MCPInstall

binance-leaderboard

On-chain wallet leaderboard and Gem Hunter analysis on Binance Web3. Query top trader rankings by PnL, win rate, volume, trade count, or token count across BSC/Solana/Base/ETH. Analyze individual wallet addresses with a 6-dimension scoring model (winrate/stability/drawdown/ tags/pnl/followfriendly) plus AI archetype overlay. Query Gem Hunter to find wallets holding specific tokens. Save and load preset filter conditions and Gem Hunter configs. Trigger whenever the user mentions leaderboard, top traders, wallet analysis, Address Analysis, AddressScore, Gem Hunter, or wants to evaluate a wallet's trading quality — even if they don't say "leaderboard" explicitly.

Installs
2K
Last 24h
+244
Trending rank
#1119
Security
Not audited

Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/binance/binance-skills-hub --skill binance-leaderboard
  1. 01

    Install

    Run the command in your project. The CLI asks which agents to install for.

  2. 02

    Describe the task

    Ask your agent for something this skill covers. It loads binance-leaderboard automatically when relevant.

  3. 03

    Review the result

    The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

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:

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 intentCommand
Query top traders by PnL/win rate/volumebaw 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 conditionsbaw leaderboard preset save/list
Save/load Gem Hunter configsbaw leaderboard alpha-radar-config save/list

Supported Chains

ChainchainId
BSC56
SolanaCT_501
Base8453
Ethereum1

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

# 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

# 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

# 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

# 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 or 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 valueUser-facing termNotes
archetype: sniperSniper
archetype: swingSwing Trader
archetype: accumulatorAccumulator
archetype: farmerFarmer
archetype: mixedMixed
sort-by: 0Sort by PnL
sort-by: 20Sort by Win Rate
sort-by: 30Sort by Total Volume
sort-by: 50Sort by Trade Count
sort-by: 60Sort by Recent Activity
sort-by: 70Sort by Profit Rate
sort-by: 80Sort by Token Count
address (in display)omitDon't show raw address unless user asks; use {addressLabel}
finalScoreScoreDon'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.

CodeInternal NameUser-Facing Message
70001001TRACKER_API_ERRORQuery failed, please try again later
70004001TRACKER_LEADERBOARD_EMPTYLeaderboard data is empty
70004002TRACKER_ADDRESS_NOT_RANKEDAddress 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

FAQ

What does the binance-leaderboard skill do?

On-chain wallet leaderboard and Gem Hunter analysis on Binance Web3. Query top trader rankings by PnL, win rate, volume, trade count, or token count across BSC/Solana/Base/ETH. Analyze individual wallet addresses with a 6-dimension scoring model (winrate/stability/drawdown/ tags/pnl/followfriendly) plus AI archetype overlay. Query Gem Hunter to find wallets holding specific tokens. Save and load preset filter conditions and Gem Hunter configs. Trigger whenever the user mentions leaderboard, top traders, wallet analysis, Address Analysis, AddressScore, Gem Hunter, or wants to evaluate a wallet's trading quality — even if they don't say "leaderboard" explicitly.

How do I install binance-leaderboard?

Run npx skills add https://github.com/binance/binance-skills-hub --skill binance-leaderboard in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/binance-leaderboard/ (personal) or .claude/skills/binance-leaderboard/ (project).

Which AI agents can use binance-leaderboard?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is binance-leaderboard safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains binance-leaderboard?

It is published by binance in binance/binance-skills-hub. It has 2,027 recorded installs.

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