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sqlite-pattern-learner

Analyze historical SQLite query patterns and suggest optimization approaches.

Updated Aug 27, 2026
Search Tags:#performance#database#optimization#machine learning#patterns#sqlite#query
One-click install
npx skills add https://github.com/codetalcott/fixiplug --skill sqlite-pattern-learner
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: sqlite-pattern-learner
Source: https://github.com/codetalcott/fixiplug/tree/main/.claude/skills/sqlite-pattern-learner
Command: npx skills add https://github.com/codetalcott/fixiplug --skill sqlite-pattern-learner

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps you optimize SQLite database queries by leveraging historical data and proven patterns. Instead of reinventing solutions or struggling with manual optimization, you can get intelligent recommendations based on similar use cases, saving time and improving performance.

Core Features & Use Cases

  • Pattern Recommendations: Get suggestions for optimal query approaches based on your domain and description, with confidence scores.
  • Semantic Search: Find similar database patterns using natural language, helping you discover relevant solutions.
  • Usage Statistics: Access aggregate and per-pattern statistics on usage, success rates, and performance metrics.
  • Pattern Recording: Record new successful query patterns and their performance data for future reuse and community contribution.
  • Use Case: When building a new analytics dashboard, use this skill to find proven patterns for "real-time customer lifetime value with cohort analysis" in the "ecommerce" domain, ensuring you start with an optimized approach.

Quick Start

Use the sqlite-pattern-learner skill to get recommendations for optimizing a 'real-time customer analytics dashboard with streaming data' in the 'analytics' domain, with a minimum confidence of 0.75.

Frequently Asked Questions about sqlite-pattern-learner

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I optimize SQLite query performance using historical patterns?▼

SQLite query optimization uses learned patterns from historical database performance data to suggest improvements. Analyze your query workload against similar successful patterns in your domain to identify proven optimization approaches and reduce manual tuning effort.

Can I find database optimization patterns for my specific domain and use case?▼

Yes, semantic search across domain-specific patterns lets you discover relevant optimization strategies by describing your query problem in natural language. The Skill returns matched patterns with confidence scores, helping you select the most applicable approach for analytics, finance, ecommerce, or other data-intensive workloads.

What performance metrics and usage statistics are available for SQLite patterns?▼

Access aggregate and per-pattern statistics including usage frequency, success rates, and performance metrics. These statistics help you assess pattern reliability and choose optimizations most effective for your workload scale and query complexity.

How do I record and share successful query patterns for future reuse?▼

Record new query patterns along with their performance data and metadata through the pattern recording interface. Captured patterns contribute to the community knowledge base and enable others to benefit from your optimization discoveries across similar use cases.

What information do I need to get relevant pattern recommendations?▼

Provide your domain, a natural language description of your query task, and optionally set minimum confidence thresholds and result limits. The Skill accepts timeRange and sortBy parameters to tailor recommendations to your specific performance requirements and business context.

Can SQLite pattern optimization help with real-time analytics workloads?▼

Yes, pattern recommendations support real-time analytics dashboards and streaming data scenarios. Find proven patterns for cohort analysis, customer lifetime value calculations, and other real-time computations across analytics and ecommerce domains to accelerate dashboard development.

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