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Skillsecommerce-full-pipeline

ecommerce-full-pipeline

Marketing & growth

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Updated 10-15 19:56

About this skill

Cross-Border E-Commerce Full-Chain Automation Tool

Task Objective

  • This skill is used for: full-process automation of cross-border e-commerce from product selection to promotion
  • Capabilities include:
    1. 1688 Product Sourcing: keyword search, detail scraping, image download, anti-scraping fallback (Mock data as a fallback)
    2. Intelligent Data Cleaning: automatic title translation, price standardization, supplier rating, profit margin calculation, batch filtering
    3. Multi-Platform Listing: one-click generation of WeChat Store/Shopify/TikTok listing templates, supporting CSV/Excel/JSON export
    4. Promotion Plan Generation: automatic keyword generation, competitor analysis, ad copywriting, budget planning
    5. Short Video Creation: automatically generates vertical promotional videos based on product images (MoviePy + Ken Burns effect)
    6. One-Click Dropshipping: direct ordering from 1688 sources, supporting stealth shipping notes, Playwright browser automation
    7. Hot Product Trend Discovery: multi-source trend aggregation (Google Trends), 6-dimension hot product scoring, automatic triggering of subsequent pipelines
    8. Xianyu Product Selection Bargain Hunting: second-hand product search, brand recognition, false listing filtering, 6-dimension bargain scoring, price monitoring, intelligent recommendations
    9. Fully Automated Pipeline: hot product discovery → sourcing → cleaning → listing → promotion → video, one-click end-to-end execution
  • Trigger conditions: when users need to engage in cross-border e-commerce business, including product selection, listing, promotion, video creation, or second-hand product selection bargain hunting

Prerequisites

  • Python 3.8+
  • Dependency installation:
pip install requests beautifulsoup4 lxml pyyaml pytrends moviepy pillow
  • 1688 / Xianyu require a logged-in state for complete sourcing; it is recommended to use them in a browser environment that is already logged in
  • Promotional video generation requires ffmpeg (a MoviePy dependency)

Operation Steps

Process 1: Full-Chain Automation (Recommended)

# 自动挖掘爆品 → 采集 → 清洗 → 上架 → 推广 → 视频生成
python main.py auto-pipeline --seed-keywords "收纳盒,蓝牙耳机" --top-n 5 --product-limit 3 --platform all

Process 2: 1688 Sourcing → Listing → Promotion

# Step 1: 采集
python main.py scrape --keyword "蓝牙耳机" --pages 2 --detail --download-images

# Step 2: 清洗（过滤毛利率低于20%、供应商评分低于60分的商品）
python main.py clean --input data/raw_products.json --min-margin 0.20 --min-score 60

# Step 3: 上架（生成多平台模板）
python main.py publish --input data/cleaned_products.json --platform all --export all

# Step 4: 推广（关键词+竞品分析+广告文案+预算）
python main.py promote --input data/cleaned_products.json --limit 10 --daily-budget 50

# Step 5: 生成推广短视频
python main.py video --input data/cleaned_products.json --limit 5 --duration 15

Process 3: Hot Product Discovery

# 自动挖掘趋势爆品
python main.py trend --categories electronics,home --top-n 10

# 指定种子词挖掘
python main.py trend --seed-keywords "充电宝,手机壳" --top-n 10

Process 4: Xianyu Product Selection Bargain Hunting

# 闲鱼搜索
python main.py xianyu --keyword "投影仪" --price-max 999 --pages 3 --condition 9成新

# 捡漏搜索（带参数需求）
python main.py hunt --keyword "投影仪 4K 云台" --budget 999 \
  --min-lumens 1000 --require-4k --require-gimbal --require-wall

# 智能推荐（多关键词综合）
python main.py recommend --keywords "投影仪,家用投影,4K投影" --budget 999 \
  --min-lumens 1000 --require-4k --require-gimbal

# 价格监控
python main.py monitor --keywords "投影仪,坚果投影" --budget 999

Process 5: One-Click Dropshipping

python main.py fulfill --source-url "https://detail.1688.com/offer/xxx.html" \
  --receiver-name "张三" --receiver-phone "13800138000" \
  --receiver-address "北京市朝阳区xxx" --sku-spec "白色/大号" --quantity 1

Core Logic

Hot Product Discovery Scoring Model

Hot product score = trend velocity × 35% + (1 - competition level) × 25% + profit potential × 25% + supply stability × 15%

Dimension Weight Scoring Logic
Trend Velocity 35% Google Trends search volume change rate; the higher the recent increase, the higher the score
Competition Level 25% Inverse indicator of market competition intensity; the lower the competition, the higher the score
Profit Potential 25% Profit margin between the 1688 supply price and the estimated selling price
Supply Stability 15% Number of suppliers and supply stability

Xianyu Bargain Hunting Scoring Model

Scoring formula: price advantage (30 points) + cost-performance ratio (20 points) + condition (15 points) + seller reputation (15 points) + parameter match (20 points) - risk deduction

Dimension Score Scoring Logic
Price Advantage 0-30 The lower the ratio of actual price to reasonable second-hand price, the higher the score; price drops add points
Cost Performance 0-20 Discount rate relative to the brand-new price; the bigger the discount, the higher the score
Condition 0-15 Brand new 15 points > Like new 13 > 99 new 12 > 90% new 10 > Normal use 7
Seller Reputation 0-15 100% positive reviews +10; number of interested buyers adds points
Spec Matching 0-20 Match degree based on user-required specs (brightness/4K/gimbal, etc.)
Risk Deduction 0-30 Risk factors such as low price with high specs falsely advertised, no brand, merchants shipping in bulk, etc.

Brand Recognition and False Advertising Filtering

  • Built-in recognition dictionary of 30+ brands (projectors/phones/laptops/tablets, etc.)
  • False advertising detection: abnormal low price with high specs (e.g. ¥200 claiming 3000+ lumens)
  • Blacklist filtering: known false advertising brand keywords, merchant bulk shipping identification

Data Cleaning Rules

  • Automatically translate titles to English (for listing on Shopify/TikTok)
  • Price normalization (unified to USD)
  • Profit margin = (estimated selling price - supply price) / estimated selling price
  • Supplier rating filtering, minimum order quantity screening

Promotional Video Generation

  • Automatically generate vertical short videos (9:16) based on MoviePy
  • Ken Burns slow zoom effects
  • Product image carousel + title overlay
  • Configurable duration (default 15 seconds)

Project Structure

ecommerce-tool/
├── main.py                  # CLI 入口（13 个子命令）
├── config.yaml              # 配置文件
├── requirements.txt         # 依赖
├── ecommerce_tool/
│   ├── scraper.py           # 1688 采集模块
│   ├── cleaner.py           # 数据清洗模块
│   ├── publisher.py          # 多平台上架模块
│   ├── promoter.py           # 推广方案模块
│   ├── video_generator.py    # 短视频生成模块
│   ├── fulfillment.py        # 一键代发模块
│   ├── trend_detector.py     # 爆品挖掘模块
│   ├── xianyu_scraper.py     # 闲鱼采集模块
│   ├── bargain_hunter.py     # 捡漏分析模块
│   └── utils.py              # 工具函数

CLI Command Overview

Command Description
scrape Collect products from 1688
clean Clean product data
publish Generate listing templates (WeChat Store/Shopify/TikTok)
promote Generate promotion plans (keywords/competitors/copy/budget)
video Generate promotional short videos
fulfill One-click dropshipping order placement
pipeline Full-process automation (collect→clean→list→promote→video)
trend Automatically mine trending hit products
auto-pipeline Fully automatic hit product pipeline (mine→collect→list→promote→video)
xianyu Xianyu product search
hunt Xianyu bargain hunt search
recommend Smart recommendations (multi-keyword comprehensive)
monitor Price monitoring

Python API Usage

from ecommerce_tool.scraper import Scraper1688
from ecommerce_tool.cleaner import DataCleaner
from ecommerce_tool.publisher import Publisher
from ecommerce_tool.promoter import Promoter
from ecommerce_tool.video_generator import VideoGenerator
from ecommerce_tool.fulfillment import FulfillmentEngine
from ecommerce_tool.trend_detector import TrendDetector
from ecommerce_tool.xianyu_scraper import XianyuScraper
from ecommerce_tool.bargain_hunter import BargainHunter

# 1688 采集
scraper = Scraper1688(config, logger)
products = scraper.search("蓝牙耳机", pages=2)

# 爆品挖掘
detector = TrendDetector(config, logger)
candidates = detector.discover(categories=["electronics"], top_n=10)

# 闲鱼捡漏
hunter = BargainHunter(config, logger)
results = hunter.hunt(
    keyword="投影仪",
    price_max=999,
    spec_requirements={
        "min_lumens": 1000,
        "support_4k": True,
        "has_gimbal": True,
        "support_wall": True,
    },
)
print(hunter.display_results(results))

Notes

  • 1688 / Xianyu have anti-scraping mechanisms; it is recommended to set reasonable request intervals (1.5-3.5 seconds)
  • Automatically fall back to Mock data when the network is unavailable (for development and debugging)
  • Video generation requires an ffmpeg environment
  • Promotional copy and keywords are AI-generated references; actual placement requires manual review
  • Xianyu bargain scores are for product selection reference only and do not constitute trading advice
  • False advertising filtering cannot 100% eliminate counterfeit goods; please verify carefully before purchasing

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