Dexscreener API Skill for Claude

by agiprolabs

Free, no-auth multi-chain DEX pair data — prices, volume, liquidity, transactions, and token profiles

9k tokens

context cost

the whole folder, loaded on every use

files

ships runnable scripts

copies elsewhere

how many repositories repackaged it

257

stars on the repo

on the repository, not the skill itself

Install

one command, takes just this skill from the repository

npx skills add https://github.com/agiprolabs/claude-trading-skills --skill dexscreener-api

The instruction itself

20 sections, as written by the author

DexScreener API — Free Multi-Chain DEX Data

DexScreener provides DEX pair data across 80+ chains with no API key required. Prices, volume, liquidity, transaction counts, and token profiles — all free. Best for quick lookups, cross-chain comparison, and lightweight monitoring.

Quick Start

No authentication needed. Just make requests:

import httpx

# Search for a token
resp = httpx.get("https://api.dexscreener.com/latest/dex/search", params={"q": "BONK"})
pairs = resp.json()["pairs"]
for p in pairs[:3]:
    print(f"{p['baseToken']['symbol']}/{p['quoteToken']['symbol']} on {p['chainId']}: ${p['priceUsd']}")

# Or with curl
curl "https://api.dexscreener.com/latest/dex/search?q=BONK"

Base URL

https://api.dexscreener.com

No API key, no headers required. Rate limits: ~300 req/min for DEX data endpoints, 60 req/min for profile/boost endpoints.

Core Endpoints

Search Pairs

# Search by token name, symbol, or address (returns ~30 pairs across all chains)
GET /latest/dex/search?q=BONK
GET /latest/dex/search?q=So11111111111111111111111111111111111111112

Get Pairs by Chain + Pair Address

# Single pair
GET /latest/dex/pairs/solana/PAIR_ADDRESS

# Multiple pairs (comma-separated)
GET /latest/dex/pairs/solana/PAIR1,PAIR2,PAIR3

Get Pairs by Token Address

# All pairs containing this token across all chains
GET /latest/dex/tokens/TOKEN_MINT_ADDRESS

# Chain-specific (v1)
GET /token-pairs/v1/solana/TOKEN_MINT

# Multiple tokens on one chain (v1)
GET /tokens/v1/solana/TOKEN1,TOKEN2,TOKEN3

Token Profiles & Boosts

# Latest token profiles (60 req/min)
GET /token-profiles/latest/v1

# Latest boosted tokens
GET /token-boosts/latest/v1

# Top boosted tokens (sorted by total boost amount)
GET /token-boosts/top/v1

# Token orders (ads, profile claims)
GET /orders/v1/solana/TOKEN_ADDRESS

# Community takeovers
GET /community-takeovers/latest/v1

Pair Response Schema

{
  "chainId": "solana",
  "dexId": "raydium",
  "url": "https://dexscreener.com/solana/PAIR_ADDR",
  "pairAddress": "3nMFwZX...",
  "labels": ["CLMM"],
  "baseToken": { "address": "So11...", "name": "Wrapped SOL", "symbol": "SOL" },
  "quoteToken": { "address": "EPjF...", "name": "USD Coin", "symbol": "USDC" },
  "priceNative": "86.08",
  "priceUsd": "86.08",
  "txns": {
    "m5":  { "buys": 25, "sells": 18 },
    "h1":  { "buys": 916, "sells": 1282 },
    "h6":  { "buys": 11309, "sells": 12661 },
    "h24": { "buys": 27974, "sells": 31475 }
  },
  "volume": { "m5": 41680, "h1": 6773038, "h6": 71628073, "h24": 167164493 },
  "priceChange": { "m5": -0.01, "h1": -0.36, "h6": 0.82, "h24": 0.25 },
  "liquidity": { "usd": 28168478, "base": 232348, "quote": 8167805 },
  "fdv": 8171740,
  "marketCap": 8171740,
  "pairCreatedAt": 1688106058000,
  "info": {
    "imageUrl": "https://cdn.dexscreener.com/...",
    "websites": [{ "url": "https://...", "label": "Website" }],
    "socials": [{ "url": "https://x.com/...", "type": "twitter" }]
  }
}

Key notes:

priceNative and priceUsd are strings (preserves precision)
pairCreatedAt is milliseconds (divide by 1000 for unix timestamp)
labels: "CLMM" (concentrated), "DLMM" (dynamic), "wp" (whirlpool)
info is optional — only present if the token profile has been claimed
fdv/marketCap may be absent for tokens without supply data

Common Patterns

Quick Token Lookup

def lookup_token(address: str) -> dict | None:
    """Get the best pair for a token by liquidity."""
    resp = httpx.get(f"https://api.dexscreener.com/latest/dex/tokens/{address}")
    pairs = resp.json().get("pairs", [])
    if not pairs:
        return None
    # Sort by liquidity (highest first)
    pairs.sort(key=lambda p: p.get("liquidity", {}).get("usd", 0), reverse=True)
    return pairs[0]

Cross-Chain Price Check

def find_best_price(symbol: str) -> list[dict]:
    """Find a token across all chains and compare prices."""
    resp = httpx.get("https://api.dexscreener.com/latest/dex/search", params={"q": symbol})
    pairs = resp.json().get("pairs", [])
    results = []
    for p in pairs:
        if p["baseToken"]["symbol"].upper() == symbol.upper():
            results.append({
                "chain": p["chainId"],
                "dex": p["dexId"],
                "price": float(p.get("priceUsd", 0)),
                "liquidity": p.get("liquidity", {}).get("usd", 0),
                "volume_24h": p.get("volume", {}).get("h24", 0),
            })
    return sorted(results, key=lambda x: x["liquidity"], reverse=True)

Monitor New Tokens via Boosts

def get_newly_boosted() -> list[dict]:
    """Get tokens that were recently boosted (paid promotion)."""
    resp = httpx.get("https://api.dexscreener.com/token-boosts/latest/v1")
    return [
        {
            "chain": t["chainId"],
            "address": t["tokenAddress"],
            "boost_amount": t.get("amount", 0),
            "total_boost": t.get("totalAmount", 0),
            "description": t.get("description", ""),
        }
        for t in resp.json()
    ]

Buy/Sell Ratio Analysis

def analyze_pressure(pair: dict) -> dict:
    """Analyze buy/sell pressure from transaction counts."""
    txns = pair.get("txns", {})
    result = {}
    for tf in ["m5", "h1", "h6", "h24"]:
        data = txns.get(tf, {})
        buys = data.get("buys", 0)
        sells = data.get("sells", 0)
        total = buys + sells
        result[tf] = {
            "buys": buys, "sells": sells, "total": total,
            "buy_ratio": buys / total if total > 0 else 0.5,
        }
    return result

Supported Chains

Major chains: solana, ethereum, base, arbitrum, bsc, polygon, avalanche, optimism, zksync, scroll, linea, tron, near, aptos, ton, sui, hyperliquid

80+ chains total. The chainId matches the URL path on dexscreener.com.

Limitations

No OHLCV/candle data — Use Birdeye or SolanaTracker for historical candles
No pagination — Search returns ~30 results max
No wallet/trader data — Use Birdeye or Helius for wallet analysis
No token security data — Use Birdeye token_security endpoint
Snapshot data only — Current state, not historical time series
No WebSocket — Polling only

When to Use DexScreener vs Alternatives

| Need | Use |

|------|-----|

| Quick free token lookup | DexScreener |

| Cross-chain comparison | DexScreener |

| Historical OHLCV for backtesting | Birdeye or SolanaTracker |

| Wallet/trader analysis | Helius or SolanaTracker |

| Real-time streaming | Yellowstone gRPC or Birdeye WebSocket |

| Token security check | Birdeye or SolanaTracker risk score |

Files

References

references/endpoints.md — Complete endpoint listing with response schemas
references/error_handling.md — Rate limits, error codes, best practices

Scripts

scripts/token_lookup.py — Look up any token across all chains with liquidity analysis
scripts/boost_monitor.py — Monitor newly boosted/promoted tokens

Other skills for the same job

different authors, same section of the catalogue

MCP Builder

by anthropics

vendor ×13

Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).

Automatically creates user-facing changelogs from git commits by analyzing commit history, categorizing changes, and transforming technical commits into clear, customer-friendly release notes. Turns hours of manual changelog writing into minutes of automated generation.

774 tokens

Finishing A Development Branch

by ZhanlinCui

×7

Use when implementation is complete, all tests pass, and you need to decide how to integrate the work - guides completion of development work by presenting structured options for merge, PR, or cleanup

Vercel React Native Skills

by vercel-labs

vendor ×6

React Native and Expo best practices for building performant mobile apps. Use when building React Native components, optimizing list performance, implementing animations, or working with native modules. Triggers on tasks involving React Native, Expo, mobile performance, or native platform APIs.

39k tokens

Vercel React Best Practices

by ratacat

×5

React and Next.js performance optimization guidelines from Vercel Engineering. This skill should be used when writing, reviewing, or refactoring React/Next.js code to ensure optimal performance patterns. Triggers on tasks involving React components, Next.js pages, data fetching, bundle optimization, or performance improvements.

Next.js best practices - file conventions, RSC boundaries, data patterns, async APIs, metadata, error handling, route handlers, image/font optimization, bundling

Use when starting feature work that needs isolation from current workspace or before executing implementation plans - creates isolated git worktrees with smart directory selection and safety verification

1k tokens

How to use it

Copy the folder

Take agiprolabs/dexscreener-api from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.