Aibijia Price Comparison Platform

SkillWeb & browsing

Your AI can compare prices for AI account tokens across multiple platforms and point you to the cheapest reliable options. It collects prices from different platforms and combines them so you can see everything side by side. That makes it easier to find the tokens you need without overpaying.

Available today. Use it from your connected AI after setup.

After adding it, tell your AI which AI tokens you are shopping for and ask it to compare prices across platforms. It will pull together current offers so you can pick the cheapest reliable one.

Then ask your AI: use the Aibijia Price Comparison Platform skill

What your AI can do with it

  • Compare AI account token prices across multiple platforms
  • Collect current prices from different platforms and put them together in one place
  • Find the cheapest offers for the AI tokens you need
  • Surface cheap and reliable AI account token options
  • Check prices side by side before you buy

What this skill tells your AI

The instructions your AI receives, as published by aradotso/trending-skills in skills/aibijia-price-comparison/SKILL.md and read by ahel’s review.

Skill by ara.so — Daily 2026 Skills collection.

Aibijia is a multi-platform price scraping and comparison website for AI tokens (ChatGPT Plus CDKs, API keys, etc.). It aggregates prices from various resellers/agents across platforms, helping users find the cheapest reliable source and avoid scams.

Live site: https://aibijia.org Telegram: https://t.me/ai_bi_jia_notice


What This Project Does

  • Scrapes token/CDK prices from multiple card-selling platforms (卡网)
  • Compares prices across vendors for the same type of AI account (e.g., ChatGPT Plus, GPT Pro)
  • Aggregates vendor reliability info via community submissions
  • Exposes price differences between resellers sourcing from the same upstream

Project Structure

Since the repo is primarily a community/data project with a web frontend, the core components are:

AIbijia/
├── assets/           # Static assets (banner, images)
├── data/             # Price data / scraped results (JSON/CSV)
├── scrapers/         # Platform price scrapers
├── frontend/         # Website UI (aibijia.org)
└── SKILL.md

Installation & Setup

Clone the Repository

git clone https://github.com/ka-pi-ba-la/AIbijia.git
cd AIbijia

Install Dependencies

If Python-based scrapers:

pip install -r requirements.txt

If Node.js-based:

npm install
# or
pnpm install

Core Concepts

Token Types Tracked

Token TypeExample Price RangeNotes
ChatGPT Plus CDK¥30–¥60Same upstream, different markup
GPT Pro (shared)~¥20/personSplit among multiple users
API Keys (各模型)VariesPer-token pricing
Claude / GeminiVariesScraped from resellers

Price Scraping Pattern

import requests
from bs4 import BeautifulSoup
import json
from datetime import datetime

class TokenPriceScraper:
    """
    Base scraper for AI token price platforms.
    Each platform subclasses this with custom parsing.
    """

    def __init__(self, platform_name: str, base_url: str):
        self.platform_name = platform_name
        self.base_url = base_url
        self.session = requests.Session()
        self.session.headers.update({
            "User-Agent": "Mozilla/5.0 (compatible; Aibijia/1.0)"
        })

    def fetch_page(self, url: str) -> BeautifulSoup:
        resp = self.session.get(url, timeout=10)
        resp.raise_for_status()
        return BeautifulSoup(resp.text, "html.parser")

    def parse_prices(self, soup: BeautifulSoup) -> list[dict]:
        """Override in subclass to extract price data."""
        raise NotImplementedError

    def scrape(self) -> list[dict]:
        soup = self.fetch_page(self.base_url)
        prices = self.parse_prices(soup)

        # Annotate with metadata
        for item in prices:
            item["platform"] = self.platform_name
            item["scraped_at"] = datetime.utcnow().isoformat()

        return prices


class KawangScraper(TokenPriceScraper):
    """Example scraper for a 卡网 (card platform)."""

    def parse_prices(self, soup: BeautifulSoup) -> list[dict]:
        results = []

        # Adapt selectors to target platform's HTML structure
        for card in soup.select(".product-card"):
            name = card.select_one(".product-name")
            price = card.select_one(".product-price")
            stock = card.select_one(".product-stock")

            if name and price:
                results.append({
                    "name": name.get_text(strip=True),
                    "price_cny": float(
                        price.get_text(strip=True)
                             .replace("¥", "")
                             .replace(",", "")
                    ),
                    "in_stock": stock and "有货" in stock.get_text(),
                })

        return results

Aggregating Prices Across Platforms

import asyncio
import aiohttp
from dataclasses import dataclass

@dataclass
class PriceListing:
    token_type: str
    platform: str
    price_cny: float
    in_stock: bool
    url: str
    scraped_at: str

async def aggregate_all_platforms(platforms: list[TokenPriceScraper]) -> list[PriceListing]:
    """
    Run all scrapers concurrently and merge results.
    """
    results = []

    async def run_scraper(scraper):
        loop = asyncio.get_event_loop()
        # Run sync scraper in thread pool
        data = await loop.run_in_executor(None, scraper.scrape)
        return data

    tasks = [run_scraper(p) for p in platforms]
    all_data = await asyncio.gather(*tasks, return_exceptions=True)

    for platform_data in all_data:
        if isinstance(platform_data, Exception):
            print(f"Scraper error: {platform_data}")
            continue
        results.extend(platform_data)

    return results


def find_cheapest(listings: list[PriceListing], token_type: str) -> list[PriceListing]:
    """Filter and sort by price for a specific token type."""
    filtered = [
        l for l in listings
        if token_type.lower() in l.token_type.lower()
        and l.in_stock
    ]
    return sorted(filtered, key=lambda x: x.price_cny)


# Usage
async def main():
    platforms = [
        KawangScraper("platform_a", "https://example-card-site-a.com/chatgpt"),
        KawangScraper("platform_b", "https://example-card-site-b.com/chatgpt"),
    ]

    all_listings = await aggregate_all_platforms(platforms)
    cheapest = find_cheapest(all_listings, "ChatGPT Plus")

    print("Cheapest ChatGPT Plus CDKs:")
    for listing in cheapest[:5]:
        print(f"  ¥{listing.price_cny} — {listing.platform}")

asyncio.run(main())

Vendor Submission API

The site exposes a submission endpoint for community-sourced vendors:

import requests
import os

AIBIJIA_API = "https://aibijia.org/api"  # hypothetical endpoint

def submit_vendor(vendor_info: dict) -> dict:
    """
    Submit a new vendor/price source for review.

    vendor_info keys:
      - name: str          Vendor/platform name
      - url: str           Purchase URL
      - token_type: str    e.g. "ChatGPT Plus CDK"
      - price_cny: float   Current price in RMB
      - notes: str         Optional reliability notes
    """
    resp = requests.post(
        f"{AIBIJIA_API}/submit",
        json=vendor_info,
        headers={
            "Content-Type": "application/json",
            # Use env var if auth is required:
            "Authorization": f"Bearer {os.environ.get('AIBIJIA_API_KEY', '')}",
        },
        timeout=10,
    )
    resp.raise_for_status()
    return resp.json()


# Example usage
result = submit_vendor({
    "name": "某卡网",
    "url": "https://example-card-site.com/gpt-plus",
    "token_type": "ChatGPT Plus CDK",
    "price_cny": 32.0,
    "notes": "24h售后,支持补货",
})
print(result)

Data Storage Pattern

import json
import os
from pathlib import Path
from datetime import datetime

DATA_DIR = Path("./data")

def save_price_snapshot(listings: list[dict], token_type: str):
    """Save a timestamped price snapshot to data/."""
    DATA_DIR.mkdir(exist_ok=True)

    date_str = datetime.utcnow().strftime("%Y-%m-%d")
    filename = DATA_DIR / f"{token_type.replace(' ', '_')}_{date_str}.json"

    snapshot = {
        "token_type": token_type,
        "captured_at": datetime.utcnow().isoformat(),
        "count": len(listings),
        "listings": listings,
    }

    with open(filename, "w", encoding="utf-8") as f:
        json.dump(snapshot, f, ensure_ascii=False, indent=2)

    print(f"Saved {len(listings)} listings to {filename}")


def load_latest_snapshot(token_type: str) -> dict | None:
    """Load the most recent snapshot for a token type."""
    pattern = f"{token_type.replace(' ', '_')}_*.json"
    files = sorted(DATA_DIR.glob(pattern), reverse=True)

    if not files:
        return None

    with open(files[0], encoding="utf-8") as f:
        return json.load(f)

Community Reporting (Avoid Scams)

Post scam reports as GitHub Issues or submit to the repo:

## 避雷报告模板

**平台名称:** xxx卡网
**购买时间:** 2026-04-28
**商品:** ChatGPT Plus CDK
**价格:** ¥35
**问题:** CDK已失效,无法联系售后
**证据:** [截图]
**建议:** 避免购买

Configuration

# config.py — Aibijia scraper configuration

import os

CONFIG = {
    # Scraping behavior
    "request_timeout": int(os.environ.get("SCRAPE_TIMEOUT", "10")),
    "rate_limit_seconds": float(os.environ.get("SCRAPE_RATE_LIMIT", "2.0")),
    "max_retries": int(os.environ.get("SCRAPE_MAX_RETRIES", "3")),

    # Proxy (optional, for bot detection avoidance)
    "proxy": os.environ.get("HTTP_PROXY", None),

    # Data output
    "data_dir": os.environ.get("DATA_DIR", "./data"),

    # Notifications (Telegram)
    "telegram_bot_token": os.environ.get("TELEGRAM_BOT_TOKEN"),
    "telegram_channel_id": os.environ.get("TELEGRAM_CHANNEL_ID"),

    # Price alert threshold (alert if price drops below X CNY)
    "alert_price_threshold": float(os.environ.get("ALERT_PRICE_CNY", "30.0")),
}

Environment Variables

# .env (never commit this file)
SCRAPE_TIMEOUT=15
SCRAPE_RATE_LIMIT=3.0
HTTP_PROXY=http://proxy.example.com:8080
DATA_DIR=./data
TELEGRAM_BOT_TOKEN=your_bot_token_here
TELEGRAM_CHANNEL_ID=@ai_bi_jia_notice
ALERT_PRICE_CNY=28.0

Telegram Price Alert Bot

import os
import asyncio
from telegram import Bot

async def send_price_alert(listings: list[dict], threshold: float):
    """
    Send Telegram alert when ChatGPT Plus CDK drops below threshold price.
    """
    bot = Bot(token=os.environ["TELEGRAM_BOT_TOKEN"])
    channel = os.environ["TELEGRAM_CHANNEL_ID"]

    cheap = [l for l in listings if l["price_cny"] <= threshold and l["in_stock"]]

    if not cheap:
        return

    lines = [f"🔥 低价预警!ChatGPT Plus CDK ≤ ¥{threshold}\n"]
    for l in cheap[:5]:
        lines.append(f"• ¥{l['price_cny']} — {l['platform']}")

    await bot.send_message(
        chat_id=channel,
        text="\n".join(lines),
        disable_web_page_preview=True,
    )

asyncio.run(send_price_alert(all_listings, threshold=30.0))

Common Patterns

Daily Cron Job (GitHub Actions)

# .github/workflows/scrape.yml
name: Daily Price Scrape

on:
  schedule:
    - cron: "0 2 * * *"   # 2 AM UTC daily
  workflow_dispatch:

jobs:
  scrape:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-python@v5
        with:
          python-version: "3.12"
      - run: pip install -r requirements.txt
      - run: python scrapers/run_all.py
        env:
          TELEGRAM_BOT_TOKEN: ${{ secrets.TELEGRAM_BOT_TOKEN }}
          TELEGRAM_CHANNEL_ID: ${{ secrets.TELEGRAM_CHANNEL_ID }}
      - uses: actions/upload-artifact@v4
        with:
          name: price-data
          path: data/

Troubleshooting

ProblemCauseFix
Scraper returns empty resultsTarget site changed HTML structureUpdate CSS selectors in parse_prices()
403 / blocked requestsBot detection on target platformAdd proxy via HTTP_PROXY env var or rotate User-Agent
Prices staleCron not runningCheck GitHub Actions logs; run python scrapers/run_all.py manually
Telegram alerts not sendingWrong token/channelVerify TELEGRAM_BOT_TOKEN and TELEGRAM_CHANNEL_ID env vars
CDK already used / invalidUpstream fraudReport in repo issues with evidence; avoid that vendor

Anti-Bot Countermeasures

import time
import random

def polite_get(session, url: str, min_delay=1.5, max_delay=4.0) -> str:
    """Add random delay between requests to avoid rate limiting."""
    time.sleep(random.uniform(min_delay, max_delay))
    resp = session.get(url, timeout=10)
    resp.raise_for_status()
    return resp.text

Contributing Price Sources

  1. Fork the repo
  2. Add your vendor/source to data/sources.json
  3. Open a PR with evidence of reliability (screenshots, purchase history)
  4. Community reviews and merges
// data/sources.json entry format
{
  "id": "vendor_slug",
  "name": "平台名称",
  "url": "https://example-card-site.com",
  "token_types": ["ChatGPT Plus CDK", "Claude API"],
  "verified": false,
  "submitted_by": "github_username",
  "notes": "24h售后,微信群支持"
}

Signals

GitHub stars
78
Forks
13
Last commit
Jul 2026

ahel review

  • K1binfo
    installs-packages

Automated review, not a security audit. Ruleset v1+k2.

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aibijia-price-comparison
Source
github.com/aradotso/trending-skills