Agent Skills › afx-team/hebb-mind › bench-compare

bench-compare

GitHub

对比多个Agent记忆系统，分析代码、基准测试及架构。通过评估内存类型、检索策略、可扩展性、API设计等维度，生成Markdown对比矩阵并提供包含权衡建议的推荐总结。

.agents/skills/bench-compare/SKILL.md afx-team/hebb-mind

Trigger Scenarios

需要比较不同Agent记忆系统的性能或架构评估记忆存储方案的可扩展性与集成难度寻找特定功能（如向量/图检索）的记忆实现

Install

npx skills add afx-team/hebb-mind --skill bench-compare -g -y

More Options

Non-standard path

npx skills add https://github.com/afx-team/hebb-mind/tree/main/.agents/skills/bench-compare -g -y

Use without installing

npx skills use afx-team/hebb-mind@bench-compare

指定 Agent (Claude Code)

npx skills add afx-team/hebb-mind --skill bench-compare -a claude-code -g -y

安装 repo 全部 skill

npx skills add afx-team/hebb-mind --all -g -y

预览 repo 内 skill

npx skills add afx-team/hebb-mind --list

SKILL.md

Frontmatter

{
    "name": "bench-compare",
    "arguments": [
        {
            "name": "projects",
            "required": true,
            "description": "Comma-separated list of projects to compare (e.g. mem0,zep,letta)"
        },
        {
            "name": "criteria",
            "required": false,
            "description": "Comparison criteria (e.g. performance,api-design,scalability)"
        }
    ],
    "description": "Compare multiple agent memory systems by analyzing their code, benchmarks, and architecture",
    "user_invocable": true
}

Benchmark & Compare

Compare multiple agent memory projects across key dimensions.

Instructions

For each project in ${projects}:
- Search for benchmarks, performance reports, and comparisons
- Analyze the architecture and API design
- Check community feedback (GitHub issues, discussions, Reddit, HN)
Compare across these default criteria (override with ${criteria} if provided):
- Memory Types: What types of memory are supported
- Retrieval Strategy: How memories are retrieved (vector, graph, hybrid)
- Scalability: How it handles large memory stores
- API Design: Ease of integration
- Ecosystem: Integrations with LLM frameworks
- Performance: Latency, throughput if data available
- Community: Stars, contributors, activity
Output a comparison matrix (markdown table)
Provide a recommendation summary with trade-offs