スキル一覧に戻る
majesticlabs-dev

query-expansion-strategy

by majesticlabs-dev

18🍴 1📅 2026年1月24日
GitHubで見るManusで実行

SKILL.md


name: query-expansion-strategy description: Query fan-out coverage for AI visibility. Covers semantic variation analysis and sub-question targeting. allowed-tools: WebSearch

Query Expansion Strategy

Maximize AI visibility through query fan-out coverage.

How LLMs Process Queries

LLMs expand queries into 5-10 semantic variations (sub-questions) before generating responses. To get cited:

  1. Cover topic clusters comprehensively
  2. Include semantic variations naturally
  3. Address related questions
  4. Build entity relationships
  5. Create topical depth

Query Fan-Out Analysis

Example: "How to prioritize leads" fans out to:

  • "What methodologies exist for lead prioritization?"
  • "What tools help with lead scoring?"
  • "What metrics indicate lead quality?"
  • "How do sales teams rank prospects?"
  • "What is lead scoring automation?"

Your content must answer ALL sub-questions to maximize visibility.

Tools for Fan-Out Analysis

ToolUse
KuforiaVisualizes how AI breaks down topics
Dan's Fan-out ToolShows sub-question decomposition
ChatGPT/PerplexityAsk "what sub-questions would you ask to answer X?"

Semantic Coverage Checklist

For any target topic:

  1. Core question - Direct answer to primary query
  2. Definition - What is X? (for newcomers)
  3. How-to - How do you do X?
  4. Why - Why is X important?
  5. Comparison - How does X compare to Y?
  6. Examples - What are examples of X?
  7. Tools - What tools help with X?
  8. Metrics - How do you measure X?
  9. Mistakes - What mistakes to avoid with X?
  10. Trends - What's changing about X?

Content Structure for Fan-Out

Recommended sections:

## What is [Topic]?
[Definition for newcomers]

## Why [Topic] Matters
[Business case, importance]

## How to [Topic]
[Step-by-step methodology]

## [Topic] Tools and Software
[Tool comparison table]

## [Topic] Metrics to Track
[KPIs and measurement]

## Common [Topic] Mistakes
[What to avoid]

## FAQ
### [Sub-question 1]?
[Complete answer]

### [Sub-question 2]?
[Complete answer]

Semantic Footprint Expansion

Build entity relationships around your topic:

Primary Topic: Lead Scoring
├── Related Concepts: lead qualification, MQL, SQL, BANT
├── Tools: HubSpot, Salesforce, Marketo
├── Metrics: conversion rate, lead velocity
├── Personas: sales rep, marketing manager, SDR
└── Use Cases: B2B sales, SaaS, enterprise

Include related terms naturally throughout content.

Analysis Output

When analyzing content for query expansion:

Target Query: [query]

Sub-Questions Covered: X/10
☑ Definition/What is
☑ How-to/Process
☐ Why/Importance (MISSING)
☐ Comparison (MISSING)
☑ Tools/Software
...

Semantic Coverage: X%
Missing Entities: [list]

Recommendations:
1. Add section on [missing sub-question]
2. Include comparison with [related concept]
3. Add FAQ addressing [query variation]

スコア

総合スコア

60/100

リポジトリの品質指標に基づく評価

SKILL.md

SKILL.mdファイルが含まれている

+20
LICENSE

ライセンスが設定されている

+10
説明文

100文字以上の説明がある

0/10
人気

GitHub Stars 100以上

0/15
最近の活動

3ヶ月以内に更新がある

0/10
フォーク

10回以上フォークされている

0/5
Issue管理

オープンIssueが50未満

+5
言語

プログラミング言語が設定されている

+5
タグ

1つ以上のタグが設定されている

0/5

レビュー

💬

レビュー機能は近日公開予定です