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agent-architecture-evaluatorAgent 架构评估器

Agent Skill

agent-architecture-evaluator 用于辅助测试设计、自动化测试和回归验证,适合在 OpenClaw 中需要补充测试、分析失败日志或验证功能改动时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

7,956

周安装

325

GitHub Stars

公开资料未说明

下载量

2,548
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:agent-architecture-evaluator(Agent 架构评估器)
来源仓库:https://github.com/ada01325150-alt/agent-architecture-evaluator
安装命令:
openclaw skills install agent-architecture-evaluator
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install agent-architecture-evaluator

简介

协助评估代理架构可靠性,覆盖规划、布线与工具链完整性。

  • 适合在系统集成阶段发现潜在瓶颈与单点故障风险。
  • 支持内存防中毒检测与操作规则合规性校验。agent-architecture-evaluator 属于效率类 Skill,可作为该场景下的辅助能力补充。
  • 扫描结果可能存在误报,需结合人工判断排除干扰项。
  • 建议定期执行回归测试以确保架构演进不影响原有稳定性。

SKILL.md

name
agent-architecture-evaluator
description
Use when evaluating, testing, and optimizing an agent architecture or multi-agent system. Best for reviewing planning, routing, memory, tool use, reliability, observability, cost, and system-level failure modes.
version
1.0.0

Agent Architecture Evaluator

Version: 1.0.0

Overview

This skill reviews the architecture of an agent system, not just its prompts or its attached skills.

Use it for architectures involving components such as:

  • planner / executor splits
  • routers and specialists
  • tool-use layers
  • memory systems
  • human approval gates
  • multi-agent coordination

Use this skill when

  • A user wants to assess an existing agent architecture.
  • Reliability, latency, cost, or coordination problems appear to be architectural.
  • A team needs a structured architecture review and optimization roadmap.
  • You need system-level test scenarios rather than single-skill evals.

Do not use this skill when

  • The problem is one isolated skill.
  • The task is to create a new skill from scratch.
  • The main need is portfolio review across many related skills.

Use agent-test-measure-refine or agent-skill-portfolio-evaluator in those cases.

Output contract

Always produce these named outputs:

  • architecture_inventory
  • failure_mode_map
  • architecture_test_plan
  • optimization_roadmap
  • measurement_plan
  • architecture_recommendation

Review dimensions

Evaluate at least these dimensions:

  1. component clarity
  2. routing correctness
  3. memory usefulness
  4. coordination reliability
  5. cost and latency efficiency
  6. observability and debuggability

Quick start

  1. Map the current architecture.
  2. Identify critical paths and failure-prone handoffs.
  3. Define architecture-level test scenarios.
  4. Identify bottlenecks in routing, memory, tools, or coordination.
  5. Recommend the smallest structural changes with the highest leverage.

Workflow

1. Build the architecture inventory

Capture:

  • components
  • responsibilities
  • inputs and outputs
  • state or memory boundaries
  • human approval points
  • observability signals

2. Map failure modes

Look for:

  • planner produces unusable tasks
  • router sends work to the wrong specialist
  • memory pollutes current decisions
  • tool calls are slow, redundant, or poorly validated
  • multi-agent handoffs lose context
  • approval gates appear too late

3. Design system tests

Cover:

  • happy path
  • degraded upstream input
  • partial component failure
  • tool unavailability
  • stale or noisy memory
  • high-latency coordination
  • rollback or recovery behavior

See references/architecture-review-framework-v1.0.0.md.

4. Prioritize architectural changes

Prefer:

  • clarifying responsibilities before adding components
  • removing weak indirection
  • tightening interface contracts
  • adding observability before adding complexity
  • isolating state when cross-contamination is likely

5. Define measurement

Recommend concrete metrics where available:

  • task success rate
  • retry rate
  • fallback rate
  • cost per successful task
  • latency by stage
  • human intervention rate

Anti-patterns

  • adding new components to hide unclear ownership
  • keeping weak memory because it sounds sophisticated
  • optimizing one stage without measuring system impact
  • blaming prompts for structural routing failures

Resources

  • references/architecture-review-framework-v1.0.0.md for system review steps.
  • references/optimization-patterns-v1.0.0.md for architecture optimization guidance.
  • assets/architecture-review-template.md for the final report structure.
  • assets/example-architecture-review.md for a realistic filled review.
  • assets/architecture-input-example.json for structured input.
  • scripts/render_architecture_review.py to normalize a structured architecture review into Markdown.

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

OpenClaw

80.25%
按下载量换算2,045

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

通过

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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