MCP HubMCP Hub
スキル一覧に戻る

d-inspect

majiayu000
更新日 Today
23 閲覧
58
9
58
GitHubで表示
メタgeneral

について

d-inspectは、SYMPTOM.mdの症状をもとに、バグの根本原因となる仮説をランク付けして生成するためにコードを分析します。関連するコードパスをトレースし、その推論をHYPOTHESES.mdファイルに記録します。このスキルは、症状を定義した後、検証テストの前に行い、デバッグ調査を導くために使用してください。

クイックインストール

Claude Code

推奨
プラグインコマンド推奨
/plugin add https://github.com/majiayu000/claude-skill-registry
Git クローン代替
git clone https://github.com/majiayu000/claude-skill-registry.git ~/.claude/skills/d-inspect

このコマンドをClaude Codeにコピー&ペーストしてスキルをインストールします

ドキュメント

<role> You are a code investigator. You analyze code to form hypotheses about root causes.

Core responsibilities:

  • Read symptom description
  • Inspect relevant code paths
  • Form multiple hypotheses ranked by confidence
  • Document reasoning for each hypothesis </role>
<objective> Generate ranked hypotheses about the root cause of the bug.

Flow: Load Symptom → Trace Code → Form Hypotheses → Rank by Confidence </objective>

<context> **Required files:**
  • ./.gtd/debug/current/SYMPTOM.md — Must exist

Output:

  • ./.gtd/debug/current/HYPOTHESES.md

Agents used:

  • research — During code tracing </context>
<related>
WorkflowRelationship
/d-symptomProvides symptom for analysis
/d-verifyTests these hypotheses
</related> <philosophy>

Multiple Hypotheses

Don't fixate on the first idea. Generate 3-5 competing hypotheses.

Confidence Scoring

Rate each hypothesis honestly:

  • High (70-90%): Strong evidence, most likely cause
  • Medium (40-70%): Plausible, needs verification
  • Low (10-40%): Possible but less likely

Evidence-Based

Each hypothesis needs supporting evidence from code analysis.

</philosophy> <process>

1. Load Symptom

Read ./.gtd/debug/current/SYMPTOM.md.

if ! test -f "./.gtd/debug/current/SYMPTOM.md"; then
    echo "Error: No symptom documented. Run /d-symptom first."
    exit 1
fi

2. Spawn Investigator Agent

Trigger: Immediately after loading symptom.

Fill prompt and spawn:

<objective>
Analyze root cause for symptom_file: ./.gtd/debug/current/SYMPTOM.md
</objective>

<investigation_checklist>

1. Identify Entry Points (triggers)
2. Trace Execution Flow (conditions, branches)
3. Examine Suspect Areas (logic gaps, state)
4. Check Dependencies (config, DB)
   </investigation_checklist>

<output_format>
Ranked Hypotheses (3-5):

- Description
- Evidence (File:Line)
- Confidence Level
- Verification Method
  </output_format>
Task(
  prompt=filled_prompt,
  subagent_type="researcher",
  description="Investigating root cause"
)

4. Document HYPOTHESES.md

Write to ./.gtd/debug/current/HYPOTHESES.md:

# Root Cause Hypotheses

**Analyzed:** {date}
**Status:** PENDING VERIFICATION

## Summary

Based on code analysis, here are the most likely root causes:

---

## Hypothesis 1: {Short description}

**Confidence:** High (75%)

**Description:**
{Detailed explanation of what you think is wrong}

**Evidence:**

- {Observation 1 from code}
- {Observation 2 from code}
- {Supporting fact}

**Location:**

- Files: `{file1}`, `{file2}`
- Lines: {line ranges}

**Verification Method:**
{How to confirm/reject this hypothesis}

---

## Hypothesis 2: {Short description}

**Confidence:** Medium (50%)

{Same structure as above}

---

## Hypothesis 3: {Short description}

**Confidence:** Low (25%)

{Same structure as above}

---

## Code Analysis Notes

{Any additional observations, patterns, or concerns}

</process>

<offer_next>

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
 GTD:DEBUG ► HYPOTHESES GENERATED ✓
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Hypotheses documented: ./.gtd/debug/current/HYPOTHESES.md

Total hypotheses: {N}
Highest confidence: {X}%

─────────────────────────────────────────────────────

▶ Next Up

/d-verify — verify hypotheses with debug logs

─────────────────────────────────────────────────────

</offer_next>

GitHub リポジトリ

majiayu000/claude-skill-registry
パス: skills/d-inspect

関連スキル

algorithmic-art

メタ

This Claude Skill creates original algorithmic art using p5.js with seeded randomness and interactive parameters. It generates .md files for algorithmic philosophies, plus .html and .js files for interactive generative art implementations. Use it when developers need to create flow fields, particle systems, or other computational art while avoiding copyright issues.

スキルを見る

subagent-driven-development

開発

This skill executes implementation plans by dispatching a fresh subagent for each independent task, with code review between tasks. It enables fast iteration while maintaining quality gates through this review process. Use it when working on mostly independent tasks within the same session to ensure continuous progress with built-in quality checks.

スキルを見る

executing-plans

デザイン

Use the executing-plans skill when you have a complete implementation plan to execute in controlled batches with review checkpoints. It loads and critically reviews the plan, then executes tasks in small batches (default 3 tasks) while reporting progress between each batch for architect review. This ensures systematic implementation with built-in quality control checkpoints.

スキルを見る

cost-optimization

その他

This Claude Skill helps developers optimize cloud costs through resource rightsizing, tagging strategies, and spending analysis. It provides a framework for reducing cloud expenses and implementing cost governance across AWS, Azure, and GCP. Use it when you need to analyze infrastructure costs, right-size resources, or meet budget constraints.

スキルを見る