SQLite Extension Generator

Purpose

This skill enables you to generate high-performance SQLite extensions automatically from natural language descriptions. It analyzes requirements, recommends optimal implementation approaches, and generates production-ready code with tests.

When to Use

Use this skill when you need to:

• Generate SQLite extensions from requirements
• Choose optimal backend language (C, Rust, Mojo)
• Analyze implementation complexity and trade-offs
• Get production-ready code with comprehensive tests
• Optimize for specific performance characteristics

Available Hooks

sqlite.extension.analyze

Analyze requirements and recommend implementation approach.

Parameters:

• description (string, required): What the extension should do
• domain (string, optional): Domain area (e.g., 'finance', 'analytics')
• performanceRequirements (object, optional): Performance constraints
  • maxLatency (number): Maximum acceptable latency (ms)
  • throughput (number): Required throughput (ops/sec)
  • memoryLimit (number): Memory limit (MB)

Returns:

• analysis (object): Detailed requirement analysis
• recommendations (array): Recommended approaches
• complexity (string): Estimated complexity ('low', 'medium', 'high')

Example:

const analysis = await fixiplug.dispatch('sqlite.extension.analyze', {
  description: 'Real-time streaming aggregation with rolling windows',
  domain: 'analytics',
  performanceRequirements: {
    maxLatency: 1,      // 1ms max
    throughput: 10000,  // 10k ops/sec
    memoryLimit: 100    // 100MB
  }
});

console.log(analysis.analysis);
// {
//   requirementType: 'streaming-aggregation',
//   estimatedComplexity: 'high',
//   keyChallenge: 'Maintaining rolling window state efficiently',
//   suggestedApproaches: ['ring-buffer', 'sliding-window']
// }

console.log(analysis.recommendations);
// [
//   {
//     backend: 'mojo',
//     confidence: 0.95,
//     reasoning: 'Best for sub-millisecond latency with high throughput',
//     pros: ['Ultra-low latency', 'Zero-copy operations', 'SIMD support'],
//     cons: ['Newer ecosystem', 'Limited libraries']
//   },
//   {
//     backend: 'rust',
//     confidence: 0.88,
//     reasoning: 'Excellent balance of performance and safety',
//     pros: ['Memory safety', 'Rich ecosystem', 'Mature tooling'],
//     cons: ['Slightly higher latency than Mojo']
//   }
// ]

sqlite.extension.recommend_path

Get recommended implementation path for requirements.

Parameters:

• requirements (object, required): Implementation requirements
  • description (string): What to build
  • performanceLevel (string): 'speed', 'balanced', or 'size'
• constraints (object, optional): Additional constraints
  • teamExperience (array): Languages team knows
  • deploymentTarget (string): Target platform

Returns:

• path (object): Recommended implementation path
• steps (array): Implementation steps
• estimatedEffort (string): Effort estimate

Example:

const path = await fixiplug.dispatch('sqlite.extension.recommend_path', {
  requirements: {
    description: 'Customer lifetime value calculation',
    performanceLevel: 'balanced'
  },
  constraints: {
    teamExperience: ['python', 'javascript'],
    deploymentTarget: 'linux-x64'
  }
});

console.log(path.path);
// {
//   backend: 'rust',
//   reasoning: 'Team can learn Rust easily from Python/JS, balanced performance',
//   learningCurve: 'moderate',
//   timeToProduction: '2-3 weeks'
// }

console.log(path.steps);
// [
//   'Set up Rust development environment',
//   'Implement core CLV calculation logic',
//   'Add SQLite FFI bindings',
//   'Write unit tests and benchmarks',
//   'Build and package extension'
// ]

sqlite.extension.generate

Generate complete SQLite extension with tests and build instructions.

Parameters:

• description (string, required): Extension functionality description
• backend (string, required): Backend language ('c', 'rust', 'mojo')
• performanceLevel (string, optional): Optimization level ('speed', 'balanced', 'size', default: 'balanced')
• includeTests (boolean, optional): Include test suite (default: true)
• includeBenchmarks (boolean, optional): Include benchmarks (default: true)
• metadata (object, optional): Additional metadata

Returns:

• code (string): Extension source code
• tests (string): Test suite code
• benchmarks (string): Benchmark code
• buildInstructions (string): How to build
• usage (string): Usage examples
• metadata (object): Generation metadata

Example:

const extension = await fixiplug.dispatch('sqlite.extension.generate', {
  description: 'Calculate portfolio Sharpe ratio with configurable risk-free rate',
  backend: 'rust',
  performanceLevel: 'speed',
  includeTests: true,
  includeBenchmarks: true,
  metadata: {
    author: 'trading-team',
    version: '1.0.0'
  }
});

console.log('Generated code length:', extension.code.length);
console.log('Test coverage:', extension.metadata.testCoverage);

// Save to files
import fs from 'fs';
fs.writeFileSync('sharpe_ratio.rs', extension.code);
fs.writeFileSync('tests.rs', extension.tests);
fs.writeFileSync('BUILD.md', extension.buildInstructions);

Example Output Structure:

{
  code: `
    use rusqlite::functions::FunctionContext;

    pub fn sharpe_ratio(ctx: &FunctionContext) -> rusqlite::Result<f64> {
      let returns: Vec<f64> = ctx.get(0)?;
      let risk_free_rate: f64 = ctx.get(1)?;
      // ... implementation
    }
  `,
  tests: `
    #[test]
    fn test_sharpe_ratio_positive() {
      // ... tests
    }
  `,
  benchmarks: `
    #[bench]
    fn bench_sharpe_ratio(b: &mut Bencher) {
      // ... benchmarks
    }
  `,
  buildInstructions: "# Building\n\n1. Install Rust...\n2. cargo build --release...",
  usage: "-- SQL Usage\nSELECT sharpe_ratio(returns, 0.02) FROM portfolio;",
  metadata: {
    backend: 'rust',
    performanceLevel: 'speed',
    estimatedPerformance: '< 1ms for 1000 data points',
    testCoverage: '95%',
    linesOfCode: 247,
    generatedAt: '2025-11-20T10:30:00Z'
  }
}

sqlite.extension.quick_generate

Quick generation from description (uses smart defaults).

Parameters:

• description (string, required): What to build
• backend (string, optional): Backend language (auto-selected if omitted)

Returns:

• Same as sqlite.extension.generate but with auto-selected options

Example:

// Simplest usage - let it choose everything
const extension = await fixiplug.dispatch('sqlite.extension.quick_generate', {
  description: 'Calculate moving average over 30-day window'
});

// Backend auto-selected based on requirements
console.log(`Generated in ${extension.metadata.backend}`);

Backend Language Guidance

When to Use C

Best for:

• Maximum portability
• Minimal dependencies
• Integrating with existing C code
• Platforms where Rust/Mojo unavailable

Characteristics:

• Latency: Low (~1-5ms)
• Memory safety: Manual
• Ecosystem: Mature, stable
• Learning curve: Moderate

When to Use Rust

Best for:

• Production systems requiring safety
• Complex logic with many edge cases
• Teams familiar with modern languages
• Long-term maintenance

Characteristics:

• Latency: Very low (~0.5-2ms)
• Memory safety: Guaranteed
• Ecosystem: Growing rapidly
• Learning curve: Moderate-High

When to Use Mojo

Best for:

• Ultra-low latency requirements (<1ms)
• High-throughput streaming data
• SIMD-heavy computations
• Cutting-edge performance

Characteristics:

• Latency: Ultra-low (~0.1-0.5ms)
• Memory safety: High
• Ecosystem: Emerging
• Learning curve: Moderate (if you know Python)

Best Practices

1. Start with Analysis

   • Always run sqlite.extension.analyze first
   • Review recommendations before generating
   • Consider team expertise and constraints

2. Choose Appropriate Performance Level

   • speed: Maximum performance, larger binary
   • balanced: Good performance, reasonable size (recommended)
   • size: Minimal binary size, acceptable performance

3. Always Include Tests

   • Set includeTests: true (default)
   • Review and extend generated tests
   • Add domain-specific test cases

4. Review Generated Code

   • Generated code is production-quality but review it
   • Customize for your specific use case
   • Add domain-specific validation

5. Benchmark Before Deploying

   • Use generated benchmarks
   • Test with realistic data volumes
   • Measure actual latency in your environment

Performance Characteristics

Generation Speed

• Analysis: ~500ms
• Code generation: ~2-5 seconds
• Full suite (code + tests + benchmarks): ~5-10 seconds

Generated Extension Performance

Backend	Typical Latency	Throughput	Binary Size
C	1-5ms	1k-10k/s	50-200 KB
Rust	0.5-2ms	5k-50k/s	200-500 KB
Mojo	0.1-0.5ms	50k-500k/s	100-300 KB

Note: Actual performance depends on extension complexity

Common Use Cases

Use Case 1: Financial Calculations

// Generate Sharpe ratio calculator
const extension = await fixiplug.dispatch('sqlite.extension.generate', {
  description: 'Sharpe ratio calculation for portfolio performance',
  backend: 'rust',
  performanceLevel: 'speed',
  includeTests: true
});

// Deploy to database
// ... save code and build

Use Case 2: Analytics Aggregations

// Generate custom aggregation function
const extension = await fixiplug.dispatch('sqlite.extension.generate', {
  description: 'Custom percentile aggregation with interpolation',
  backend: 'mojo',
  performanceLevel: 'speed',
  includeTests: true,
  includeBenchmarks: true
});

Use Case 3: String Processing

// Generate text processing function
const extension = await fixiplug.dispatch('sqlite.extension.quick_generate', {
  description: 'Fuzzy string matching using Levenshtein distance'
});
// Auto-selects C for portability

Use Case 4: Exploration

// Analyze first to explore options
const analysis = await fixiplug.dispatch('sqlite.extension.analyze', {
  description: 'Real-time fraud detection scoring',
  domain: 'security',
  performanceRequirements: {
    maxLatency: 10,
    throughput: 1000
  }
});

// Review recommendations
analysis.recommendations.forEach(rec => {
  console.log(`${rec.backend}: ${rec.reasoning}`);
});

// Generate based on analysis
const extension = await fixiplug.dispatch('sqlite.extension.generate', {
  description: 'Real-time fraud detection scoring',
  backend: analysis.recommendations[0].backend,
  performanceLevel: 'speed'
});

Error Handling

Possible errors:

• ValidationError: Invalid parameters
• GenerationError: Code generation failed
• ServiceError: SQLite service unavailable
• TimeoutError: Generation exceeded timeout

Example:

try {
  const extension = await fixiplug.dispatch('sqlite.extension.generate', params);
} catch (error) {
  if (error.name === 'GenerationError') {
    console.error('Generation failed:', error.details);
    console.error('Suggestions:', error.suggestions);
  } else if (error.name === 'TimeoutError') {
    console.error('Generation timed out, try simplifying requirements');
  } else {
    console.error('Unexpected error:', error.message);
  }
}

Prerequisites

• SQLite Extensions Framework installed
• Environment variable: SQLITE_FRAMEWORK_PATH
• Backend language toolchain (for building generated code):
  • C: gcc or clang
  • Rust: rustc + cargo
  • Mojo: mojo compiler

Related Skills

• sqlite-pattern-learner: Find proven patterns before generating
• sqlite-agent-amplification: Create dynamic tools from extensions
• sqlite-agent-context: Understand agent capabilities

Version

1.0.0 - Initial release