user asks to analyze an entire run, review all failures in a run, or wants to understand why a model scored poorly.

Empirically verify guideline changes by running before/after eval runs across multiple models and ensuring no regressions. Use when proposing or reviewing changes to runner/models/guidelines.ts, or when the user asks to validate guidelines.

Analyze guideline ablation experiment results to determine which guideline sections are essential, marginal, or dispensable. Use when the user asks to analyze ablation results, interpret guideline compaction data, or wants to know which guidelines to keep for AGENTS.md.