菜单
Systematically test headlines, content formats, CTAs, channels, and posting timing for Bouts marketing with a one-variable-at-a-time discipline, minimum sample sizes, winner criteria, and immediate application of results. Use when running any Bouts content optimization test.
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
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.
基于 SOC 职业分类
| name | ab-testing-framework |
| description | Systematically test headlines, content formats, CTAs, channels, and posting timing for Bouts marketing with a one-variable-at-a-time discipline, minimum sample sizes, winner criteria, and immediate application of results. Use when running any Bouts content optimization test. |
| Test | Variable | Variant A | Variant B | Winner | Lift | Applied |
|---|
