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Zhongwei Li
117
skills/research/scripts/context-loader.js
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117
skills/research/scripts/context-loader.js
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#!/usr/bin/env bun
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/**
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* Domain Context Loader
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*
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* Loads relevant user context from preferences based on detected research domain.
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*
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* Usage: bun context-loader.js <domain>
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*
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* Domains: travel, work_education, shopping, ai_coding, consulting
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*/
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import fs from 'fs';
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import path from 'path';
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const PREFERENCES_PATH = path.join(
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process.env.HOME,
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'Library/Mobile Documents/com~apple~CloudDocs/Geoffrey/knowledge/preferences.json'
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);
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function loadPreferences() {
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const content = fs.readFileSync(PREFERENCES_PATH, 'utf-8');
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return JSON.parse(content);
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}
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function getNestedValue(obj, keyPath) {
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return keyPath.split('.').reduce((current, key) => {
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return current && current[key] !== undefined ? current[key] : null;
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}, obj);
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}
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function detectDomain(query) {
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const queryLower = query.toLowerCase();
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const domainPatterns = {
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travel: ['trip', 'vacation', 'hotel', 'flight', 'points', 'miles', 'ski', 'travel', 'resort', 'airline', 'marriott', 'alaska'],
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work_education: ['school', 'district', 'education', 'udl', 'learning', 'student', 'teacher', 'psd', 'k-12', 'iste', 'cosn'],
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shopping: ['buy', 'purchase', 'product', 'review', 'price', 'deal', 'compare', 'best', 'recommend'],
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ai_coding: ['code', 'programming', 'api', 'framework', 'library', 'github', 'ai', 'llm', 'model', 'development'],
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consulting: ['strategy', 'client', 'engagement', 'project', 'consulting', 'advisory']
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};
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for (const [domain, patterns] of Object.entries(domainPatterns)) {
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if (patterns.some(p => queryLower.includes(p))) {
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return domain;
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}
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}
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return 'general';
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}
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function loadDomainContext(domain, preferences) {
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const domainConfig = preferences.preferences.research_domains?.[domain];
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if (!domainConfig) {
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return {
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domain,
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context: {},
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patterns: [],
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dynamic_data: []
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};
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}
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// Load context from specified keys
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const context = {};
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for (const keyPath of domainConfig.context_keys || []) {
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const value = getNestedValue(preferences.preferences, keyPath);
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if (value) {
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context[keyPath] = value;
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}
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}
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return {
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domain,
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context,
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patterns: domainConfig.research_patterns || [],
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dynamic_data: domainConfig.fetch_dynamic || []
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};
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}
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async function main() {
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const args = process.argv.slice(2);
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const domainOrQuery = args.join(' ');
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if (!domainOrQuery) {
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console.error(JSON.stringify({
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error: 'Missing domain or query',
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usage: 'bun context-loader.js <domain or query>',
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domains: ['travel', 'work_education', 'shopping', 'ai_coding', 'consulting']
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}));
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process.exit(1);
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}
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try {
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const preferences = loadPreferences();
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// Check if it's a known domain name or a query to detect from
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const knownDomains = ['travel', 'work_education', 'shopping', 'ai_coding', 'consulting'];
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const domain = knownDomains.includes(domainOrQuery.toLowerCase())
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? domainOrQuery.toLowerCase()
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: detectDomain(domainOrQuery);
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const result = loadDomainContext(domain, preferences);
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console.log(JSON.stringify(result, null, 2));
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} catch (error) {
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console.error(JSON.stringify({
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error: error.message
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}));
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process.exit(1);
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}
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}
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main();
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module.exports = { loadPreferences, detectDomain, loadDomainContext };
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263
skills/research/scripts/orchestrator.js
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263
skills/research/scripts/orchestrator.js
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#!/usr/bin/env bun
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/**
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* Deep Research Orchestrator
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*
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* Architecture based on current best practices:
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* - Single agent with iterative deepening
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* - Search → Summarize → Compress → Report
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* - Specialized models for different steps
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* - Cross-validation and reflection
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*
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* Sources:
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* - https://arxiv.org/abs/2506.18096 (Deep Research Agents survey)
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* - https://github.com/langchain-ai/open_deep_research
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* - https://openai.com/index/introducing-deep-research/
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*
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* Usage: bun orchestrator.js --query "Research topic" [--domain travel]
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*/
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import fs from 'fs';
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import path from 'path';
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const SECRETS_PATH = path.join(
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process.env.HOME,
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'Library/Mobile Documents/com~apple~CloudDocs/Geoffrey/secrets/.env'
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);
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// Load environment variables
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function loadEnv() {
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if (fs.existsSync(SECRETS_PATH)) {
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const content = fs.readFileSync(SECRETS_PATH, 'utf-8');
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for (const line of content.split('\n')) {
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const [key, ...valueParts] = line.split('=');
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if (key && valueParts.length) {
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process.env[key.trim()] = valueParts.join('=').trim();
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}
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}
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}
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}
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loadEnv();
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/**
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* Phase 1: Query Decomposition
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* Break research topic into 5-10 sub-questions from different angles
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*/
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async function decomposeQuery(topic, context) {
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// This would ideally call an LLM, but we can structure it deterministically
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const angles = [
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'factual/definitional',
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'comparative/alternatives',
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'current state/latest developments',
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'expert opinions/professional sources',
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'user experiences/reviews',
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'common problems/gotchas',
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'optimization/best practices',
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'multimedia/visual resources'
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];
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const subQueries = angles.map(angle => ({
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angle,
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query: `${topic} - ${angle}`,
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priority: angle.includes('user') || angle.includes('current') ? 'high' : 'normal'
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}));
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return {
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mainTopic: topic,
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context,
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subQueries,
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timestamp: new Date().toISOString()
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};
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}
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/**
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* Phase 2: Parallel Search
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* Execute multiple searches simultaneously
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*/
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async function executeSearches(decomposition) {
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const results = [];
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// In full implementation, this would:
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// 1. Call WebSearch for each sub-query
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// 2. Use multiple search engines
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// 3. Try different query formulations
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// 4. Launch browser-control for JS-heavy sites
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for (const sq of decomposition.subQueries) {
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results.push({
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angle: sq.angle,
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query: sq.query,
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sources: [], // Would be populated by actual searches
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status: 'pending'
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});
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}
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return results;
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}
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/**
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* Phase 3: Deep Scraping
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* Use browser-control to get full content from promising sources
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*/
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async function deepScrape(searchResults) {
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// Would call browser-control scripts for:
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// - Reddit threads (full comments)
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// - FlyerTalk forum posts
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// - Sites that block scrapers
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// - Authenticated content
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return {
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scraped: [],
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skipped: [],
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errors: []
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};
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}
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/**
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* Phase 4: Summarization
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* Compress each source into key findings
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*/
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async function summarizeFindings(sources) {
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// Would use a smaller model (gpt-4o-mini) to summarize each source
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// Extract: key claims, supporting evidence, credibility signals
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return {
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summaries: [],
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totalSources: sources.length
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};
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}
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/**
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* Phase 5: Cross-Validation
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* Check claims across sources, identify consensus vs conflicts
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*/
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async function crossValidate(summaries) {
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// Compare claims across sources
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// Identify: consensus, conflicts, unique findings
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return {
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consensus: [],
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conflicts: [],
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unique: []
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};
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}
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/**
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* Phase 6: Report Generation
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* Synthesize everything into final report
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*/
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async function generateReport(topic, context, validated) {
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const report = {
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title: `Research: ${topic}`,
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generatedAt: new Date().toISOString(),
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context: {
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domain: context.domain,
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loaded: Object.keys(context.context),
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patterns: context.patterns
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},
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executiveSummary: '',
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detailedFindings: [],
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multimediaResources: [],
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discoveries: [], // "What I discovered you might not know"
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recommendations: [],
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confidence: {
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high: [],
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needsVerification: [],
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conflicting: []
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},
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sources: []
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};
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return report;
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}
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/**
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* Main Orchestration Loop
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*/
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async function runDeepResearch(topic, options = {}) {
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console.error('🔍 Starting deep research...');
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console.error(`📋 Topic: ${topic}`);
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// Load context
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const { loadDomainContext, detectDomain, loadPreferences } = require('./context-loader.js');
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const preferences = loadPreferences();
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const domain = options.domain || detectDomain(topic);
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const context = loadDomainContext(domain, preferences);
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console.error(`🏷️ Detected domain: ${domain}`);
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console.error(`📦 Context loaded: ${Object.keys(context.context).length} keys`);
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// Phase 1: Decompose
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console.error('\n📝 Phase 1: Decomposing query...');
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const decomposition = await decomposeQuery(topic, context);
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console.error(` Generated ${decomposition.subQueries.length} sub-queries`);
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// Phase 2: Search
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console.error('\n🔎 Phase 2: Executing parallel searches...');
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const searchResults = await executeSearches(decomposition);
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console.error(` Searched ${searchResults.length} angles`);
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// Phase 3: Deep Scrape
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console.error('\n🌐 Phase 3: Deep scraping promising sources...');
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const scraped = await deepScrape(searchResults);
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console.error(` Scraped ${scraped.scraped.length} sources`);
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// Phase 4: Summarize
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console.error('\n📋 Phase 4: Summarizing findings...');
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const summaries = await summarizeFindings([...searchResults, ...scraped.scraped]);
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console.error(` Summarized ${summaries.totalSources} sources`);
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// Phase 5: Cross-validate
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console.error('\n✅ Phase 5: Cross-validating claims...');
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const validated = await crossValidate(summaries);
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console.error(` Found ${validated.consensus.length} consensus, ${validated.conflicts.length} conflicts`);
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// Phase 6: Generate report
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console.error('\n📄 Phase 6: Generating report...');
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const report = await generateReport(topic, context, validated);
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return report;
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}
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// CLI interface
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async function main() {
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const args = process.argv.slice(2);
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let topic = '';
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let domain = null;
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for (let i = 0; i < args.length; i++) {
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if (args[i] === '--query') {
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topic = args[++i];
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} else if (args[i] === '--domain') {
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domain = args[++i];
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} else if (!args[i].startsWith('--')) {
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topic = args.slice(i).join(' ');
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break;
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}
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}
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if (!topic) {
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console.error(JSON.stringify({
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error: 'Missing research topic',
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usage: 'bun orchestrator.js --query "Research topic" [--domain travel]'
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}));
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process.exit(1);
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}
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try {
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const report = await runDeepResearch(topic, { domain });
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console.log(JSON.stringify(report, null, 2));
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} catch (error) {
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console.error(JSON.stringify({
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error: error.message,
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stack: error.stack
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}));
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process.exit(1);
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}
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}
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main();
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module.exports = { runDeepResearch, decomposeQuery };
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