telegram-bsc-airdrop-hunter — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited telegram-bsc-airdrop-hunter (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
Surfaces potential airdrop opportunities on BSC by detecting high-activity, low-liquidity tokens.
from lib.gumloop_telegram import BotConfig, send_alert, build_alert, ScheduledBot, escape_md
import requests, os, jsonconfig = BotConfig(bot_token=os.environ["TELEGRAM_BOT_TOKEN"], chat_id=os.environ["TELEGRAM_CHAT_ID"])def scan():
pairs = requests.get(f"https://api.dexscreener.com/token-pairs/v1/56", timeout=15).json()
for p in pairs:
txs = int(p.get("txns", {"h24": 0})["h24"])
liq = float(p.get("liquidity", {"usd": 0})["usd"])
if txs > 500 and liq < 5000:
msg = (
f"🎁 *Airdrop Candidate on BSC*\n"
f"Token: {escape_md(p['baseToken']['symbol'])} ({escape_md(p['baseToken']['name'])})\n"
f"24h TXs: {txs} | Liq: ${liq:,.0f}\n"
f"→ High TX + low liq = points farming"
)
send_alert(config, msg)FROM python:3.11-slim
WORKDIR /app
RUN pip install lib-gumloop-telegram requests
COPY bot.py .
CMD ["python", "bot.py"]docker build -t tg-bsc-airdrop .
docker run -d -e TELEGRAM_BOT_TOKEN=x -e TELEGRAM_CHAT_ID=y tg-bsc-airdrop| Platform | Notes |
|---|---|
| Railway | railway up with env vars |
| Fly.io | fly launch, set secrets |
| Render | Worker service, hourly schedule |
Not financial advice. Airdrop hunting carries wallet security risks. No reward guaranteed.
~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.