onchain-alpha-radar

name

onchain-alpha-radar

description

On-chain alpha discovery and research pipeline. Chains together Token Discovery, Holdings Analysis, Smart Money Tracking, and Research Report generation using on-chain data, Twitter intelligence, deep research methodology, and Excalidraw visualizations. Use when the user wants to: research a token, find alpha, track smart money, analyze on-chain holdings, generate a crypto research report, discover trending tokens, or investigate whale/KOL activity. Trigger keywords: "alpha", "research token", "smart money", "whale tracking", "on-chain research", "链上研究", "Token分析", "持仓分析", "研报", "Smart Money", "聪明钱", "巨鲸追踪", "meme coin analysis", "token discovery"

OnChain Alpha Radar

A systematic on-chain data research pipeline that transforms raw blockchain signals into actionable research reports.

Pipeline Overview

Token Discovery → Holdings Analysis → Smart Money Tracking → Research Report
     ↓                  ↓                    ↓                    ↓
  onchainos          onchainos            onchainos          deep-research
  + Twitter          + Twitter            + Twitter          + excalidraw

Tool Dependencies

This skill orchestrates four external tool sources:

Tool	Purpose	Type
onchainos (OKX OnchainOS)	On-chain data: token search, prices, holders, smart money signals, meme pump, portfolio, swaps	CLI binary (onchainos)
opentwitter-mcp (6551Team)	Twitter/X intelligence: search tweets, KOL followers, deleted tweets, user profiles	MCP server (stdio)
deep-research (wshuyi)	Structured research methodology: 8-step workflow with fact cards and verification	Claude Code Skill
excalidraw-diagram (coleam00)	Visual diagrams: architecture, flow, comparison diagrams in .excalidraw format	Claude Code Skill

Before starting, verify tool availability:

1. Run which onchainos — if missing, install via curl -sSL https://raw.githubusercontent.com/okx/onchainos-skills/main/install.sh | sh
2. Check if Twitter MCP tools are available (e.g., search_twitter)
3. Check if excalidraw diagram skill is present at .claude/skills/excalidraw-diagram/

If any tool is unavailable, inform the user and proceed with what IS available. The pipeline degrades gracefully — each phase can work independently.

---

Phase 1: Token Discovery

Goal: Identify high-potential tokens through on-chain signals and social buzz.

Step 1.1: On-Chain Signal Scan

Choose the appropriate discovery method based on user intent:

A. Trending Token Scan (broad discovery):

# Top tokens by volume in last 24h
onchainos token trending --chains <target-chains> --sort-by 5 --time-frame 4

# Top tokens by price change in last 1h (momentum)
onchainos token trending --chains <target-chains> --sort-by 2 --time-frame 2

B. Meme Token Discovery (degen alpha):

# New launches on Solana (pumpfun)
onchainos market memepump-tokens 501 --stage NEW

# Recently migrated (survived bonding curve)
onchainos market memepump-tokens 501 --stage MIGRATED

C. Specific Token Lookup (user provides name/address):

onchainos token search <query>
onchainos token price-info <address> --chain <chain>

Step 1.2: Social Signal Cross-Reference

For each interesting token found in Step 1.1, gather Twitter sentiment:

# Search for token mentions
search_twitter(keywords="$TOKEN_SYMBOL OR $TOKEN_NAME", min_likes=10, limit=20)

# Advanced search with time filter
search_twitter_advanced(keywords="$TOKEN_SYMBOL", min_likes=50, since_date="YYYY-MM-DD", product="Top")

Step 1.3: Discovery Scoring

Rate each discovered token on a 1-5 scale across:

Dimension	Data Source	Weight
On-chain momentum	Price change, volume, tx count	30%
Social buzz	Tweet count, avg likes, KOL mentions	20%
Liquidity depth	Liquidity USD, market cap	20%
Community recognition	communityRecognized flag, holder count	15%
Risk flags	Honeypot check, rug pull history, bundle %	15%

Output a ranked shortlist of 3-10 tokens for deep analysis.

---

Phase 2: Holdings Analysis

Goal: Deep-dive into token economics and holder distribution.

Step 2.1: Token Fundamentals

For each shortlisted token:

# Full price and market data
onchainos token price-info <address> --chain <chain>

# Holder distribution (top 20)
onchainos token holders <address> --chain <chain>

# K-line data for price trend
onchainos market kline <address> --chain <chain> --bar 1H

Step 2.2: Meme Token Due Diligence (if applicable)

# Developer reputation
onchainos market memepump-token-dev-info <address> --chain <chain>

# Bundle/sniper detection
onchainos market memepump-token-bundle-info <address> --chain <chain>

# Audit details
onchainos market memepump-token-details <address> --chain <chain>

# Other tokens by same dev
onchainos market memepump-similar-tokens <address> --chain <chain>

Step 2.3: Key Wallet Holdings

For significant holder addresses discovered in Step 2.1:

# What else does this whale hold?
onchainos portfolio all-balances --address <whale-address> --chains 1,56,501,8453

# Total portfolio value
onchainos portfolio total-value --address <whale-address> --chains 1,56,501,8453

Step 2.4: Twitter Profile of Key Holders (if identifiable)

# If wallet is linked to a known Twitter account
get_twitter_user(username="whale_account")
get_twitter_user_tweets(username="whale_account", limit=10)
get_twitter_kol_followers(username="whale_account")

Step 2.5: Holdings Summary

Produce a structured analysis:

## Holdings Analysis: $TOKEN

### Token Economics
- Market Cap: $X | FDV: $Y
- Liquidity: $Z | Liquidity/MCap: N%
- Holders: N | Top 10 Concentration: X%

### Holder Distribution
| Rank | Address (short) | % Supply | Wallet Type |
|------|----------------|----------|-------------|

### Risk Assessment
- Dev Holdings: X%
- Bundle/Sniper %: X%
- Insider %: X%
- Rug Pull History: X/Y tokens

### Price Trend (24h)
- 5min: +X% | 1h: +X% | 4h: +X% | 24h: +X%
- Volume 24h: $X | Txs 24h: N

---

Phase 3: Smart Money Tracking

Goal: Identify what smart money, whales, and KOLs are buying/selling.

Step 3.1: Signal Collection

# Smart money buy signals (type 1)
onchainos market signal-list <chain> --wallet-type 1 --min-amount-usd 10000

# Whale signals (type 3)
onchainos market signal-list <chain> --wallet-type 3 --min-amount-usd 50000

# KOL/Influencer signals (type 2)
onchainos market signal-list <chain> --wallet-type 2

For a specific token:

onchainos market signal-list <chain> --token-address <address> --wallet-type 1,2,3

Step 3.2: Wallet Deep Dive

For each smart money wallet address:

# Full portfolio
onchainos portfolio all-balances --address <sm-wallet> --chains 1,56,501,8453

# Recent trades on the token
onchainos market trades <token-address> --chain <chain>

Step 3.3: Aped Wallet Network (Meme Tokens)

# Who else is holding what smart money holds?
onchainos market memepump-aped-wallet <token-address> --chain <chain>

Step 3.4: Social Intelligence on Smart Money

# Search for wallet address mentions
search_twitter(keywords="<wallet-address-short>", limit=10)

# Search for token + smart money narrative
search_twitter_advanced(keywords="$TOKEN smart money OR whale", min_likes=20, product="Top")

# Check deleted tweets (alpha leak detection)
get_twitter_deleted_tweets(username="suspected_kol", limit=20)

Step 3.5: Smart Money Flow Map

Synthesize findings into:

## Smart Money Flow: $TOKEN

### Signal Summary
| Wallet Type | # Wallets | Total USD | Avg Sold % |
|-------------|-----------|-----------|------------|
| Smart Money | N | $X | Y% |
| Whale | N | $X | Y% |
| KOL | N | $X | Y% |

### Key Wallets
| Address | Type | Amount | Still Holding? | PnL |
|---------|------|--------|----------------|-----|

### Conviction Score
- Smart money buying + low sold ratio = HIGH conviction
- Mixed signals = MEDIUM conviction
- Smart money selling = LOW conviction

---

Phase 4: Research Report Output

Goal: Synthesize all findings into a professional research report with visualizations.

Step 4.1: Data Consolidation

Aggregate all data from Phases 1-3 into structured sections:

1. Executive Summary — 1-paragraph verdict with conviction rating
2. Token Overview — fundamentals, economics, price action
3. On-Chain Analysis — holder distribution, whale activity, liquidity depth
4. Smart Money Activity — signal analysis, wallet tracking, conviction assessment
5. Social Sentiment — Twitter buzz, KOL mentions, community growth
6. Risk Assessment — rug pull flags, bundle detection, dev history, honeypot check
7. Conclusion & Recommendation — actionable insights with risk/reward framing

Step 4.2: Generate Excalidraw Diagrams

Create visual diagrams to accompany the report. Use the excalidraw-diagram skill to generate:

Diagram 1: Token Flow Architecture

• Show token movement between key wallets (dev, top holders, smart money, DEX liquidity)
• Use fan-out pattern for distribution, convergence for accumulation

Diagram 2: Smart Money Signal Timeline

• Timeline pattern showing when smart money entered/exited
• Overlay with price chart key levels

Diagram 3: Risk Assessment Matrix

• Side-by-side comparison of risk factors
• Color-coded: green (safe), yellow (caution), red (danger)

Diagram 4: Holder Distribution

• Visual representation of token concentration
• Highlight dev/insider/bundler portions

Step 4.3: Report Generation

Apply the deep-research methodology for rigorous analysis:

• Source Tiering: On-chain data = L1 (primary source), Twitter data = L3-L4 (supporting evidence)
• Fact Cards: Each claim links to specific on-chain transaction or data point
• Confidence Levels: Mark each conclusion with HIGH/MEDIUM/LOW confidence
• Time Sensitivity: Crypto data has extremely short shelf life — flag data staleness

Step 4.4: Report Format

Output as a Markdown file with this structure:

# Alpha Radar Report: $TOKEN_SYMBOL ($TOKEN_NAME)

> **Date**: YYYY-MM-DD | **Chain**: X | **Analyst**: OnChain Alpha Radar
> **Conviction**: HIGH/MEDIUM/LOW | **Risk Level**: 1-5

## TL;DR
[One-sentence verdict]

## 1. Token Overview
[From Phase 1 + 2 data]

## 2. On-Chain Deep Dive
[From Phase 2 data, including holder analysis]

## 3. Smart Money Activity
[From Phase 3 data]

## 4. Social Sentiment
[Twitter data aggregation]

## 5. Risk Assessment
[Comprehensive risk table]

## 6. Visual Analysis
[Reference generated Excalidraw diagrams]

## 7. Conclusion
[Actionable recommendation with explicit risk/reward]

---
*Data sources: OKX OnchainOS API, Twitter/X, On-chain transactions*
*This is not financial advice. DYOR.*

---

Execution Modes

The user can trigger the full pipeline or individual phases:

Full Pipeline

"Research $TOKEN for me" / "Full alpha report on $TOKEN" / "给我做一份 $TOKEN 的研报"

Run all 4 phases sequentially, generating a complete report.

Individual Phases

"What's trending on Solana?" → Phase 1 only "Analyze holders of $TOKEN" → Phase 2 only "Track smart money on $TOKEN" → Phase 3 only "Generate a report from my findings" → Phase 4 only

Quick Scan Mode

"Quick scan $TOKEN" / "快速扫描 $TOKEN"

Abbreviated pipeline:

1. Token price-info + basic fundamentals
2. Top 5 holders check
3. Smart money signal check (if any)
4. 1-paragraph summary (no diagrams)

---

Chain Reference

Common chain indices for onchainos commands:

Chain	Index	Common Tokens
Ethereum	1	ETH, ERC-20
BSC	56	BNB, BEP-20
Solana	501	SOL, SPL
Base	8453	ETH (Base)
Arbitrum	42161	ETH (Arb)
Polygon	137	MATIC
Avalanche	43114	AVAX
XLayer	196	OKB

Default to Solana (501) and Ethereum (1) if user doesn't specify a chain.

---

Error Handling

• If onchainos returns empty data: try alternate chains or broaden search parameters
• If Twitter search returns nothing: try shorter/simpler keywords, remove filters
• If a token address isn't found: use onchainos token search by symbol first, then retry with the discovered address
• If rate limited: wait 5 seconds and retry once, then inform user
• Always inform the user which data was successfully collected and which was unavailable