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Agent Skill · 研究检索

diagnose(diagnose 搜索)

diagnose 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。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。

diagnose 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为调用外部服务;许可证信息还不明确,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 31,941。

总安装 2,614周安装 110GitHub Stars 189下载 915核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

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:diagnose(diagnose 搜索)
来源仓库:https://github.com/sharpdeveye/maestro
仓库路径:skills/diagnose
安装命令:
npx skills add https://github.com/sharpdeveye/maestro --skill diagnose
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 npx skills 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。

skills.shnpx skills
npx skills add https://github.com/sharpdeveye/maestro --skill diagnose

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

diagnose 用于查找、检索和筛选相关信息。适合在 Codex、Claude、Cursor、Gemini CLI 中根据关键词、任务场景或来源线索快速定位候选结果时使用。通过 npx skills add 命令从指定 GitHub 仓库安装,需结合原始 README 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操作。diagnose 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

来源仓库内路径:skills/diagnose

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/sharpdeveye/maestro --skill diagnose;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要调用外部服务,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

diagnose 怎么安装?

在终端执行 npx skills add https://github.com/sharpdeveye/maestro --skill diagnose。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

diagnose 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

diagnose 需要什么权限?

权限级别是调用外部服务。该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

diagnose 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

diagnose 用的人多吗?

公开数据是累计安装 2,614 次,近一周安装 110 次,下载 915 次,GitHub 189 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

diagnose 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
diagnose

diagnose 搜索 · 本页

2,614189调用外部服务未明确通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

MANDATORY PREPARATION

Invoke /agent-workflow — it contains workflow principles, anti-patterns, and the Context Gathering Protocol. Follow the protocol before proceeding — if no workflow context exists yet, you MUST run /teach-maestro first.


Perform a systematic diagnostic scan across 5 dimensions. For each dimension, score 1-5 and provide specific findings.

Dimension 1: Prompt Quality (1-5)

Evaluate:

  • Structure (4-zone pattern: role, context, instructions, output)
  • Output schema definition (explicit vs. implicit)
  • Instruction clarity (specific vs. vague)
  • Edge case handling (addressed vs. ignored)
  • Anti-patterns present (wall of text, contradictions, implicit format)

Dimension 2: Context Efficiency (1-5)

Evaluate:

  • Context budget allocation (planned vs. ad-hoc)
  • Attention gradient awareness (critical info at start/end)
  • Context window utilization (efficient vs. wasteful)
  • State management (explicit vs. implicit)
  • Memory strategy (appropriate for conversation length)

Dimension 3: Tool Health (1-5)

Evaluate:

  • Tool count (3-7 ideal, 13+ problematic)
  • Description quality (specific vs. vague)
  • Error handling (graceful vs. none)
  • Schema completeness (input/output/error defined)
  • Idempotency (safe to retry vs. side-effect prone)
  • Scope attribution: Distinguish between project-configured tools (e.g., custom scripts, project MCP servers) and agent-level tools (e.g., built-in IDE tools, global MCP servers). Only flag tool overhead for tools the project can actually control

Dimension 4: Architecture Fitness (1-5)

Evaluate:

  • Topology appropriateness (single vs. multi-agent justified)
  • Agent boundaries (clear vs. overlapping)
  • Handoff protocols (structured vs. ad-hoc)
  • Observability (decisions logged vs. black box)
  • Cost awareness (budgeted vs. unbounded)

Dimension 5: Safety & Reliability (1-5)

Evaluate:

  • Input validation (present vs. absent)
  • Output filtering (PII, content policy) — scope contextually: data flowing between a user's own frontend and backend (e.g., authenticated sessions, internal APIs) is lower risk than data exposed to external services or third-party APIs
  • Cost controls (ceilings set vs. unbounded)
  • Error recovery (fallbacks vs. crash)
  • Evaluation strategy (golden tests vs. "it seems to work")

Diagnostic Report Format

╔══════════════════════════════════════╗
║          MAESTRO DIAGNOSTIC         ║
╠══════════════════════════════════════╣
║ Prompt Quality      ████░  4/5      ║
║ Context Efficiency   ███░░  3/5      ║
║ Tool Health          ██░░░  2/5      ║
║ Architecture         ████░  4/5      ║
║ Safety & Reliability ██░░░  2/5      ║
╠══════════════════════════════════════╣
║ Overall Score:       15/25           ║
╚══════════════════════════════════════╝

CRITICAL FINDINGS:
1. [Most severe issue — immediate action needed]
2. [Second most severe]
3. [Third]

RECOMMENDED ACTIONS:
1. Run /fortify to add error handling (addresses Tool Health + Safety)
2. Run /streamline to reduce tool count (addresses Tool Health)
3. Run /refine for prompt structure improvements (addresses Prompt Quality)

Maestro Command Mapping

Every recommended action MUST reference the specific Maestro command that addresses it. Use this mapping:

Dimension GapMaestro CommandWhen to Recommend
Prompt structure, clarity, output schema/refineScore ≤ 4 on Prompt Quality
Context budget, attention gradient, memory/streamlineScore ≤ 3 on Context Efficiency
Tool errors, missing tools, redundant tools/fortifyScore ≤ 3 on Tool Health
Tool count reduction, unused tools/streamlineTool count > 7 or unused tools found
Safety gaps, error recovery, validation/fortifyScore ≤ 3 on Safety & Reliability
Test coverage, golden tests, evaluation/guardNo automated tests or evaluation strategy
Architecture boundaries, observability/calibrateScore ≤ 3 on Architecture Fitness

Do NOT give generic manual actions (e.g., "Add Vitest", "Create a rollback script") without also specifying which Maestro command the user should run to implement it. The recommended action format is:

Run /<command> to [specific action] (addresses [Dimension] #[gap number])

Scoring Guide

ScoreMeaningMaestro Action
5Production-excellentNo action needed
4Good with minor gaps/refine for polish
3Functional but risky/fortify or /streamline for targeted fix
2Significant issues/fortify + /guard — immediate attention
1Broken or missing/onboard-agent — rebuild required

Diagnostic Checklist

  • All 5 dimensions scored with specific evidence
  • Critical findings listed in priority order
  • Each finding includes specific file/component location
  • Recommended actions reference specific Maestro commands (see Command Mapping above)
  • Overall score calculated and report generated

Recommended Next Step

After diagnosis, run the command mapped to your lowest-scoring dimension. For a general improvement sequence: /fortify → /streamline → /refine.

NEVER:

  • Give all 5s unless the workflow is genuinely production-excellent
  • Skip dimensions — score all 5 even if some seem fine
  • Diagnose without reading the actual workflow code/config
  • Recommend changes without specific findings to support them
  • Give generic manual actions without mapping them to a Maestro command

平台分布

Codex

34.79%
按下载量换算318

Claude

29.68%
按下载量换算272

Cursor

20.89%
按下载量换算191

Gemini CLI

10.4%
按下载量换算95

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

调用外部服务

该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

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。目前只有一个公开来源,建议同时核对源仓库。

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