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

bench-compare

GitHub

用于对比多个Agent记忆系统。通过分析代码、基准测试及架构，评估内存类型、检索策略、可扩展性等维度，输出比较矩阵与建议。

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

Trigger Scenarios

需要对比不同Agent记忆系统的优劣选择适合项目的Agent记忆框架分析Agent记忆系统的性能与架构差异

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/.claude/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