Skills base-alpha-scanner

Real-time Base chain alpha intelligence for ZHAO (CryptoZhaoX). Use when scanning Base memecoins for second-wave setups or early gem launches; checking GMGN smart money flows; analyzing holder distribution for a Base token; scanning Clanker or Bankr.fun for high-quality narrative token deployments; monitoring VIRTUAL Protocol AI agent launches; running the AI narrative scanner on Base; generating trade alerts on Base memecoins or mainstream assets (BTC/ETH/UNI); any on-chain analysis task on Base chain.

install

source · Clone the upstream repo

git clone https://github.com/openclaw/skills

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/openclaw/skills "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/a4205586/base-alpha-scanner" ~/.claude/skills/clawdbot-skills-base-alpha-scanner && rm -rf "$T"

manifest: skills/a4205586/base-alpha-scanner/SKILL.md

source content

Base Alpha Scanner

ZHAO's on-chain intelligence toolkit for Base chain. Data-first, no hype. Alert only on high-conviction setups.

Scripts

scan_base.py — Core on-chain scanner

python3 skills/base-alpha-scanner/scripts/scan_base.py --mode <mode> [addr]

Modes:

```
trending
```
— Top Base tokens ranked by conviction score (1h)
```
new
```
— Early launch scanner: 0–45min + 45min–3h windows
```
token <addr>
```
— Deep dive on specific token (all timeframes)
```
holders <addr>
```
— Holder distribution + concentration check
```
gmgn <addr>
```
— GMGN smart money data (may need browser fallback)

scan_narrative.py — Narrative & platform scanner

python3 skills/base-alpha-scanner/scripts/scan_narrative.py --mode <mode>

Modes:

```
clanker
```
— Latest Clanker token deployments on Base
```
bankr
```
— Bankr.fun trending tokens (Farcaster-native)
```
virtual
```
— VIRTUAL Protocol AI agent ecosystem
```
ai
```
— Broad AI narrative scan across Base

Workflow

Standard market scan (run on demand or every 1–2h):

```
scan_base.py --mode trending
```
→ identify what's moving
For anything score ≥ 60:
```
scan_base.py --mode token <addr>
```
→ deep dive
If AI narrative or Farcaster signals:
```
scan_narrative.py --mode ai
```
+
```
clanker
```
Apply alert rules → ping ZHAO only if threshold met

Early launch scan (continuous background):

```
scan_base.py --mode new
```
→ check 0–45min window
Score ≥ 60 + clean signals → immediate check with
```
token
```
mode
Cross-reference with
```
scan_narrative.py --mode clanker
```
for Farcaster origin
If all checks pass → early gem ping

Holder distribution check:

```
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

Alert Rules

Read

references/alert-rules.md

for full ruleset. Summary:

Immediate ping: Tier 1 only (vol spike + narrative + clean chart + liq > $100K)
Second-wave alert: 45min–3h old, sustained vol + holder growth, score ≥ 65
Early gem: <45min, score ≥ 60, clean team, real momentum. Max 2–3/day
Mainstream (BTC/ETH/UNI): Key level breaks, on-chain flows, funding extremes

API Reference

See

references/api-endpoints.md

for all endpoints, field names, and data source details.

Key addresses:

VIRTUAL token (Base):

0x0b3e328455c4059EEb9e3f84b5543F74E24e7E1b

cbBTC (Base):

0xcbB7C0000aB88B473b1f5aFd9ef808440eed33Bf

Conviction Score (0–100)

Built into

scan_base.py

. Score ≥ 65 = alert candidate. Score < 50 = ignore. Factors: 1h volume, liquidity, buy pressure ratio, age (45min–3h = peak), momentum, mcap.

GMGN Notes

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>
```

Bankr Notes

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