Binance Leaderboard Skill

On-chain wallet leaderboard ranking and address analysis. Query top traders by PnL, win rate, and more. Evaluate wallet quality with a 6-dimension scoring model.

Prerequisites

This skill requires the

baw

CLI (

@binance/agentic-wallet

npm package). If

baw

is not found:

bash

npm install -g @binance/agentic-wallet

Verify:

baw --version

should print

1.6.2

or higher. If installation fails or the user doesn't have Node.js, inform them that Node.js >= 18 is required.

When to Use

User intent	Command
Query top traders by PnL/win rate/volume	`baw leaderboard query`
Analyze a single wallet address (6-dim score + AI archetype)	`baw leaderboard analyze`
Find wallets holding specific tokens (Gem Hunter)	`baw leaderboard alpha-radar`
Save/load preset filter conditions	`baw leaderboard preset save/list`
Save/load Gem Hunter configs	`baw leaderboard alpha-radar-config save/list`

Supported Chains

Chain	chainId
BSC	`56`
Solana	`CT_501`
Base	`8453`
Ethereum	`1`

Command Tree

baw leaderboard
  query                     # Leaderboard query (Public, no auth)
  analyze                   # Single address analysis (6-dim score + AI overlay)
  alpha-radar              # Gem Hunter query (Private, agentSessionId)
  preset
    save                    # Save preset filters (Private)
    list                    # List preset filters (Private)
  alpha-radar-config
    save                    # Save Gem Hunter config (Private)
    list                    # List Gem Hunter config (Private)

All commands support

--json

for structured output.

Leaderboard Query

bash

# Basic query — top 20 by PnL on BSC, 7d period
baw leaderboard query -c 56 -p 7d -t ALL --json

# Sort by win rate, KOL tag only
baw leaderboard query -c 56 -p 30d -t KOL --sort-by 20 --json

# Pagination (page from 0, size max 20)
baw leaderboard query -c 56 -p 7d --page 0 --size 20 --json

Public endpoint — no auth required. Returns per-address PnL, win rate, volume, trade count, token distribution, daily PNL, and top earning tokens.

Key query parameters:

-c/--chain-id

(required),

-p/--period

(7d/30d/90d),

-t/--tag

(ALL/KOL/MPC),

--sort-by

(0=PnL · 20=Win Rate · 30=Total Volume · 50=Trade Count · 60=Recent Activity · 70=Profit Rate · 80=Token Count),

--order-by

(0/2=Descending · 1=Ascending),

--page

(from 0),

--size

(max 20).

Full parameter and return field reference:

references/cli.md

Single Address Analyze

bash

# Analyze a wallet address — 6-dim scoring + AI archetype
# Default scans top 1000 entries
baw leaderboard analyze -c 56 -a 0xabc... --json

# Scan more entries (up to 5000) for long-tail addresses
baw leaderboard analyze -c 56 -a 0xabc... --top-n 5000 --json

Evaluates the address across 6 dimensions (winrate 25 + stability 20 + drawdown 20 + tags 15 + pnl 10 + follow_friendly 10 = 100), then applies an AI overlay (archetype + behavior_flags + ai_adjustment ±10).

Flow: Query leaderboard top 1000 (configurable via

--top-n

) → reverse-lookup the target address → compute scores → apply AI overlay → output rating.

Rating: ⭐⭐⭐ ≥ 80 · ⭐⭐ ≥ 65 · ⭐ ≥ 50 · ❌ < 50

If the address is not in the top N (default 1000), returns "N beyond top". Use

--top-n

to increase scan range up to 5000.

Full scoring model details:

references/scoring.md

Gem Hunter

bash

# Find wallets holding specific tokens
baw leaderboard alpha-radar -c 56 \
  -t 0xtoken1,0xtoken2 \
  -m 1 --json

Private endpoint — requires

agentSessionId

. Extra required params:

-t/--tokens

(comma-separated token addresses),

-m/--match-count

(≥ 1). Supports

-p/--period

--page

--size

like query.

Returns records with the same fields as leaderboard query, but

topEarningTokens

replaced by

marchedTokens

(matched tokens).

Note: The field is spelled

marchedTokens

(not "matched") in the CLI output.

Preset & Gem Hunter Config

bash

# Save preset filter conditions (pass null/empty to clear all)
baw leaderboard preset save --config '[{"name":"MyPreset","period":"7d","winRateMin":50}]' --json

# List saved presets
baw leaderboard preset list --json

# Save Gem Hunter config (pass null/empty to clear)
baw leaderboard alpha-radar-config save -c 56 \
  --config '[{"uuid":"u1","name":"MyRadar","matchTokenCount":2,"tokenAddressList":[{"tokenAddress":"0xabc"}]}]' --json

# List saved configs
baw leaderboard alpha-radar-config list -c 56 --json

Core Rules

Scoring Model Overview

6-dimension model (total 100) + AI overlay (±10). Dimensions: winrate (25), stability (20), drawdown (20), tags (15), pnl (10), follow_friendly (10). Full tiered scoring tables and AI overlay rules:

references/scoring.md

Top-N Reverse Lookup

analyze

queries the leaderboard's top 1000 entries (configurable via

--top-n

, max 5000) and reverse-looks-up the target address. If not found, returns "N beyond top" — inform the user they can increase

--top-n

or use

binance-wallet-tracker

's address list for long-tail wallets.

Preset & Config Clearing

Passing

null

or empty array to

preset save

alpha-radar-config save

clears all saved items.

Cross-Skill Scenarios

Some scenarios require both leaderboard and wallet-tracker skills:

evaluate follow list: Use
```
binance-wallet-tracker
```
's
```
tracker follow
```
to get the user's followed addresses, then
```
leaderboard analyze
```
each one.
leaderboard diff: Query leaderboard top N, then diff against
```
binance-wallet-tracker
```
's
```
address list
```
to find untracked wallets.
batch import: Query leaderboard with filters → output address list → feed to
```
binance-wallet-tracker
```
's
```
address batch
```
command.

Write-Back Confirmation

After preset/config save operations, re-fetch via

list

to confirm the operation succeeded — don't assume success from the API response alone. If readback shows the saved data is missing, tell the user "Save may not have taken effect, please try again later" — never mention backend bugs or silent failures.

User-Facing Presentation

This skill serves end users, not developers. Internal field names, error codes, and CLI internals must never appear in user-facing output.

1. Term Mapping (internal → user-facing)

Internal value	User-facing term	Notes
`archetype: sniper`	Sniper
`archetype: swing`	Swing Trader
`archetype: accumulator`	Accumulator
`archetype: farmer`	Farmer
`archetype: mixed`	Mixed
`sort-by: 0`	Sort by PnL
`sort-by: 20`	Sort by Win Rate
`sort-by: 30`	Sort by Total Volume
`sort-by: 50`	Sort by Trade Count
`sort-by: 60`	Sort by Recent Activity
`sort-by: 70`	Sort by Profit Rate
`sort-by: 80`	Sort by Token Count
`address` (in display)	omit	Don't show raw address unless user asks; use `{addressLabel}`
`finalScore`	Score	Don't show the formula `totalScore + aiAdjustment`

Raw enum values (

sniper

swing

, etc.) may appear in CLI syntax examples and internal lookup tables, but never in user-facing replies.

2. Never expose internal identifiers

```
groupId
```
,
```
displayOrder
```
— internal IDs, never shown to users
Error codes (
```
70001001
```
, etc.) — translate to natural-language messages only
Backend behavior details (e.g. preset save returning
```
data: true
```
without persisting) — never mention "backend bug" or "silent failure"

3. Display template hygiene

Use user-facing terms in all output. The

archetype

field should be translated to its Chinese term (Sniper, Swing Trader, etc.) — never show the raw English enum value. Score should be shown as a number, not as a formula.

Error Codes

Error codes are for internal lookup only — never show numeric codes or internal names to users. Translate to natural-language messages.

Code	Internal Name	User-Facing Message
70001001	TRACKER_API_ERROR	Query failed, please try again later
70004001	TRACKER_LEADERBOARD_EMPTY	Leaderboard data is empty
70004002	TRACKER_ADDRESS_NOT_RANKED	Address not ranked (beyond top 250)

Display Templates

Leaderboard entry:

{addressLabel} | PnL: {realizedPnl} ({realizedPnlPercent}%) | Win Rate: {winRate}% | Trades: {totalTxCnt} | Tokens: {totalTradedTokens}

Address analysis rating:

📊 {addressLabel} Address Analysis

Rating: ⭐⭐⭐ (85/100)
Trading Style: Sniper
Behavior Patterns: High Frequency Small Amount, Nocturnal Active

Dimension Scores:
  Win Rate: 22/25 | Stability: 18/20 | Drawdown: 16/20
  Tags: 15/15 | PnL: 8/10 | Trackability: 6/10

AI Adjustment: +5 (consistent trading style, stable pattern)

Full CLI Reference

```
references/cli.md
```
— All commands with parameter tables, return field tables, and examples
```
references/scoring.md
```
— 6-dimension scoring model, AI overlay, rating standards

binance-leaderboard

NPX Install

Tags

SKILL.md Content

Binance Leaderboard Skill

Prerequisites

When to Use

Supported Chains

Command Tree

Leaderboard Query

Single Address Analyze

Gem Hunter

Preset & Gem Hunter Config

Core Rules

Scoring Model Overview

Top-N Reverse Lookup

Preset & Config Clearing

Cross-Skill Scenarios

Write-Back Confirmation

User-Facing Presentation

Error Codes

Display Templates

Full CLI Reference