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agent-architecture-evaluator

作者 ada01325150-alt · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ✓ 安全检测通过
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在 OpenClaw 中安装
/install agent-architecture-evaluator
功能描述
Use when evaluating, testing, and optimizing an agent architecture or multi-agent system. Best for reviewing planning, routing, memory, tool use, reliability...
使用说明 (SKILL.md)

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.
安全使用建议
This skill appears coherent and low-risk: it ships templates, documentation, and a small Python script that renders a JSON architecture review to Markdown. Before using, review how you supply input to the script: it reads a file path you provide, so avoid pointing it at local files that contain credentials or other sensitive data. If you intend to allow autonomous invocation or run the script in an automated environment, run it in a sandbox or inspect the script first (it's short and included). Otherwise, no additional credentials, network endpoints, or installs are required.
功能分析
Type: OpenClaw Skill Name: agent-architecture-evaluator Version: 1.0.0 The skill bundle provides a structured framework for evaluating and optimizing agent architectures. The included Python script (scripts/render_architecture_review.py) is a simple utility for rendering JSON data into Markdown reports and contains no dangerous execution patterns. The instructions in SKILL.md and the supporting documentation are consistent with the stated purpose and do not exhibit signs of prompt injection, data exfiltration, or malicious intent.
能力评估
Purpose & Capability
Name and description match the included assets (templates, references, example input) and a small helper script. There are no unrelated env vars, binaries, or config paths requested.
Instruction Scope
SKILL.md stays focused on mapping architectures, failure modes, tests, and measurements. It does not instruct reading arbitrary system secrets, contacting external endpoints, or performing actions outside the stated scope.
Install Mechanism
No install spec is provided (instruction-only). The only executable is a small local Python renderer; there are no downloads, external package installs, or extracted archives.
Credentials
The skill requires no environment variables, credentials, or config paths. Nothing requests broad secrets or unrelated service tokens.
Persistence & Privilege
always:false and no persistent install behavior. agents/openai.yaml contains allow_implicit_invocation:false which further limits implicit invocation on that interface. The skill does not modify other skills or system-wide settings.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install agent-architecture-evaluator
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /agent-architecture-evaluator 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v1.0.0
- Initial release of the Agent Architecture Evaluator skill. - Enables systematic evaluation, testing, and optimization of agent or multi-agent system architectures. - Reviews key dimensions: planner/executor splits, routers, memory, tool use, reliability, cost, observability, and failure modes. - Provides standardized outputs: architecture inventory, failure mode map, test plan, optimization roadmap, measurement plan, and final recommendations. - Includes best practices, anti-patterns, and references for structured architecture review and optimization.
元数据
Slug agent-architecture-evaluator
版本 1.0.0
许可证 MIT-0
累计安装 1
当前安装数 1
历史版本数 1
常见问题

agent-architecture-evaluator 是什么?

Use when evaluating, testing, and optimizing an agent architecture or multi-agent system. Best for reviewing planning, routing, memory, tool use, reliability... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 259 次。

如何安装 agent-architecture-evaluator?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install agent-architecture-evaluator」即可一键安装,无需额外配置。

agent-architecture-evaluator 是免费的吗?

是的,agent-architecture-evaluator 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。

agent-architecture-evaluator 支持哪些平台?

agent-architecture-evaluator 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。

谁开发了 agent-architecture-evaluator?

由 ada01325150-alt(@ada01325150-alt)开发并维护,当前版本 v1.0.0。

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