Ali1688 Sourcing
Scrape 1688.com listings to compare factory MOQs, tiered wholesale prices, and production hubs before you commit to a SKU.
Overview
Ali1688 Sourcing is an agent skill for the Validate phase that finds 1688 factory and wholesale price listings via a Python scraper for ecommerce supplier research.
Install
npx skills add https://github.com/zhuhongyin/global-ecom-skills --skill ali1688-sourcingWhat is this skill?
- CLI scraper for 1688.com keyword search with optional province filtering
- Structured product records with MOQ-style quantity tiers and per-tier pricing
- Reference maps for major China production clusters by product category
- Certification hints (CE, FCC, ROHS, FDA, UL) for export-minded sourcing checks
- Falls back to mock data when requests/BeautifulSoup are not installed
- Reference data covers 6 product-category production hub groupings (e.g. office furniture, small appliances)
- 5 export certification types documented (CE, FCC, ROHS, FDA, UL)
Adoption & trust: 543 installs on skills.sh; 1 GitHub stars; 2/3 security scanners passed (skills.sh audits).
What problem does it solve?
You cannot estimate real wholesale tiers or factory options for a product idea without manually trawling Chinese wholesale marketplaces.
Who is it for?
Indie ecommerce operators comparing MOQs and factory regions on 1688 before ordering samples or listing on Shopify/Amazon.
Skip if: Builders who need US/EU retail analytics, automated purchase orders, or compliance filing without human supplier vetting.
When should I use this skill?
You need 1688 factory or wholesale price research with keyword search, optional province filter, and structured JSON output for margin modeling.
What do I get? / Deliverables
You get JSON-structured 1688 product candidates with tiered prices and metadata you can plug into margin spreadsheets and shortlists.
- JSON export of 1688 products with tiered price tiers and URLs
- Console or file output from scrape_1688.py runs for supplier shortlists
Recommended Skills
Journey fit
How it compares
Lightweight Python scraper skill—not a licensed sourcing agent, ERP, or customs brokerage workflow.
Common Questions / FAQ
Who is ali1688-sourcing for?
Solo and small-shop sellers researching factory wholesale prices and production areas on 1688 during product validation.
When should I use ali1688-sourcing?
Use it in Validate when modeling unit economics, comparing tiered MOQ pricing, or shortlisting suppliers before sample orders.
Is ali1688-sourcing safe to install?
Check the Security Audits panel on this page; the skill runs outbound HTTP scraping—review code, credentials, and robots/terms compliance before use.
SKILL.md
READMESKILL.md - Ali1688 Sourcing
#!/usr/bin/env python3 """ 1688 Factory & Wholesale Price Scraper Find factories and wholesale prices on 1688.com Usage: python scrape_1688.py --keyword "升降桌" --limit 20 python scrape_1688.py --keyword "desk converter" --province 浙江 """ import argparse import json import re import time import random from dataclasses import dataclass, asdict from datetime import datetime from typing import List, Optional from urllib.parse import quote_plus try: import requests from bs4 import BeautifulSoup HAS_REQUESTS = True except ImportError: HAS_REQUESTS = False print("Warning: requests/beautifulsoup4 not installed, will use mock data") PRODUCTION_AREAS = { "办公家具": ["浙江安吉", "广东佛山", "江苏苏州"], "小家电": ["广东顺德", "浙江慈溪"], "箱包": ["浙江平湖", "广东花都"], "玩具": ["广东澄海", "浙江云和"], "纺织品": ["浙江绍兴", "江苏南通"], "五金工具": ["浙江永康", "广东东莞"], } CERTIFICATIONS = { "CE": "欧洲安全认证", "FCC": "美国联邦通信认证", "ROHS": "环保认证", "FDA": "美国食品药品认证", "UL": "美国安全认证", } USER_AGENTS = [ "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36", "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36", "Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:121.0) Gecko/20100101 Firefox/121.0", ] @dataclass class PriceTier: quantity: str price: float @dataclass class Product: product_id: str title: str url: str image_url: str price_tiers: List[PriceTier] starting_price: float moq: int material: str colors: List[str] size: str weight: str packaging: str lead_time: str certifications: List[str] customization: bool sample_available: bool sample_price: Optional[float] @dataclass class Factory: rank: int company_name: str company_url: str verified: bool verification_type: str location: dict main_products: List[str] factory_info: dict products: List[Product] trade_info: dict ratings: dict transaction_history: dict contact: dict notes: str @dataclass class SourcingGuide: recommended_factories: List[dict] negotiation_tips: List[str] quality_checklist: List[str] shipping_options: List[dict] @dataclass class Ali1688SearchResult: keyword: str search_time: str total_results: int returned_results: int lowest_price: float highest_price: float average_price: float median_price: float main_production_areas: List[str] recommended_starting_price: float wholesale_price: dict factories: List[Factory] sourcing_guide: SourcingGuide price_for_calculator: dict class Ali1688Scraper: def __init__(self): self.base_url = "https://www.1688.com" self.session = None if HAS_REQUESTS: self.session = requests.Session() self.session.headers.update({ "Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,*/*;q=0.8", "Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8", "Accept-Encoding": "gzip, deflate, br", "Connection": "keep-alive", }) def _get_headers(self) -> dict: return { "User-Agent": random.choice(USER_AGENTS), "Referer": self.base_url, } def _fetch_page(self, url: str, retries: int = 3) -> Optional[str]: if not HAS_REQUESTS or not self.session: return None for attempt in range(retries): try: time.sleep(random.uniform(1, 2)) response = self.session.get( url, headers=self._get_headers(), timeout=30, allow_redirects=True ) if response.status_code == 200: response.encoding = 'utf-8'