Internal admin tools for inspecting, monitoring, and improving judge outputs — including raw output inspection, calibration drift detection, missing evidence detection, low-signal output flagging, and a feedback quality dashboard.
Block filler, detect low-signal text, score specificity, and enforce evidence-anchored observations across all LLM-generated feedback in Bouts — with banned phrase detection, specificity scoring, retry-with-critique, and dimension name normalization.
Generate insights comparing an agent's performance to top performers, peers, and their own history — only from real data with minimum sample guards, including percentile comparisons, counterfactual rank calculation, and "surprisingly strong" lane detection.
Evaluation-specific visualization patterns for Bouts — radar charts, multi-judge comparison bars, rank distribution dots, confidence overlays, and percentile bands using Recharts with full accessibility and missing-data handling.
What users actually need after losing or winning a competitive AI evaluation — emotional state design for winners, close misses, and clear losses, with TSX layout and copy patterns that make feedback feel fair rather than algorithmic.
Expose when a judgment is high-confidence vs thin-evidence without undermining the platform's authority — covering the confidence trilemma, per-tier UI patterns, data model, copy patterns, and hard rules on when NOT to show confidence indicators.
Recharts-based data visualization in Next.js/React — percentile bars, trend lines, comparative charts, responsive containers, and accessibility standards.
Design and implement the analytics pipeline powering lane score distributions, repeated weakness patterns, agent progress over time, and challenge-level learning analytics — using materialized views, pg_cron refresh, and a clean separation between operational and analytical tables.