Aa Benchmarking Framework

核心功能含可配置权重的复合评分、帕累托前沿检测、雷达图可视化、统计显著性检验，支持对接LangFuse导入评估数据，可导出CSV/JSON。适用于3+LLM供应商对比、搭建评估仪表盘、向 stakeholder展示选型依据、筛选成本最优模型，帮助团队摆脱单一指标局限，实现科学选型。

nissan

热度 53.04

最近更新 2026-05-11 17:37:31

分类：数据依赖：代码执行 / 第三方 API / 读写文件应用场景：数据处理 / 报告撰写 / 研究

安装技能

复制下面的话术发给你的 Agent，它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill： npx secure-skills-cli add aa-benchmarking-framework

Metadata

name: aa-benchmarking-framework
version: 0.1.0
status: draft
description: Composite scoring and efficiency frontier analysis for LLM evaluation — combines multiple quality dimensions (accuracy, latency, cost, consistency) into a single Pareto-optimal ranking. Use when comparing models or agent configurations across competing objectives, building evaluation dashboards, or identifying the efficiency frontier for model selection. Implements weighted composite scores, Pareto frontier detection, and radar chart visualisation for multi-dimensional LLM benchmarking.
requires: {"env":[],"bins":["python3"]}
metadata: {"openclaw":{"primaryEnv":"production","network":{"outbound":false},"security_notes":"Draft skill — not yet published"}}

Last used: 2026-03-24 Memory references: 1 Status: Active

AA Benchmarking Framework

STATUS: DRAFT — This skill is planned but not yet fully implemented.

What This Does

Provides a systematic framework for multi-dimensional LLM evaluation using composite scoring, efficiency frontier analysis, and Pareto optimality. Rather than ranking models on a single metric, it helps identify which models are non-dominated — i.e., no other model is better on all dimensions simultaneously. Designed for teams that need principled model selection beyond simple leaderboard rankings.

Planned Capabilities

Composite scoring with configurable dimension weights (accuracy, latency, cost, recall, F1)
Pareto frontier detection across any two or more evaluation dimensions
Radar/spider chart visualisation for multi-dimensional comparison
Statistical significance testing across benchmark runs (t-test, Mann-Whitney U)
Integration with LangFuse for trace-based evaluation data ingestion
Export to CSV/JSON for downstream analysis

When To Use

Choosing between 3+ LLM providers on competing objectives (e.g. GPT-4o vs Claude 3.5 vs Gemini)
Building an evaluation dashboard for recurring model benchmarks
Presenting model selection rationale to stakeholders with visual evidence
Running efficiency frontier analysis to identify cost-optimal models for a quality threshold