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#1	---
#2	name: dex-screener-scanner
#3	description: "Automate DexScreener Solana token discovery and screening via browser automation. Navigate dexscreener.com/solana, scrape real-time token listings, filter by volume/liquidity/age/holders, and identify the best opportunities. Triggers: scan dexscreener, find new tokens, find trending tokens, screen Solana tokens, best tokens on Solana, dexscreener scanner."
#4	---
#5
#6	# DexScreener Solana Scanner
#7
#8	Autonomous agent skill for scanning and screening Solana tokens on DexScreener using browser automation (Playwright/Puppeteer). The goal is to navigate `dexscreener.com/solana`, extract token data from live listings, apply screening filters, and identify the best trading opportunities.
#9
#10	## Overview
#11
#12	This skill uses browser automation to:
#13
#14	1. Navigate to DexScreener's Solana board
#15	2. Scrape live token listings (new, trending, gainers, etc.)
#16	3. Extract key metrics for each token
#17	4. Apply configurable screening filters to find the best opportunities
#18	5. Return ranked results with rationale
#19
#20	## Workflow
#21
#22	### Step 1: Launch Browser & Navigate
#23
#24	Use a browser automation tool (Playwright or Puppeteer) to open `https://dexscreener.com/solana`.
#25
#26	```python
#27	# Example with Playwright
#28	page = await browser.new_page()
#29	await page.goto("https://dexscreener.com/solana", wait_until="domcontentloaded")
#30	```
#31
#32	Important: DexScreener is a heavy React SPA. Wait for the token table rows to render before scraping. The table typically appears within 3-8 seconds.
#33
#34	### Step 2: Wait for Token Table to Load
#35
#36	The token listing is a dynamic table. Watch for:
#37
#38	- CSS selector for table rows: `div[data-group="token-row"]` or `.ds-table-row` or `tr[data-id]`
#39	- The table has multiple tabs: New, Trending, Gainers, Recently Copied, etc.
#40	- Wait for at least one row to be visible before scraping
#41
#42	Recommended wait strategy:
#43
#44	```python
#45	# Wait for first token row to appear
#46	await page.wait_for_selector('[data-group="token-row"]', timeout=15000)
#47	# Or wait for the table body
#48	await page.wait_for_selector('table tbody tr', timeout=15000)
#49	```
#50
#51	If the wait times out, the page may be loading slowly — retry once with a longer timeout.
#52
#53	### Step 3: Parse Token Listing Data
#54
#55	Each token row typically contains these fields (column order may vary slightly):
#56
#57	\| Field \| Description \| Example \|
#58	\|-------\|-------------\|---------\|
#59	\| # \| Rank/position \| 1, 2, 3 \|
#60	\| Name/Symbol \| Token name + ticker \| "dogwifhat / WIF" \|
#61	\| Price \| Current SOL or USD price \| "$0.0042" \|
#62	\| Price Change \| 5m/1h/6h/24h change % \| "+15.3%" \|
#63	\| Volume \| 24h trading volume \| "$1.2M" \|
#64	\| Liquidity \| Pool liquidity \| "$45K" \|
#65	\| Market Cap \| Fully diluted market cap \| "$2.1M" \|
#66	\| Age \| How long since creation \| "3m" (3 minutes), "1h" \|
#67	\| Txns \| Transaction count (buys/sells) \| "1.2K / 800" \|
#68	\| Holders \| Unique holder count \| "450" \|
#69	\| FDV / Liq \| FDV-to-Liquidity ratio \| "12.5x" \|
#70
#71	Parsing approach:
#72
#73	```python
#74	rows = await page.query_selector_all('[data-group="token-row"]')
#75	tokens = []
#76	for row in rows:
#77	cells = await row.query_selector_all('td')
#78	# Extract based on cell index
#79	tokens.append({
#80	"rank": await cells[0].inner_text(),
#81	"name": await cells[1].inner_text(),
#82	"price": await cells[2].inner_text(),
#83	"change_5m": await cells[3].inner_text(),
#84	"volume": await cells[4].inner_text(),
#85	"liquidity": await cells[5].inner_text(),
#86	"market_cap": await cells[6].inner_text(),
#87	"age": await cells[7].inner_text(),
#88	"txns": await cells[8].inner_text(),
#89	"holders": await cells[9].inner_text(),
#90	# ... etc
#91	})
#92	```
#93
#94	Note: Cell indices may shift if DexScreener updates their layout. Always inspect the actual DOM first. Use `page.evaluate()` for more robust scraping if query selectors are unreliable.
#95
#96	### Step 4: Switch Between Listing Tabs
#97
#98	DexScreener has multiple tabs that reveal different token sets. Click these to scan more broadly:
#99
#100	```python
#101	# Available tabs (selectors may vary):
#102	# New: Most recently created pairs
#103	await page.click('button:has-text("New")')
#104	# Trending: Hottest tokens right now
#105	await page.click('button:has-text("Trending")')
#106	# Gainers: Top gainers
#107	await page.click('button:has-text("Gainers")')
#108	# Recently Copied: Tokens being copied from other chains
#109	await page.click('button:has-text("Recently Copied")')
#110	```
#111
#112	Wait 2-3 seconds after clicking a tab for the table to re-render.
#113
#114	### Step 5: Scroll to Load More Tokens
#115
#116	DexScreener loads tokens in batches (about 25 per page). Scroll down to trigger lazy loading:
#117
#118	```python
#119	# Scroll to bottom of table to load more
#120	for _ in range(3):
#121	await page.evaluate('window.scrollBy(0, 800)')
#122	await page.wait_for_timeout(1500)
#123	```
#124
#125	Be respectful: Don't hammer the page. 2-3 scrolls is enough to get a meaningful sample (~75-100 tokens per tab).
#126
#127	### Step 6: Apply Screening Filters
#128
#129	After collecting raw token data, apply filters to find the "best" tokens. These are the recommended default thresholds:
#130
#131	\| Criterion \| Recommended Threshold \| Why \|
#132	\|-----------\|----------------------\|-----\|
#133	\| Min Liquidity \| ≥ $10,000 \| Below this = high slippage, rug risk \|
#134	\| Min Volume \| ≥ $50,000 (24h) \| Shows organic interest \|
#135	\| Max Age \| ≤ 48 hours \| Catches new launches \|
#136	\| Min Holders \| ≥ 50 unique \| Indicates distribution, not a single dev wallet \|
#137	\| Max Holder Concentration \| Top 10 holders < 20% \| Prevents whale manipulation \|
#138	\| Min Price Change (5m) \| ≥ 5% (for momentum) \| Shows buying pressure \|
#139	\| FDV / Liquidity Ratio \| < 50x \| Lower = less overvalued relative to available liquidity \|
#140	\| Min Txns \| ≥ 100 transactions \| Shows real activity \|
#141
#142	Screening function example:
#143
#144	```python
#145	def screen_tokens(tokens, config=None):
#146	defaults = {
#147	"min_liquidity": 10_000,
#148	"min_volume": 50_000,
#149	"max_age_hours": 48,
#150	"min_holders": 50,
#151	"min_txns": 100,
#152	}
#153	config = {defaults, (config or {})}
#154
#155	passed = []
#156	for t in tokens:
#157	reasons = []
#158	if t.get("liquidity_usd", 0) >= config["min_liquidity"]:
#159	reasons.append(f"liquidity={t['liquidity_usd']}")
#160	if t.get("volume_24h", 0) >= config["min_volume"]:
#161	reasons.append(f"volume={t['volume_24h']}")
#162	if t.get("age_hours", 999) <= config["max_age_hours"]:
#163	reasons.append(f"age={t['age_hours']}h")
#164	if t.get("holders", 0) >= config["min_holders"]:
#165	reasons.append(f"holders={t['holders']}")
#166	if t.get("txns", 0) >= config["min_txns"]:
#167	reasons.append(f"txns={t['txns']}")
#168	if reasons:
#169	passed.append((t, reasons))
#170
#171	# Sort by volume (descending) - highest volume = most liquid interest
#172	passed.sort(key=lambda x: x[0].get("volume_24h", 0), reverse=True)
#173	return passed
#174	```
#175
#176	### Step 7: Click Through for Token Details
#177
#178	For tokens that pass screening, click through to the pair page for deeper analysis:
#179
#180	```python
#181	# Click on a token row to navigate to its pair page
#182	pair_link = await row.query_selector('a[href*="/solana/"]')
#183	pair_url = await pair_link.get_attribute('href')
#184	await page.goto(f"https://dexscreener.com{pair_url}")
#185	```
#186
#187	On the pair page you can extract:
#188	- Full holder distribution (top holders %)
#189	- Price chart context (support/resistance levels)
#190	- Social links (Twitter, Telegram, website)
#191	- Token contract address (for further analysis)
#192	- Creator wallet (check if it's a known rug deployer)
#193
#194	Safety check on pair page:
#195
#196	```python
#197	# Check if the token CA has been rug-checked
#198	# Look for "Rug Pull" or scam warnings in the UI
#199	risk_warning = await page.query_selector('[class="risk"], [class="warning"], [class*="scam"]')
#200	if risk_warning:
#201	warning_text = await risk_warning.inner_text()
#202	# Flag this token as potentially dangerous
#203	```
#204
#205	## Token Ranking System
#206
#207	Rank screened tokens using a scoring system:
#208
#209	```python
#210	def score_token(t):
#211	score = 0
#212	score += min(t.get("volume_24h", 0) / 100_000, 10) # up to 10 pts for volume
#213	score += min(t.get("liquidity_usd", 0) / 50_000, 10) # up to 10 pts for liquidity
#214	score += 5 if t.get("age_hours", 999) < 1 else 0 # bonus for brand new
#215	score += 3 if t.get("change_5m", 0) > 10 else 0 # bonus for strong momentum
#216	score += 2 if t.get("holders", 0) > 200 else 0 # bonus for wide distribution
#217	score -= 5 if t.get("fdv_liquidity_ratio", 0) > 100 else 0 # penalty for high FDV/liq
#218	return score
#219	```
#220
#221	Return results sorted by score descending, with a brief rationale for each pick.
#222
#223	## Tab Strategy
#224
#225	Scan tabs in this priority order for best results:
#226
#227	1. Trending — Hottest tokens, highest chance of continuation
#228	2. New — Recently created, potential early entries
#229	3. Gainers — Strong momentum plays
#230	4. Recently Copied — Arbitrage opportunities from other chains
#231
#232	For a comprehensive scan, scrape all 4 tabs and deduplicate by contract address.
#233
#234	## Edge Cases & Handling
#235
#236	\| Situation \| Handling \|
#237	\|-----------\|----------\|
#238	\| Cloudflare/rate limiting \| Add random delays (1-3s) between actions. If blocked, rotate user-agent \|
#239	\| No tokens pass filters \| Report honestly: "No tokens meet the criteria. Consider loosening thresholds" \|
#240	\| Table fails to load \| Retry with page refresh. If persistent, report error and suggest checking if dexscreener.com is accessible \|
#241	\| Dynamic class names \| Use stable data attributes (`[data-group]`) or text matchers instead of CSS classes \|
#242	\| Very new tokens (< 1 min) \| May not have full data. These are high-risk; flag them as "extremely early" \|
#243	\| Duplicate tokens across tabs \| Deduplicate by contract address to avoid double-counting \|
#244
#245	## Output Format
#246
#247	Present findings in a structured format:
#248
#249	```
#250	## DexScreener Scan Results
#251
#252	### Top Picks (Passed Screening)
#253	1. $TOKEN — Score: 18/20
#254	- Price: $0.0042 \| Vol: $1.2M \| Liq: $45K \| Age: 3m \| Holders: 150
#255	- Rationale: Brand new, strong volume, good liquidity, wide holder distribution
#256	- CA: [contract_address]
#257
#258	2. $TOKEN2 — Score: 14/20
#259	- ...
#260
#261	### Tokens Scanned
#262	- Trending tab: 25 tokens
#263	- New tab: 25 tokens
#264	- Total unique: 48 tokens
#265	- Passed screening: 2 tokens
#266
#267	### Market Notes
#268	- Average liquidity across scanned tokens: $12K
#269	- Heaviest volume: $TOKEN ($1.2M)
#270	- Oldest token in top 25: 6h ago
#271	```
#272
#273	## Scripts
#274
#275	### `scripts/scan_dexscreener.py`
#276	Main scanning script that orchestrates: open browser → navigate → scrape → filter → rank → output.
#277
#278	### `scripts/screen_tokens.py`
#279	Standalone screening/filtering logic that can be used independently on cached data. Accepts configurable thresholds via stdin or arguments.
#280
#281	## References
#282
#283	### `references/dexscreener_layout.md`
#284	Current DOM structure and selectors for dexscreener.com/solana. Update this when DexScreener changes their layout. Always inspect the page before scraping to verify selectors are still valid.
#285

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