Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Real-time Base chain alpha intelligence for ZHAO (CryptoZhaoX). Use when scanning Base memecoins for second-wave setups or early gem launches; checking GMGN...
Real-time Base chain alpha intelligence for ZHAO (CryptoZhaoX). Use when scanning Base memecoins for second-wave setups or early gem launches; checking GMGN...
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.
ZHAO's on-chain intelligence toolkit for Base chain. Data-first, no hype. Alert only on high-conviction setups.
python3 skills/base-alpha-scanner/scripts/scan_base.py --mode <mode> [addr] Modes: trending β Top Base tokens ranked by conviction score (1h) new β Early launch scanner: 0β45min + 45minβ3h windows token <addr> β Deep dive on specific token (all timeframes) holders <addr> β Holder distribution + concentration check gmgn <addr> β GMGN smart money data (may need browser fallback)
python3 skills/base-alpha-scanner/scripts/scan_narrative.py --mode <mode> Modes: clanker β Latest Clanker token deployments on Base bankr β Bankr.fun trending tokens (Farcaster-native) virtual β VIRTUAL Protocol AI agent ecosystem ai β Broad AI narrative scan across Base
scan_base.py --mode trending β identify what's moving For anything score β₯ 60: scan_base.py --mode token <addr> β deep dive If AI narrative or Farcaster signals: scan_narrative.py --mode ai + clanker Apply alert rules β ping ZHAO only if threshold met
scan_base.py --mode new β check 0β45min window Score β₯ 60 + clean signals β immediate check with token mode Cross-reference with scan_narrative.py --mode clanker for Farcaster origin If all checks pass β early gem ping
scan_base.py --mode holders <addr> Flag if top-5 > 40% supply or any single wallet > 15% Cross with DexScreener buy/sell maker count to confirm real distribution
Read references/alert-rules.md for full ruleset. Summary: Immediate ping: Tier 1 only (vol spike + narrative + clean chart + liq > $100K) Second-wave alert: 45minβ3h old, sustained vol + holder growth, score β₯ 65 Early gem: <45min, score β₯ 60, clean team, real momentum. Max 2β3/day Mainstream (BTC/ETH/UNI): Key level breaks, on-chain flows, funding extremes
See references/api-endpoints.md for all endpoints, field names, and data source details. Key addresses: VIRTUAL token (Base): 0x0b3e328455c4059EEb9e3f84b5543F74E24e7E1b cbBTC (Base): 0xcbB7C0000aB88B473b1f5aFd9ef808440eed33Bf
Built into scan_base.py. Score β₯ 65 = alert candidate. Score < 50 = ignore. Factors: 1h volume, liquidity, buy pressure ratio, age (45minβ3h = peak), momentum, mcap.
GMGN often blocks direct API access. Fallback options: Use browser tool to navigate https://gmgn.ai/base/token/<addr> Take screenshot for ZHAO if needed Check wallet history at https://gmgn.ai/base/address/<wallet>
No clean public API. Bankr alpha comes from Warpcast: Channel: https://warpcast.com/~/channel/bankr Use web_search for recent Bankr mentions + web_fetch on Warpcast casts High signal: power users (>5K followers) buying via Bankr frame in <30min of launch
Agent frameworks, memory systems, reasoning layers, and model-native orchestration.
Largest current source with strong distribution and engagement signals.