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Recipe Diagnose

skill-shinpr-claude-code-workflows-recipe-diagnose · by shinpr

Investigate problem, verify findings, and derive solutions

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Install

$ agentstack add skill-shinpr-claude-code-workflows-recipe-diagnose

✓ scanned · ✓ verified — works with Claude Code, Cursor, and more.

Security review

✓ Passed

No issues found. Passed automated security review. · v0.1.0 How review works →

  • Prompt-injection patterns
  • Secret / credential exfiltration
  • Dangerous shell & filesystem operations
  • Untrusted network calls
  • Known-malicious package signatures

What it can access

  • Network access No
  • Filesystem access No
  • Shell / process execution No
  • Environment & secrets No
  • Dynamic code execution No

From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.

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About

Execute Skill: llm-friendly-context before writing Agent prompts, handoffs, or generated artifacts.

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

| Type | Criteria | |------|----------| | Change Failure | Indicates some change occurred before the problem appeared | | New Discovery | No 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 / logicerror / missingconstraint / designgap / externalfactor
  • [ ] 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

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

Install and usage instructions live in the source repository linked above.

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Versions

  • v0.1.0 Imported from the upstream source.