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Agent Skill · 研究检索

recipe-diagnose(食谱诊断)

recipe-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。

recipe-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,权限为只读;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 63,093。

总安装 321周安装 13GitHub Stars 323下载 101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:recipe-diagnose(食谱诊断)
来源仓库:https://github.com/shinpr/claude-code-workflows
仓库路径:skills/recipe-diagnose
安装命令:
npx skills add https://github.com/shinpr/claude-code-workflows --skill recipe-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/shinpr/claude-code-workflows --skill recipe-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。

recipe-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、任务场景快速定位候选结果。通过命令行调用,需提供查询目标和筛选条件。安装前建议确认权限范围和维护状态,注意可能触发联网或文件读写操作。

适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

来源仓库内路径:skills/recipe-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/shinpr/claude-code-workflows --skill recipe-di…;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

    它标注为只读,先用只读任务试一次,确认行为符合预期再扩大使用范围。

常见问题

6 条

recipe-diagnose 怎么安装?

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

recipe-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。

recipe-diagnose 需要什么权限?

权限级别是只读。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。

recipe-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。

recipe-diagnose 用的人多吗?

公开数据是累计安装 321 次,近一周安装 13 次,下载 101 次,GitHub 323 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。

recipe-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。
  • 标注为只读权限,试用阶段不容易误改本地文件。

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。
  • 只想先试试效果、不希望它改动本地文件的人。

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
recipe-diagnose

食谱诊断 · 本页

321323只读未明确通过

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

Context: Diagnosis flow to identify root cause and present solutions

Target problem: $ARGUMENTS

Orchestrator Definition

Core Identity: "I am not a worker. I am an orchestrator."

Execution Method:

  • Investigation → performed by investigator
  • Verification → performed by verifier
  • Solution derivation → performed by solver

Orchestrator invokes sub-agents and passes structured JSON between them.

Task Registration: Register execution steps using TaskCreate and proceed systematically. Update status using TaskUpdate.

Step 0: Problem Structuring (Before investigator invocation)

0.1 Problem Type Determination

TypeCriteria
Change FailureIndicates some change occurred before the problem appeared
New DiscoveryNo relation to changes is indicated

If uncertain, ask the user whether any changes were made right before the problem occurred.

0.2 Information Supplementation for Change Failures

If the following are unclear, ask with AskUserQuestion before proceeding:

  • What was changed (cause change)
  • What broke (affected area)
  • Relationship between both (shared components, etc.)

0.3 Problem Essence Understanding

Invoke rule-advisor via Agent tool:

subagent_type: rule-advisor
description: "Problem essence analysis"
prompt: Identify the essence and required rules for this problem: [Problem reported by user]

Confirm from rule-advisor output:

  • taskAnalysis.mainFocus: Primary focus of the problem
  • mandatoryChecks.taskEssence: Root problem beyond surface symptoms
  • selectedRules: Applicable rule sections
  • warningPatterns: Patterns to avoid

0.4 Reflecting in investigator Prompt

Include the following in investigator prompt:

  1. Problem essence (taskEssence)
  2. Key applicable rules summary (from selectedRules)
  3. Investigation focus (investigationFocus): Convert warningPatterns to "points prone to confusion or oversight in this investigation"
  4. For change failures, additionally include:

- Detailed analysis of the change content - Commonalities between cause change and affected area - Determination of whether the change is a "correct fix" or "new bug" with comparison baseline selection

Diagnosis Flow Overview

Problem → investigator → verifier → solver ─┐
                 ↑                          │
                 └── coverage insufficient ─┘
                      (max 2 iterations)

coverage sufficient → Report

Context Separation: Pass only structured JSON output to each step. Each step starts fresh with the JSON data only.

Execution Steps

Register the following using TaskCreate and execute:

Step 1: Investigation (investigator)

Agent tool invocation:

subagent_type: investigator
description: "Investigate problem"
prompt: |
  Comprehensively collect information related to the following phenomenon.

  Phenomenon: [Problem reported by user]

  Problem essence: [taskEssence from Step 0.3]
  Investigation focus: [investigationFocus from Step 0.4]

  [For change failures, additionally include:]
  Change details: [What was changed]
  Affected area: [What broke]
  Shared components: [Commonalities between cause and effect]

Expected output: pathMap (execution paths per symptom), failurePoints (faults found at each node), impactAnalysis per failure point, unexplored areas, investigation limitations

Step 2: Investigation Quality Check

Review investigation output:

Quality Check (verify JSON output contains the following):

  • pathMap exists with at least one symptom, and each symptom has at least one path with nodes listed
  • Each failure point has: location, upstreamDependency, symptomExplained, causalChain (reaching a stop condition), checkStatus, evidence with a source citing a specific file or location
  • Each failure point has comparisonAnalysis (normalImplementation found or explicitly null)
  • causeCategory for each failure point is one of: typo / logic_error / missing_constraint / design_gap / external_factor
  • investigationSources covers at least 3 distinct source types (code, history, dependency, config, document, external)
  • Investigation covers investigationFocus items (when provided in Step 0.4)
  • All nodes on mapped paths have been checked (no path was abandoned after finding the first fault)

If quality insufficient: Re-run investigator specifying missing items explicitly:

prompt: |
  Re-investigate with focus on the following gaps:
  - Missing: [list specific missing items from quality check]

  Previous investigation results (for context, do not re-investigate covered areas):
  [Previous investigation JSON]

design_gap Escalation:

When investigator output contains causeCategory: design_gap or recurrenceRisk: high:

  1. Insert user confirmation before verifier execution
  2. Use AskUserQuestion: "A design-level issue was detected. How should we proceed?"

- A: Attempt fix within current design - B: Include design reconsideration

  1. If user selects B, pass includeRedesign: true to solver

Proceed to verifier once quality is satisfied.

Step 3: Verification (verifier)

Agent tool invocation:

subagent_type: verifier
description: "Verify investigation results"
prompt: Verify the following investigation results.

Investigation results: [Investigation JSON output]

Expected output: Coverage check (missing paths, unchecked nodes), Devil's Advocate evaluation per failure point, failure point evaluation with checkStatus, coverage assessment

Coverage Criteria:

  • sufficient: Main paths traced, all critical nodes checked, each failure point individually evaluated
  • partial: Main paths traced, some nodes unchecked or some failure points at blocked/not_reached
  • insufficient: Significant paths untraced, or critical nodes not investigated

Step 4: Solution Derivation (solver)

Agent tool invocation:

subagent_type: solver
description: "Derive solutions"
prompt: Derive solutions based on the following verified failure points.

Confirmed failure points: [verifier's conclusion.confirmedFailurePoints]
Refuted failure points: [verifier's conclusion.refutedFailurePoints]
Failure point relationships: [verifier's conclusion.failurePointRelationships]
Impact analysis: [investigator's impactAnalysis]
Coverage assessment: [sufficient/partial/insufficient]

Expected output: Multiple solutions (at least 3), tradeoff analysis, recommendation and implementation steps, residual risks

Completion condition: coverageAssessment=sufficient

When not reached:

  1. Return to Step 1 with unchecked areas identified by verifier as investigation targets
  2. Maximum 2 additional investigation iterations
  3. After 2 iterations without reaching sufficient, present user with options:

- Continue additional investigation - Execute solution at current coverage level

Step 5: Final Report Creation

Prerequisite: coverageAssessment=sufficient achieved

After diagnosis completion, report to user in the following format:

## Diagnosis Result Summary

### Identified Failure Points
[Confirmed failure points from verification results]
- Per failure point: location, symptom explained, finalStatus

### Verification Process
- Path coverage: [Paths traced and nodes checked]
- Additional investigation iterations: [0/1/2]
- Coverage assessment: [sufficient/partial/insufficient]

### Recommended Solution
[Solution derivation recommendation]

Rationale: [Selection rationale]

### Implementation Steps
1. [Step 1]
2. [Step 2]
...

### Alternatives
[Alternative description]

### Residual Risks
[solver's residualRisks]

### Post-Resolution Verification Items
- [Verification item 1]
- [Verification item 2]

Completion Criteria

  • Executed investigator and obtained pathMap, failurePoints, and impactAnalysis
  • Performed investigation quality check and re-ran if insufficient
  • Executed verifier and obtained coverage assessment
  • Executed solver
  • Achieved coverageAssessment=sufficient (or obtained user approval after 2 additional iterations)
  • Presented final report to user

平台分布

Codex

34.85%
按下载量换算35

Claude

30.25%
按下载量换算31

Cursor

19.08%
按下载量换算19

Gemini CLI

11.08%
按下载量换算11

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

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。目前只有一个公开来源,建议同时核对源仓库。

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