context-crystallizer/dist/commands/bundle.js

AI Crystallization Engineering for Large Repositories - Transform massive repositories into crystallized, AI-consumable knowledge through systematic analysis and optimization. Crystallization extracts meaningful context from any readable files.

import { Command } from 'commander';
import chalk from 'chalk';
import { CrystallizerCore } from '../shared/crystallizer-core.js';
export function createBundleCommand() {
    return new Command('bundle')
        .description('Combine multiple crystallized contexts for complex understanding')
        .argument('<files...>', 'File paths to include in bundle')
        .option('-t, --max-tokens <number>', 'Maximum total tokens for bundle', '8000')
        .option('-j, --json', 'Output as JSON')
        .action(async (files, options) => {
        try {
            const core = new CrystallizerCore();
            const bundle = await core.getCrystallizedBundle(files, parseInt(options.maxTokens));
            if (options.json) {
                console.log(JSON.stringify(bundle, null, 2));
                return;
            }
            console.log(chalk.blue('📦 Crystallized Context Bundle'));
            console.log(chalk.cyan('─'.repeat(40)));
            console.log(chalk.green(`📁 Requested files: ${bundle.requestedFiles}`));
            console.log(chalk.green(`✓ Included files: ${bundle.includedFiles}`));
            console.log(chalk.yellow(`🔢 Total tokens: ${bundle.totalTokens.toLocaleString()}`));
            console.log();
            if (bundle.contexts.length === 0) {
                console.log(chalk.yellow('No crystallized contexts found for the specified files.'));
                console.log(chalk.gray('💡 Make sure the files exist and have been crystallized.'));
                return;
            }
            bundle.contexts.forEach((context, index) => {
                console.log(chalk.bold(`${index + 1}. ${context.file}`));
                console.log(chalk.gray(`   Category: ${context.category} | Complexity: ${context.complexity} | Tokens: ${context.tokenCount}`));
                console.log(chalk.white(`   ${context.purpose}`));
                if (context.keyTerms && context.keyTerms.length > 0) {
                    console.log(chalk.blue(`   Key Terms: ${context.keyTerms.join(', ')}`));
                }
                if (context.dependencies && context.dependencies.length > 0) {
                    console.log(chalk.magenta(`   Dependencies: ${context.dependencies.join(', ')}`));
                }
                if (context.patterns && context.patterns.length > 0) {
                    console.log(chalk.cyan(`   Patterns: ${context.patterns.join(', ')}`));
                }
                console.log();
            });
        }
        catch (error) {
            console.error(chalk.red('❌ Bundle creation failed:'));
            console.error(chalk.red(error instanceof Error ? error.message : String(error)));
            process.exit(1);
        }
    });
}
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