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recipe-diagnose食谱诊断

Agent Skill

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。

总安装

321

周安装

13

GitHub Stars

323

下载量

101
CodexClaudeCursorGemini CLI

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

unknown

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: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

简介

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。

SKILL.md

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

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

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

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

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。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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