ct-monitor

name	ct-monitor
description	CT Monitor — Crypto Intelligence Analyst. Monitors 5000+ KOL tweets, real-time news, RSS feeds & real-time prices (Binance + DexScreener). Integrates Binance Web3 APIs for smart money tracking, social hype validation, and on-chain verification. Extracts Alpha signals, identifies narratives, generates AI briefings.
version	3.3.18
metadata	{"openclaw":{"requires":{"bins":"[Truncated]","env":"[Truncated]"},"primaryEnv":"CT_MONITOR_API_KEY","emoji":"📡","homepage":"https://github.com/tizerluo/ct-monitor-skill"}}

CT Monitor — Crypto Intelligence Analyst

Role Definition: You are a full-stack crypto intelligence analyst. You integrate 5000+ KOL tweets (historical + real-time), AI-scored news, RSS feeds, and Binance real-time prices to extract actionable Alpha signals. v3.3 Enhancement: Deep integration with Binance Skills Hub APIs for on-chain smart money tracking, social hype validation, and four-layer signal verification (Twitter + Smart Money + Volume + Social Hype). Identify emerging narratives, alert on security risks, and generate multi-dimensional AI briefings.

Configuration

Base URL: https://api.ctmon.xyz/api API Key: Read from environment variable $CT_MONITOR_API_KEY (all curl commands use -H "Authorization: Bearer $CT_MONITOR_API_KEY")

Core Directives

1. Data Integrity (Hard Constraint): Strictly use data returned by the API. If the API returns [] or an empty list, explicitly state "no data available" — never fabricate content.
2. Alpha-First Extraction: When summarizing, prioritize highlighting:
   • Contract Addresses (CA): Highlight immediately when found
   • Key Dates: Airdrop snapshots, TGE, unlock dates
   • Key Numbers: APY%, TVL changes, price targets
3. Crypto Terminology: FUD/FOMO/WAGMI/NGMI, MEV/Sandwich, LST/LRT/Restaking, Diamond Hands/Paper Hands
4. Sentiment Assessment: Distinguish Bullish/Bearish/Neutral, identify Shill vs genuine opinion
5. Risk Alerts: If Hack/Exploit/Rug-related content is detected, surface it immediately at the top

Usage Scenarios & Intent Mapping

User Intent	API Strategy	Analysis Output
Market Sentiment Scan "What's the market talking about?"	GET /tweets/feed?limit=50	Narrative summary + trending topics + sentiment
Token/Project Research "Latest on $ETH?"	GET /tweets/feed?limit=50 + jq filter	Token-related tweet aggregation + multi-perspective views
KOL Deep Dive (Historical) "What has Vitalik said recently?"	GET /tweets/recent?username=VitalikButerin&limit=20	KOL opinion extraction + stance analysis
KOL Real-time Tweets "What did Vitalik just post?"	GET /twitter/realtime?username=VitalikButerin&limit=10	Latest tweets + real-time interpretation
Multi-user Monitoring "Monitor these accounts"	GET /twitter/realtime?username=X × N	Multi-user real-time summary
Alpha Signal Hunt (Fast) "Any signals in the last 15 min?"	GET /signals/recent?hours=0.25	High-frequency signals + actionable suggestions (3¢)
Alpha Signal Hunt (Regular) "Any signals in the last 6 hours?"	GET /signals/recent?hours=6	Signal summary + trend analysis (1¢)
Unified News Feed "Latest crypto news?"	GET /info/feed?limit=30	News + RSS deduplicated + quality scored
Token Price Query "What's BTC price?"

🚀 Quick Start — What Can I Do For You?

When a user first interacts, or asks "what can you do?" / "how do I use this?" / "what combos are available?", proactively present this menu:

---

CT Monitor has 10 ready-to-run intelligence workflows (Combos). Just tell me which one you want:

#	Combo	触发方式	功能
1	📰 Morning Brief	"早报" / "daily brief" / "今天市场怎样"	每日多源情报简报：KOL推文 + 新闻 + 价格 + 信号，5分钟掌握全局
1.5	🔥 Trending Token Discovery	"什么币在热" / "trending" / "热门代币"	四维热度排名：KOL提及 × CoinGecko × 价格涨幅 × 新闻覆盖
2	🎯 Alpha Signal Deep Dive	"Alpha信号" / "deep dive $XXX" / "挖掘机会"	深度挖掘：信号 → KOL验证 → 价格确认 → 新闻背景 → 四层交叉验证
3	👤 KOL Deep Profile	"分析 @xxx" / "KOL画像" / "这个人说了什么"	单个KOL深度画像：历史观点 + 实时推文 + 影响力评分 + 赛道标签
4	🚨 Security Alert	"有没有黑客" / "安全警报" / "hack news"	实时安全扫描：Hack/Exploit/Rug检测 → 链上验证 → 紧急程度评级
5	📖 Narrative Tracker	"市场在讲什么故事" / "叙事分析" / "narrative"	叙事追踪：KOL推文 + 新闻 + Binance社交热度 → 识别正在形成的市场叙事
6	🎁 Airdrop Hunter	"空投" / "airdrop" / "有什么活动" / "TGE"	空投猎手：扫描48h推文 + 新闻 → 提取快照日期/白名单/TGE信息
7	🐋 Smart Money Tracker	"聪明钱" / "巨鲸" / "smart money" / "链上信号"	聪明钱追踪：链上买卖信号 + 净流入排名 + Top Trader持仓 + KOL交叉验证
8	🔄 Sector Rotation	"赛道轮动" / "资金流向" / "哪个赛道热"	赛道轮动检测：KOL热度 × 媒体关注 × 聪明钱流向 → 识别资金切换方向
9	🐸 Meme Hunter	"Meme币" / "meme hunting" / "什么meme在涨"	Meme猎手：Binance Pulse排名 + KOL提及 + 链上流动性验证

示例触发语句：

• "帮我跑一下 Combo 1 早报"
• "有没有最新的 Alpha 信号？"（→ Combo 2）
• "分析一下 @VitalikButerin 最近说了什么"（→ Combo 3）
• "聪明钱最近在买什么？"（→ Combo 7）
• "BSC 上有什么 meme 在涨？"（→ Combo 9）

💡 Tip: 每个 Combo 都会自动调用多个数据源并生成中文综合分析报告。你只需要说出你的需求，我来决定用哪个 Combo。

---

Instructions

Core Principle: CT Monitor's real value is in combining multiple data sources. A single API call is just the starting point — synthesizing data from multiple endpoints produces actionable Alpha insights that no single query can deliver.

---

Combo 1: Morning Intelligence Brief (Daily)

Daily morning briefing covering the past 24 hours of crypto markets. Total cost ~5¢.

Step 1: Get AI comprehensive briefing

curl -s "https://api.ctmon.xyz/api/brief/generate?hours=24" \
  -H "Authorization: Bearer $CT_MONITOR_API_KEY" | jq '.report'

⚠️ Response structure: {"report": "...", "hours": N, "tweet_count": N, "generated_at": "..."} — always extract .report (the Markdown string). If you receive the full JSON object instead of a string, the data is intact; re-extract with | jq '.report'.

Step 2: Trending tokens + KOL mention analysis

curl -s "https://api.ctmon.xyz/api/price/trending?hours=24" \
  -H "Authorization: Bearer $CT_MONITOR_API_KEY" | jq '.'

Step 3: Last 6h high-frequency signals

curl -s "https://api.ctmon.xyz/api/signals/recent?hours=6&min_score=60" \
  -H "Authorization: Bearer $CT_MONITOR_API_KEY" | jq '.'

Step 4: Market overview + news feed (with source attribution)

curl -s "https://api.ctmon.xyz/api/price/summary" \
  -H "Authorization: Bearer $CT_MONITOR_API_KEY" | jq '.'

curl -s "https://api.ctmon.xyz/api/info/feed?limit=30" \
  -H "Authorization: Bearer $CT_MONITOR_API_KEY" | jq '[.[] | select(.score >= 50)] | sort_by(-.score)'

Step 5: Binance Smart Money Signals — 聪明钱最新买卖信号

curl -s -X POST 'https://web3.binance.com/bapi/defi/v1/public/wallet-direct/buw/wallet/web/signal/smart-money' \
  -H 'Accept-Encoding: identity' \
  -H 'Content-Type: application/json' \
  -d '{"smartSignalType":"","page":1,"pageSize":10,"chainId":"CT_501"}' | jq '.data[:10]'

Returns latest smart money buy/sell signals on Solana. Key fields: ticker, direction (buy/sell), smartMoneyCount, triggerPrice, currentPrice, maxGain.

Synthesis prompt:

You have received four data sources:

• Source A: .report — AI-generated briefing (Markdown string) with sections: Market Overview (prices), Key News, Sector Highlights, Notable Alpha. If you received the full JSON object {"report": "...", ...} instead of a string, extract .report before proceeding. Never treat an empty .report as a reason to fabricate — if the field is genuinely empty, skip that section and note "briefing unavailable".
• Source B: trending token list — each item: symbol, cg_rank (CoinGecko trending rank, 1=hottest), mention_count (distinct KOLs mentioning it), price_change (24h % from CoinGecko, accurate per-token), top_kols, sample_tweets
• Source C: alpha signals — each item: keyword (token), kol_count, kols, sample_tweets
• Source D: market summary — global (BTC dominance, total market cap, 24h change) + prices object with keys bitcoin/ethereum/solana/binancecoin/ripple, each containing price_usd, change_24h, source
• Source E: news feed — each item: title, source (media name, e.g. "CNN", "Reuters", "PRNewswire", "Twitter"), score (AI quality score 0-100), summary (AI-generated Chinese summary), url (may be null for 6551 news)
• Source F: smart money signals — latest buy/sell signals, each item: ticker, direction (buy/sell), smartMoneyCount, triggerPrice, currentPrice, maxGain, chainId

Generate a Markdown-formatted morning intelligence report with this exact structure:

Header: Use the exact date/time from .report (e.g. "October 26, 2024 20:30 PST")

📊 Market Overview: Copy the Market Overview section from .report verbatim. Then append: . Skip this line if signals is .

🤖 Automate this combo — run every morning at 8am and deliver to Telegram:

openclaw cron add \
  --name "CT Morning Brief" \
  --cron "0 8 * * *" \
  --tz "Asia/Shanghai" \
  --session isolated \
  --message "Run CT Monitor Combo 1: call /brief/generate?hours=24 (use .report field), /price/trending?hours=24, /signals/recent?hours=6&min_score=60, /price/summary, /info/feed?limit=30 (filter score>=50 sorted by score desc). Synthesize into a Markdown morning report with 6 sections: (1) 📊 Market Overview — copy .report verbatim + append KOL Signal line from signals data; (2) 📰 Key News — use info/feed score>=50 as primary source, format [source] Title → Impact: assessment, cross-ref .report Key News; (3) 🔥 Sector Pulse — table with heating/cooling/stable ratings based on .report + info/feed sector news; (4) 💡 Notable Alpha — use info/feed score>=60 as primary source, format [source] Title → Alpha: insight, cross-ref .report Notable Alpha; (5) 📈 Trending Tokens — list only mention_count>=2 sorted by mention_count desc, mark ⚡ if in signals, add warning for cg_rank<=5 AND mention_count=0; (6) 🎯 DCA 参考信号 — BTC dominance from price/summary.global, DCA recommendation in ≤2 sentences. Use price_change field (not price_change_24h). Never fabricate source names." \
  --announce \
  --channel telegram

---

Combo 1.5: Trending Token Discovery (What's hot and why?)

Answer "What tokens are hot right now and why?" with multi-dimensional heat analysis. Total cost ~4¢.

Step 1: Trending tokens — KOL mentions + CoinGecko rank + price

curl -s "https://api.ctmon.xyz/api/price/trending?hours=24" \
  -H "Authorization: Bearer $CT_MONITOR_API_KEY" | jq '.'

Step 2: Alpha signals — check if KOL consensus has formed

curl -s "https://api.ctmon.xyz/api/signals/recent?hours=6&min_score=60" \
  -H "Authorization: Bearer $CT_MONITOR_API_KEY" | jq '.'

Step 3: News feed — check media coverage for hot tokens

curl -s "https://api.ctmon.xyz/api/info/feed?limit=30" \
  -H "Authorization: Bearer $CT_MONITOR_API_KEY" | jq '.'

Step 4: Market baseline — BTC/ETH price to judge relative strength

curl -s "https://api.ctmon.xyz/api/price/summary" \
  -H "Authorization: Bearer $CT_MONITOR_API_KEY" | jq '.'

Step 5: Binance Unified Token Rank — 链上 Trending/Alpha 排名

curl -s -X POST 'https://web3.binance.com/bapi/defi/v1/public/wallet-direct/buw/wallet/market/token/pulse/unified/rank/list' \
  -H 'Accept-Encoding: identity' \
  -H 'Content-Type: application/json' \
  -d '{"page":1,"pageSize":20}' | jq '.data.tokens[:10]'

Returns Binance's unified on-chain ranking (top 200 tokens by trading activity). Key fields: symbol, percentChange24h, volume24h, price. Results are ordered by on-chain activity score (position = rank). Note: field is .data.tokens (not .data.rankList).

Synthesis prompt:

You have received five data sources:

• Source A: trending token list — each item: symbol, cg_rank (CoinGecko trending rank, 1=hottest), mention_count (distinct KOLs mentioning it), price_change (24h % from CoinGecko), top_kols, sample_tweets
• Source B: alpha signals — each item: keyword (e.g. $BTC), kol_count, kols list; tokens where multiple KOLs are simultaneously mentioning
• Source C: news feed — recent news and RSS articles, each item has title, coins, score
• Source D: market summary — BTC/ETH baseline prices and 24h changes (key is full name e.g. bitcoin, ethereum)
• Source E: Binance unified ranking — top 10 tokens by on-chain activity (position = rank), each item: symbol, percentChange24h, volume24h, price

Generate a Markdown-formatted trending token report:

Header: "🔥 Trending Token Analysis — Past [N] Hours" with current timestamp

📊 Market Baseline: One line — BTC and ETH 24h change from Source D. This is the baseline to judge relative strength.

📈 Heat Ranking — use a Markdown table with 6 columns, sorted by mention_count descending (ties broken by abs(price_change) descending):

Signal	Token	KOL提及	代表KOL	24h涨跌	验证	热度原因
⚡	$BTC	52次	AshCrypto, CoinDesk, lookonchain	-2.11%	⚡📰	宏观压力，KOL争论支撑位
—	$RIVER	4次	KOL1, KOL2	+21.12%	🔥	跨链稳定币叙事+补偿计划

Rules for the 验证 (Verification) column — combine applicable emoji, no spaces between them:

• ⚡ = token's symbol (strip $ prefix) appears in Source B field (KOL consensus signal)

---

Combo 2: Alpha Signal Deep Dive (When opportunity appears)

A signal shows a token being mentioned by multiple KOLs simultaneously — deep dive to validate the alpha opportunity. Focused on altcoins and meme tokens only. Total cost ~6¢.

Step 1: Discover alpha signals — filter out majors, focus on altcoins/memes

curl -s "https://api.ctmon.xyz/api/signals/recent?hours=6&min_score=60" \
  -H "Authorization: Bearer $CT_MONITOR_API_KEY" | \
  jq '[.[] | select(.keyword | ascii_downcase | test("btc|eth|sol|xrp|bnb|usdc|usdt|ada|ltc") | not)]'

Filters out major coins (BTC/ETH/SOL/XRP/BNB/USDC/USDT/ADA/LTC) to surface only altcoin and meme alpha signals. Key fields: keyword (token symbol e.g. $PENGU), kol_count (number of KOLs mentioning), kols (list of KOL usernames). Pick the signal with highest kol_count for deep dive. If no signals pass the filter, report "No altcoin alpha signals in the past 6h."

Step 2: Query token price and momentum (replace TOKEN with the symbol from Step 1's top signal)

curl -s "https://api.ctmon.xyz/api/price/token?symbol=TOKEN" \
  -H "Authorization: Bearer $CT_MONITOR_API_KEY" | jq '.'

Use the actual token symbol from Step 1 (strip $ prefix, e.g. $PENGU → PENGU). Auto-fallback: CoinGecko → Binance → DexScreener. Check source field to see data origin. Key fields: price_usd, change_24h, source. If change_24h > 0 while BTC is down, it's counter-trend strength.

Step 3: Read what KOLs are actually saying about this token

curl -s "https://api.ctmon.xyz/api/tweets/feed?hours=6&limit=200" \
  -H "Authorization: Bearer $CT_MONITOR_API_KEY" | \
  jq '[.[] | select(.text | test("PENGU|\\$PENGU"; "i"))] | sort_by(.like_count) | reverse'

Fetches 6h of tweets from all monitored KOLs, filters for the signal token, sorted by engagement. Key fields: username, text, like_count, retweet_count, priority (ultra_high/high/normal/low), sector. If 0 results: The keyword from Step 1 (e.g. $ABTC) may differ from how KOLs actually write about it. Try searching by the token's full name (e.g. "American Bitcoin") or common abbreviation. If still 0, note "KOL tweets not found in feed — signal may be from retweets or external sources" in the report.

Step 4: Related news and RSS coverage

curl -s "https://api.ctmon.xyz/api/info/feed?coin=PENGU&limit=20" \
  -H "Authorization: Bearer $CT_MONITOR_API_KEY" | jq '.'

Key fields: title, url (include in report as clickable link), score (AI quality 0-100), source (media name), summary (AI-generated summary). Note: summary may contain raw HTML tags — strip them and extract plain text when presenting. Note: For small/niche tokens, score may be 0. Include all news items regardless of score; use score only as a quality indicator in the report. Translate all titles and summaries into the user's language in the final report.

Step 5: Assess the quality of KOLs who mentioned this token

curl -s "https://api.ctmon.xyz/api/users/top?limit=200" \
  -H "Authorization: Bearer $CT_MONITOR_API_KEY" | \
  jq '[.[] | select(.username | IN("KOL1","KOL2","KOL3"))]'

Replace KOL1/KOL2/KOL3 with the actual kols list from Step 1. Key fields: username, score (0-100, KOL quality score), priority (ultra_high/high/normal/low), followers_count, sector. This tells you if the signal is backed by high-quality KOLs or low-quality noise accounts.

Step 6: On-chain validation — Binance Smart Money multi-chain signals

Query all supported chains in parallel to maximize coverage:

for CHAIN in CT_501 CT_56 CT_1 CT_8453 CT_42161; do
  echo "=== $CHAIN ===" && \
  curl -s -X POST 'https://web3.binance.com/bapi/defi/v1/public/wallet-direct/buw/wallet/web/signal/smart-money' \
    -H 'Accept-Encoding: identity' \
    -H 'Content-Type: application/json' \
    -d "{\"smartSignalType\":\"\",\"page\":1,\"pageSize\":20,\"chainId\":\"$CHAIN\"}" | jq '.data[:5]'
done

Supported chains: CT_501 (Solana), CT_56 (BSC/BNB Chain), CT_1 (Ethereum), CT_8453 (Base), CT_42161 (Arbitrum). Key fields: ticker, direction (buy/sell), smartMoneyCount, triggerPrice, maxGain. Cross-reference: search all chain results for the signal token's symbol.

• Token found with direction=buy on any chain → strong on-chain confirmation 🔥 (note which chain)
• Token found with direction=sell → smart money exiting ⚠️
• Token not found on any chain → no on-chain data yet (may be very early stage)

Synthesis prompt:

You have received 6 data sources for a specific altcoin/meme token (replace $PENGU with actual token):

• Source A: signal data — keyword, kol_count, kols list
• Source B: price data — price_usd, change_24h, source
• Source C: KOL tweets — each item: username, text, like_count, priority, sector
• Source D: news/RSS — each item: title, url, score, source, summary
• Source E: KOL quality scores — each item: username, score (0-100), priority, followers_count
• Source F: Binance multi-chain smart money signals — each item: ticker, direction, smartMoneyCount, chain (Solana/BSC/ETH/Base/Arbitrum)

Generate a signal analysis report in the user's language with this structure:

Header: "⚡ Alpha Signal: $TOKEN — [timestamp]"

① 信号强度: Rate as 强/中/弱 based on kol_count (≥5=强, 3-4=中, 2=弱). List the KOL names.

② KOL 质量: From Source E, classify the mentioning KOLs:

• 顶级 (score≥90 or priority=ultra_high): list names
• 优质 (score 70-89 or priority=high): list names
• 普通 (score<70): list names
• Overall verdict: "高质量共识" / "混合质量" / "低质量噪音"

③ 价格上下文: Current price + 24h change. Note if counter-trend (up while BTC down). Data source.

④ KOL 在说什么: Summarize the top 3-5 tweets by engagement. Quote key phrases. Identify the narrative (e.g. "partnership announcement", "airdrop", "technical breakout", "pure hype").

⑤ 新闻佐证: List all news items from Source D as: [source] [score分] [title](url) → [one-line translated summary]. Show score as a quality indicator. If no news at all, write "暂无相关新闻报道".

⑥ 链上验证:

• If token in Source F with direction=buy: "🔥 链上聪明钱正在建仓 (smartMoneyCount=N，链: [chain name])"
• If token in Source F with direction=sell: "⚠️ 聪明钱正在出货 (链: [chain name])"

🤖 Automate this combo — check every 15 minutes, alert only when a real altcoin signal appears:

openclaw cron add \
  --name "CT Alpha Alert" \
  --cron "*/15 * * * *" \
  --session isolated \
  --message "Call CT Monitor /api/signals/recent?hours=0.25&min_score=60. Filter out BTC/ETH/SOL/XRP/BNB/USDC/USDT. If any altcoin/meme signal has kol_count >= 3, run the full Combo 2 deep dive on that token and send an alert. If no qualifying signals, stay silent." \
  --announce \
  --channel telegram

---

Combo 3: KOL Deep Profile (Research a specific KOL)

Comprehensive understanding of a KOL's investment thesis, recent views, and influence. Total cost ~3¢.

Step 1: Get KOL profile stats

curl -s "https://api.ctmon.xyz/api/users/top?limit=200" \
  -H "Authorization: Bearer $CT_MONITOR_API_KEY" | \
  jq '[.[] | select(.username == "cobie")] | .[0]'

Key fields: score (0-100), priority, followers_count, sector, sector_tags.

Step 2: Get historical tweets (main data source)

curl -s "https://api.ctmon.xyz/tweets/recent?username=cobie&limit=50" \
  -H "Authorization: Bearer $CT_MONITOR_API_KEY" | jq '[.[] | {text, created_at, like_count, retweet_count, view_count, is_quote, is_retweet}]'

Note: /tweets/recent (no /api/ prefix) requires the same Authorization: Bearer header. Key fields: text, created_at, like_count, view_count, is_quote, is_retweet.

Step 3: Search how other KOLs reference this KOL

curl -s "https://api.ctmon.xyz/api/tweets/feed?hours=48&limit=500" \
  -H "Authorization: Bearer $CT_MONITOR_API_KEY" | \
  jq '[.[] | select(.text | ascii_downcase | test("cobie|@cobie"))]'

Note: For data-broadcaster type KOLs (e.g. lookonchain, whale_alert), this may return 0 results — other KOLs typically retweet rather than mention by name. This is expected behavior; proceed with Step 2 data only.

Synthesis prompt:

Above is @cobie's data (profile stats + historical tweets + others' references). Generate a KOL profile report in the user's language: ① 近期关注赛道/项目 — What sectors/tokens/projects has this KOL been focused on recently? ② 核心观点 — Bullish/Bearish stance on key assets. Does the KOL express personal opinions or just report data? ③ 投资逻辑分析 — What is the KOL's analytical framework? (on-chain data, fundamentals, narratives, TA, macro?) ④ 影响力评估 — Score, followers, avg engagement (likes/views), citation quality from Step 3. ⑤ 关键洞察 — What unique alpha or early signals has this KOL surfaced recently? Any actionable insights?

---

Combo 4: Security Alert Response (When risk appears)

A Hack/Rug surfaces in the market — quickly assess the impact. Total cost ~4¢.

Step 1: Confirm the event

curl -s "https://api.ctmon.xyz/api/tweets/feed?hours=48&limit=500" \
  -H "Authorization: Bearer $CT_MONITOR_API_KEY" | \
  jq '[.[] | select(
    .text | ascii_downcase | test("hack|exploit|rug|drain|stolen|breach|compromised|vulnerability")
    or (
      (.text | ascii_downcase | test("attack|warning|alert|suspicious|emergency|pause"))
      and (.text | ascii_downcase | test("contract|vault|protocol|wallet|token|defi|nft|fund|pool|liquidity"))
    )
  ) | {username, created_at, text, like_count, view_count}]'

Filter logic: Group A keywords (hack|exploit|rug|drain|stolen|breach|compromised|vulnerability) trigger alone. Group B keywords (attack|warning|alert|suspicious|emergency|pause) only trigger when co-occurring with DeFi context words — this avoids false positives from geopolitical/macro news. After filtering, manually identify distinct security events (ignore duplicates and macro/political noise).

Step 2: Check news coverage

curl -s "https://api.ctmon.xyz/api/info/feed?hours=48&limit=50" \
  -H "Authorization: Bearer $CT_MONITOR_API_KEY" | \
  jq '[.[] | select(.title | ascii_downcase | test("hack|exploit|rug|stolen|breach|security|vulnerability")) | {title, source, published_at, url, score}]'

Note: For breaking events, news may lag 1-6 hours behind Twitter. If 0 results, note "news not yet available" and rely on Step 1 KOL tweets.

Step 3: Check affected token price

curl -s "https://api.ctmon.xyz/api/price/token?symbol=XXX" \
  -H "Authorization: Bearer $CT_MONITOR_API_KEY" | jq '.'

Replace XXX with the token symbol identified in Step 1. Check change_1h and change_24h for panic signals. A sharp drop (>10% in 1h) confirms market reaction.

Step 4: Recent panic signals (last 1h)

curl -s "https://api.ctmon.xyz/api/signals/recent?hours=1&min_score=0" \
  -H "Authorization: Bearer $CT_MONITOR_API_KEY" | jq '.'

If 0 results: event may be too recent or too niche to generate KOL signal volume yet. This is normal for breaking events — rely on Step 1 + Step 3 data.

Synthesis prompt:

Above is security event data (KOL tweets + news + price + signals). Generate a security flash report in the user's language: ① 事件确认 — Is this a real exploit/hack/rug or FUD? Summarize what happened, who reported it, and when. ② 影响范围 — How many users/funds affected? Which protocol/vault/chain? Is it isolated or systemic risk? ③ 受影响资产分析 — Token price reaction (1h/24h change). Is the market pricing in the risk? ④ 紧急程度评级 — Rate as 🔴 High / 🟡 Medium / 🟢 Low based on: loss size, scope, official response speed, and whether root cause is disclosed. ⑤ 操作建议 — What should holders do? (Hold/Exit/Monitor). What signals to watch next (official post-mortem, bounty response, further exploits)?

🤖 Automate this combo — monitor every 15 minutes, alert immediately on confirmed security events:

openclaw cron add \
  --name "CT Security Watch" \
  --cron "*/15 * * * *" \
  --session isolated \
  --message "Call CT Monitor /api/tweets/feed?hours=1&limit=200 and filter for: Group A (hack|exploit|rug|drain|stolen|breach|compromised|vulnerability) OR Group B (attack|warning|alert|suspicious|emergency|pause) AND DeFi context (contract|vault|protocol|wallet|token|defi|nft|fund|pool). If 2+ KOLs mention the same security event, run the full Combo 4 analysis and send an URGENT alert. If nothing found, stay silent." \
  --announce \
  --channel telegram

---

Combo 5: Narrative Trend Tracker (What story is the market telling?)

Identify which narratives are heating up and which are cooling down. Total cost ~3¢.

Step 1: Scan narrative heat by sector keywords (limit=3000 covers ~23h, a full trading day)

for sector in "agent" "AI" "RWA" "DePIN" "meme" "Solana" "stablecoin" "DeFi" "NFT" "restaking" "BTCFi" "GameFi"; do
  echo "=== $sector ===" && \
  curl -s "https://api.ctmon.xyz/api/tweets/feed?limit=3000" \
    -H "Authorization: Bearer $CT_MONITOR_API_KEY" | \
    jq --arg s "$sector" '[.[] | select(.text | test($s; "i"))] | length'
done

Step 2: Check signal-level resonance across narratives

curl -s "https://api.ctmon.xyz/api/signals/recent?hours=24&min_score=50" \
  -H "Authorization: Bearer $CT_MONITOR_API_KEY" | jq '.'

Step 3a: Verify if narratives are already reflected in prices

curl -s "https://api.ctmon.xyz/api/price/trending?hours=24" \
  -H "Authorization: Bearer $CT_MONITOR_API_KEY" | jq '.'

Step 3b: Binance spot volume validation — dynamically build symbols from Step 3a tokens where mention_count >= 2, append USDT suffix (e.g. BTC → BTCUSDT), then query Binance public API (no auth required):

curl -g -s 'https://api.binance.com/api/v3/ticker/24hr?symbols=["BTCUSDT","SOLUSDT","ETHUSDT"]' | \
  jq '[.[] | {symbol, priceChangePercent, volume: (.volume | tonumber | floor), quoteVolume: (.quoteVolume | tonumber | floor), trades: .count}]'

Replace the symbols array with actual tokens from Step 3a. Only include tokens that have a Binance USDT pair. Skip tokens not listed on Binance spot.

Step 3c: Binance Social Hype — BSC 社交热度排名

curl -s 'https://web3.binance.com/bapi/defi/v1/public/wallet-direct/buw/wallet/market/token/pulse/social/hype/rank/leaderboard?chainId=56&sentiment=All&socialLanguage=ALL&targetLanguage=en&timeRange=1' \
  -H 'Accept-Encoding: identity' | \
  jq '[.data.leaderBoardList[:10] | .[] | {symbol: .metaInfo.symbol, hype: .socialHypeInfo.socialHype, kol_count: .socialHypeInfo.kolCount, sentiment: .socialHypeInfo.sentiment, price_change_24h: .marketInfo.priceChange, summary: .socialHypeInfo.socialSummaryBriefTranslated}]'

Required params: chainId (56=BSC, 8453=Base, CT_501=Solana), targetLanguage (en/zh), timeRange (1=24h), sentiment (All/Positive/Negative/Neutral). Key fields: symbol, hype (social hype index), kol_count, sentiment, price_change_24h (24h price change %), summary (AI-generated social summary). Note: This API is only accessible from server environments (EC2/cloud). Local macOS may get connection reset due to IP geo-restriction — this is expected.

Synthesis prompt:

You have received four data sources:

• Source A: sector keyword tweet counts (from Step 1) — 12 keywords scanned across ~23h of KOL tweets
• Source B: alpha signals (from Step 2) — tokens with multi-KOL resonance in last 24h
• Source C: trending tokens + Binance spot volume (from Step 3a + 3b) — KOL mention counts, price changes, and Binance 24h volume/trade counts
• Source D: Binance Social Hype leaderboard (BSC) — top 10 tokens by social hype score, each item: symbol, hype (index), kol_count, sentiment, price_change_24h (%), summary (AI social brief)

Filter rule for "agent" keyword: When counting "agent" mentions, exclude non-crypto contexts (real estate agents, travel agents, insurance agents, FBI agents, secret agents). Only count crypto/AI/on-chain/trading agent contexts (AI agent, on-chain agent, DeFi agent, AgentFi, trading bot agent, autonomous agent).

Generate a Markdown-formatted narrative trend report:

① Narrative Heat Ranking — table sorted by tweet count descending:

Rank	Narrative	Tweet Count	Signal	Volume Signal	Social Hype	Status
#1	agent	121	⚡ $AI x5 KOL	🔥 Surge	🔥	🔥 Heating
#2	AI	104	⚡ $NEAR x3 KOL	📢 Active	🔥	🔥 Heating
#3	meme	44	—	🔇 Quiet	—	➡️ Stable

Volume Signal column rules (from Step 3b Binance data):

• 🔥 Surge: quoteVolume > $500M in 24h OR trades > 500,000
• 📢 Active: quoteVolume $50M–$500M OR trades 50,000–500,000
• 🔇 Quiet: quoteVolume < $50M OR no Binance listing
• N/A: token not on Binance spot

Social Hype column rules (from Source D — Binance Social Hype):

• 🔥 if any token in this narrative appears in Binance Social Hype Top 10
• — if not

② Four-Layer Signal Interpretation — for each narrative in Top 5, assess using the matrix:

🤖 Automate this combo — daily narrative pulse delivered every evening:

openclaw cron add \
  --name "CT Narrative Pulse" \
  --cron "0 20 * * *" \
  --tz "Asia/Shanghai" \
  --session isolated \
  --message "Run CT Monitor Combo 5: scan /tweets/feed?limit=3000 for sector keywords (agent, AI, RWA, DePIN, meme, Solana, stablecoin, DeFi, NFT, restaking, BTCFi, GameFi) — for 'agent' keyword exclude non-crypto contexts (real estate/travel/insurance/FBI agents). Check /signals/recent?hours=24&min_score=50. Check /price/trending?hours=24 for mention_count>=2 tokens, then query Binance spot ticker/24hr for those tokens (append USDT suffix). Call Binance Social Hype API (chainId=56, sentiment=All, socialLanguage=ALL, targetLanguage=en, timeRange=1, extract top 10 from leaderBoardList). Generate narrative heat ranking table with Social Hype column (🔥 if token in narrative appears in Binance Social Hype Top 10), four-layer signal interpretation matrix (Twitter Hype/Smart Money/Volume/Social Hype), price validation, overheating warnings, and emerging narrative alerts." \
  --announce \
  --channel telegram

---

Combo 6: Airdrop & Event Hunter (Never miss an opportunity)

Surface upcoming airdrops, TGEs, unlock events, and snapshot deadlines. Total cost ~2¢.

Step 1: Scan KOL tweets for event keywords

curl -s "https://api.ctmon.xyz/api/tweets/feed?hours=48&limit=500" \
  -H "Authorization: Bearer $CT_MONITOR_API_KEY" | \
  jq '[.[] | select(.text | test("airdrop|snapshot|TGE|unlock|claim|whitelist|mint|IDO|launchpad"; "i")) | {username, created_at, text, like_count, view_count}]'

Use hours=48&limit=500 to ensure full coverage — event tweets can be sparse and easily missed with smaller limits. After filtering, group by project and identify distinct events. Ignore obvious ads/spam (casino airdrops, "IYKYK" posts, non-English promotional content).

Step 2: Check news coverage for upcoming events

curl -s "https://api.ctmon.xyz/api/info/feed?hours=48&limit=50" \
  -H "Authorization: Bearer $CT_MONITOR_API_KEY" | \
  jq '[.[] | select(
    .title | ascii_downcase | test("airdrop|tge|token generation|snapshot|claim|whitelist|mint|ido|launchpad")
    or (
      (.title | ascii_downcase | test("launch|unlock"))
      and (.title | ascii_downcase | test("airdrop|token|tge|ido|crypto|defi|nft"))
    )
  ) | {title, source, published_at, url, score}]'

Filter logic: direct event keywords trigger alone; launch|unlock only trigger when co-occurring with crypto/event context — avoids false positives from product launches, ETF launches, etc.

Step 3: Check if KOLs are concentrating attention on specific events

curl -s "https://api.ctmon.xyz/api/signals/recent?hours=24" \
  -H "Authorization: Bearer $CT_MONITOR_API_KEY" | jq '.'

Synthesis prompt:

Above is event-related data (KOL tweets + news + signals). Generate an event hunter report: ① Upcoming event list (sorted by urgency/deadline) ② Participation value assessment for each (effort vs. expected reward) ③ Risk flags (potential scams or low-quality projects) ④ Action checklist (what to do and by when)

🤖 Automate this combo — daily airdrop scan every morning before the brief:

openclaw cron add \
  --name "CT Airdrop Hunter" \
  --cron "0 7 * * *" \
  --tz "Asia/Shanghai" \
  --session isolated \
  --message "Run CT Monitor Combo 6: scan /api/tweets/feed?hours=48&limit=500 for airdrop/snapshot/TGE/unlock/claim/whitelist/mint/IDO/launchpad keywords (ignore casino ads and IYKYK spam), check /api/info/feed?hours=48&limit=50 for event news (filter: direct event keywords OR launch/unlock + crypto context), check /api/signals/recent?hours=24. Generate an event list sorted by urgency with participation value assessment and action checklist." \
  --announce \
  --channel telegram

---

Combo 7: Smart Money Tracker (Follow the whales)

Track real on-chain smart money movements, not just KOL tweets. Total cost ~4¢.

Step 1: Twitter Top KOL baseline (retained from original)

curl -s "https://api.ctmon.xyz/api/users/top?limit=20" \
  -H "Authorization: Bearer $CT_MONITOR_API_KEY" | jq '.[].username'

Step 2: Binance Trading Signal — 链上聪明钱买卖信号

curl -s -X POST 'https://web3.binance.com/bapi/defi/v1/public/wallet-direct/buw/wallet/web/signal/smart-money' \
  -H 'Content-Type: application/json' \
  -H 'Accept-Encoding: identity' \
  -d '{"smartSignalType":"","page":1,"pageSize":50,"chainId":"56"}' | \
  jq '[.data[] | {ticker, direction, smartMoneyCount, alertPrice, currentPrice, maxGain, exitRate, status}]'

chainId: "56" (BSC) or "CT_501" (Solana). Response: data is a direct array. Key fields: ticker, direction (buy/sell), smartMoneyCount, alertPrice, currentPrice, maxGain (%), exitRate (%), status (active/timeout/completed).

Step 3: Binance Smart Money Inflow — 聪明钱净流入排名

curl -s -X POST 'https://web3.binance.com/bapi/defi/v1/public/wallet-direct/tracker/wallet/token/inflow/rank/query' \
  -H 'Content-Type: application/json' \
  -H 'Accept-Encoding: identity' \
  -d '{"chainId":"56","period":"24h","tagType":2}' | \
  jq '[.data | sort_by(-.inflow) | .[:15][] | {tokenName, price, priceChangeRate, inflow, traders}]'

Response: data is a direct array. Key fields: tokenName, price, priceChangeRate (%), inflow (USD net inflow, negative = outflow), traders (smart money address count).

Step 4: Binance Top Trader PnL

curl -s 'https://web3.binance.com/bapi/defi/v1/public/wallet-direct/market/leaderboard/query?tag=ALL&pageNo=1&chainId=56&pageSize=10&sortBy=0&orderBy=0&period=7d' \
  -H 'Accept-Encoding: identity' | \
  jq '[.data.data[:10][] | {addressLabel, realizedPnl, winRate, totalVolume, topEarningTokens: [.topEarningTokens[:3][] | {tokenSymbol, realizedPnl}]}]'

Response: data.data is the trader array. Key fields: addressLabel (trader name/null), realizedPnl (USD), winRate, totalVolume, topEarningTokens[].tokenSymbol.

Step 5: Cross-reference with CT Monitor KOL tweets

# Get recent tweets for tokens with high smart money inflow (example: BTC, ETH, SOL)
for token in BTC ETH SOL; do
  echo "=== $token ===" && \
  curl -s "https://api.ctmon.xyz/api/tweets/feed?limit=50" \
    -H "Authorization: Bearer $CT_MONITOR_API_KEY" | \
    jq --arg t "$token" '[.[] | select(.text | test($t; "i"))] | length'
done

Synthesis prompt:

You have received five data sources for on-chain smart money tracking:

• Source A: CT Monitor top KOL usernames (for reference)
• Source B: Binance Trading Signal — recent smart money BUY/SELL signals across tokens
• Source C: Binance Smart Money Inflow — tokens ranked by net inflow amount
• Source D: Binance Top Trader PnL — top performing traders and their positions
• Source E: CT Monitor tweet counts for tokens with high smart money activity

Generate a Markdown-formatted smart money tracker report with 5 sections:

🐋 聪明钱净流入 Top 10 (from Source C):

Rank	Token	Net Inflow (USD)	24h Change	KOL Mentions
1	$XXX	$1,234,567	+5.2%	12

KOL Mentions: cross-reference with Source E; if >= 5, mark "🔥 KOL confirmed"

📈 聪明钱建仓信号 (from Source B):

• List tokens where BUY signals > SELL signals in recent period
• Group by token: "$TOKEN: X BUY signals vs Y SELL signals"
• Highlight tokens with BUY/SELL ratio > 3:1 as "🔥 Strong accumulation"

📉 聪明钱出货警告:

• List tokens where SELL signals > BUY signals
• Any token with SELL/BUY ratio > 3:1: "⚠️ Heavy distribution detected"

🏆 Top Trader 持仓动向 (from Source D):

• List top 5 traders and their key positions
• Identify tokens held by multiple top traders

🔗 KOL × 聪明钱共振:

• Tokens appearing in BOTH Source C (inflow top 10) AND Source E (KOL mentions >= 5)
• These are the highest conviction signals
• Format: "$TOKEN: Smart Money inflow $X + Y KOL mentions = 🔥 Double confirmed"

Language rule: Detect the user's language and write the ENTIRE report in that language.

🤖 Automate this combo — daily whale watch delivered at noon:

openclaw cron add \
  --name "CT Whale Watch" \
  --cron "0 12 * * *" \
  --tz "Asia/Shanghai" \
  --session isolated \
  --message "Run CT Monitor Combo 7: get /users/top?limit=20 (Source A), call Binance Trading Signal smart-money API (Source B), call Binance Smart Money Inflow API (Source C), call Binance Top Trader PnL API (Source D), cross-reference top inflow tokens with CT Monitor /tweets/feed to count KOL mentions (Source E). Generate 5-section report: (1) 聪明钱净流入 Top 10 with KOL Mentions column (🔥 if >=5), (2) 聪明钱建仓信号 (BUY>SELL tokens, highlight ratio>3:1), (3) 聪明钱出货警告 (SELL>BUY tokens, warn ratio>3:1), (4) Top Trader 持仓动向 (top 5 traders + shared positions), (5) KOL × 聪明钱共振 (tokens in both inflow top 10 AND KOL mentions >=5). Use exact field names from APIs. Never fabricate data." \
  --announce \
  --channel telegram

---

Combo 8: Sector Rotation Detector (Where is the money flowing?)

Detect which sectors are gaining momentum and which are cooling down. Total cost ~3¢.

Step 1: Trending token snapshot + KOL mention count

curl -s "https://api.ctmon.xyz/api/price/trending?hours=24" \
  -H "Authorization: Bearer $CT_MONITOR_API_KEY" | \
  jq '[.[:15][] | {symbol, mention_count, price_usd, change_24h, top_kols: .top_kols[:3]}]'

Key fields: symbol, mention_count (KOL mentions in window), change_24h, top_kols. Use mention_count to rank sector heat.

Step 2: Signal acceleration — 6h vs. 24h KOL signal comparison

curl -s "https://api.ctmon.xyz/api/signals/recent?hours=6&min_score=0" \
  -H "Authorization: Bearer $CT_MONITOR_API_KEY" | \
  jq '[.[] | {keyword, kol_count}]'

curl -s "https://api.ctmon.xyz/api/signals/recent?hours=24&min_score=0" \
  -H "Authorization: Bearer $CT_MONITOR_API_KEY" | \
  jq '[.[] | {keyword, kol_count}]'

Compare kol_count for same keyword across 6h vs 24h. If 6h kol_count is close to 24h kol_count → signal is accelerating (most activity in last 6h).

Step 3: Media attention shift by sector

curl -s "https://api.ctmon.xyz/api/info/feed?limit=50" \
  -H "Authorization: Bearer $CT_MONITOR_API_KEY" | \
  jq '[.[] | {title: .title, sector: (
    if (.title | test("AI|agent|artificial intelligence|LLM|GPT"; "i")) then "AI"
    elif (.title | test("RWA|real.world|tokenized|tokenisation"; "i")) then "RWA"
    elif (.title | test("DePIN|decentralized physical"; "i")) then "DePIN"
    elif (.title | test("DeFi|defi|liquidity|yield|swap|lending|AMM"; "i")) then "DeFi"
    elif (.title | test("meme|memecoin|pepe|doge|shib"; "i")) then "Meme"
    elif (.title | test("Layer2|L2|rollup|zk|zkEVM|optimism|arbitrum"; "i")) then "Layer2"
    elif (.title | test("Bitcoin|BTC|satoshi|lightning network"; "i")) then "Bitcoin"
    elif (.title | test("ETH|Ethereum|staking|restaking|EIP"; "i")) then "Ethereum"
    elif (.title | test("Solana|SOL|SVM"; "i")) then "Solana"
    elif (.title | test("NFT|ordinal|inscription"; "i")) then "NFT"
    elif (.title | test("regulation|SEC|CFTC|congress|policy|law"; "i")) then "Macro/Reg"
    else "Other" end
  )}] | group_by(.sector) | map({sector: .[0].sector, count: length}) | sort_by(-.count)'

Step 4: Smart Money sector flow — 聪明钱赛道流向

curl -s -X POST 'https://web3.binance.com/bapi/defi/v1/public/wallet-direct/tracker/wallet/token/inflow/rank/query' \
  -H 'Content-Type: application/json' \
  -H 'Accept-Encoding: identity' \
  -d '{"chainId":"56","period":"24h","tagType":2}' | \
  jq '[.data | sort_by(-.inflow) | .[:30][] | {tokenName, inflow, traders, priceChangeRate}]'

Response: data is a direct array. Sort by inflow descending. Classify each tokenName by sector (AI/Meme/DeFi/RWA etc.) to count sector-level smart money concentration.

Synthesis prompt:

You have received four data sources for sector rotation analysis:

• Source A: 24h vs. 7d trending token comparison (from Step 1)
• Source B: 6h vs. 24h signal acceleration comparison (from Step 2)
• Source C: Media attention shift by sector (from Step 3)
• Source D: Smart money sector flow — top 50 tokens by net inflow, to be classified by sector

Generate a sector rotation report with enhanced matrix:

① Sector Heat Change Matrix:

Sector	24h vs 7d Trend	Signal Accel	Media Attention	聪明钱流向	Status
AI	🔥 Heating	📈 +15%	High	🔥 Inflow	🔥 Strong rotation
Meme	➡️ Stable	➡️ 0%	Medium	— Neutral	➡️ Stable
RWA	❄️ Cooling	📉 -8%	Low	📤 Outflow	❄️ Exiting

聪明钱流向 column rules (from Step 4):

• 🔥 Inflow: 3+ tokens from this sector in Smart Money Top 30
• — Neutral: 1-2 tokens in Top 30
• 📤 Outflow: 0 tokens in Top 30 but appeared in previous reports
• N/A: No data for this sector

② Rotation direction judgment: where is attention flowing FROM and TO

③ Early-stage vs. late-stage identification for each sector

④ Reallocation suggestions: which sectors to increase/decrease exposure

🤖 Automate this combo — weekly sector rotation report every Sunday evening:

openclaw cron add \
  --name "CT Sector Rotation" \
  --cron "0 21 * * 0" \
  --tz "Asia/Shanghai" \
  --session isolated \
  --message "Run CT Monitor Combo 8: compare /price/trending?hours=24 vs hours=168 (Source A), compare /signals/recent?hours=6 vs hours=24 (Source B), scan /info/feed for sector media attention (Source C), call Binance Smart Money Inflow API pageSize=50 (Source D). Generate sector heat change matrix with 聪明钱流向 column (🔥Inflow if 3+ tokens in sector in Top 30, —Neutral if 1-2, 📤Outflow if 0 but appeared before), rotation direction judgment, early/late-stage identification, and reallocation suggestions." \
  --announce \
  --channel telegram

---

Combo 9: Meme Token Hunting (Catch the viral memes early)

Hunt for trending meme tokens by combining Binance Pulse ranking with CT Monitor KOL mentions. Total cost ~2¢.

Step 1: Binance Meme Token Ranking — BNB Chain exclusive meme pulse

curl -s 'https://web3.binance.com/bapi/defi/v1/public/wallet-direct/buw/wallet/market/token/pulse/exclusive/rank/list?chainId=56' \
  -H 'Accept-Encoding: identity' | \
  jq '[.data.tokens[:20][] | {rank, symbol: .symbol, name: .metaInfo.name, price, percentChange, percentChange7d, volume, liquidity, holders, score, impression, alphaStatus}]'

Response: data.tokens is the array (not data.list). Key fields: rank, symbol, metaInfo.name, price, percentChange (24h %), percentChange7d, volume (24h USD), liquidity, holders, score (breakout score), impression (views), alphaStatus (1=Alpha listed).

Step 2: Cross-reference with CT Monitor KOL mentions

curl -s "https://api.ctmon.xyz/api/tweets/feed?hours=48&limit=500" \
  -H "Authorization: Bearer $CT_MONITOR_API_KEY" | \
  jq '[.[] | select(.text | test("meme|pepe|wojak|doge|shib|bonk|floki|pump|moon|gem|ape|frog|cat coin|dog coin|memecoin|meme coin"; "i"))] | group_by(.username) | map({kol: .[0].username, count: length, sample: .[0].text[:100]}) | sort_by(-.count) | .[:15]'

Uses hours=48&limit=500 for broader coverage. Expanded meme keywords to catch more variants.

Step 3: Check trending signals for meme tokens

curl -s "https://api.ctmon.xyz/api/signals/recent?hours=24&min_score=0" \
  -H "Authorization: Bearer $CT_MONITOR_API_KEY" | \
  jq '[.[] | select(.keyword | test("PEPE|BONK|FLOKI|DOGE|SHIB|WOJAK|MEME|WIF|POPCAT|BRETT"; "i")) | {keyword, kol_count, kols}]'

Signals use keyword field (e.g. $PEPE), not token_symbol. Filter by keyword with meme token names.

Step 4: Verify on-chain activity via DexScreener

# Replace SYMBOL with top meme token from Step 1 (e.g. PEPE, BONK, WIF)
curl -s "https://api.dexscreener.com/latest/dex/search?q=SYMBOL" | \
  jq '[.pairs[:5][] | {chain: .chainId, dex: .dexId, priceUsd: .priceUsd, volume24h: .volume.h24, liquidity: .liquidity.usd}]'

liquidity.usd >= $100K = safe, < $50K = risky. volume.h24 trend: compare with volume.h6 to detect acceleration.

Synthesis prompt:

You have received four data sources for meme token hunting:

• Source A: Binance Meme Token Ranking — top 20 meme tokens on BNB Chain with social hype scores
• Source B: CT Monitor KOL meme mentions — which KOLs are talking about memes and how often
• Source C: CT Monitor signals — any meme tokens triggering alpha signals
• Source D: DexScreener pairs — on-chain liquidity and volume for top meme candidates

Generate a Markdown-formatted meme hunting report with 4 sections:

🐸 Meme Pulse Leaderboard (from Source A):

Rank	Token	Price	24h Change	Social Hype	KOL Buzz
1	$XXX	$0.0001234	+45.6%	89	🔥 12 KOLs

KOL Buzz: count from Source B; mark 🔥 if >= 5 KOLs mentioned

📣 KOL Meme Sentiment (from Source B):

• List top 5 KOLs discussing memes
• Extract their stance: shilling/neutral/warning
• Highlight any coordinated shill patterns

⚡ Alpha Signals (from Source C):

• Any meme tokens in recent signals?
• Signal score and kol_count
• Actionable timing: early/peak/late

🔍 On-Chain Verification (from Source D):

• Liquidity check: >= $100K = safe, < $50K = risky
• Volume trend: increasing = momentum, decreasing = fading
• Chain distribution: multi-chain = broader exposure

Risk Warning: Meme tokens are high-risk. Never recommend FOMO entry. Always suggest position sizing (≤1% portfolio) and stop-loss levels.

Language rule: Detect the user's language and write the ENTIRE report in that language.

🤖 Automate this combo — meme pulse check every 4 hours:

openclaw cron add \
  --name "CT Meme Hunter" \
  --cron "0 */4 * * *" \
  --tz "Asia/Shanghai" \
  --session isolated \
  --message "Run CT Monitor Combo 9: call Binance Meme Rank API (chainId=56, top 20), scan /tweets/feed?limit=200 for meme keywords (meme|pepe|wojak|doge|shib|bonk|floki), check /signals/recent?hours=6 for meme token signals, verify top 3 candidates on DexScreener. Generate meme hunting report with: (1) Meme Pulse Leaderboard with KOL Buzz column, (2) KOL Meme Sentiment analysis, (3) Alpha Signals for meme tokens, (4) On-Chain Verification with liquidity/volume checks. Include risk warnings. Never fabricate data." \
  --announce \
  --channel telegram

---

Quick API Reference

Action	Endpoint
Market tweet feed	GET /tweets/feed?limit=50
KOL historical tweets	GET /tweets/recent?username=XXX&limit=20
KOL real-time tweets	GET /twitter/realtime?username=XXX&limit=10
Keyword filter	GET /tweets/feed?limit=100 + jq select(.text | contains("keyword"))
Unified news feed	GET /info/feed?limit=30&min_score=0.5
Token price	GET /price/token?symbol=BTC — 3-level fallback (CoinGecko→Binance→DexScreener); returns source, chain, dex, pair_address
Trending tokens	GET /price/trending?hours=6
Market overview	GET /price/summary
Alpha signals	GET /signals/recent?hours=6&min_score=60
AI briefing	GET /brief/generate?hours=24 — returns {"report": "...", "hours": N, ...}; use .report field
KOL ranking	GET /users/top?limit=10
Add to watchlist	POST /subscriptions/?username=pump_fun
Remove from watchlist	DELETE /subscriptions/pump_fun
System status	GET /price/summary
Binance Smart Money Inflow	POST https://web3.binance.com/bapi/defi/v1/public/wallet-direct/buw/wallet/token/inflow/rank/query — smart money net inflow ranking (no auth)

OpenClaw Cron Examples

Use openclaw cron add to schedule any combo as a recurring automated job. All jobs below use --session isolated (dedicated agent turn, no main chat spam) with --announce --channel telegram delivery.

Combo 1 — Daily morning brief (8am Shanghai):

openclaw cron add \
  --name "CT Morning Brief" \
  --cron "0 8 * * *" \
  --tz "Asia/Shanghai" \
  --session isolated \
  --message "Run CT Monitor Combo 1: call /brief/generate?hours=24 (use .report field), /price/trending?hours=24, /signals/recent?hours=6&min_score=60, /price/summary, /info/feed?limit=30 (filter score>=50 sorted by score desc). Synthesize into a Markdown morning report with 6 sections: (1) 📊 Market Overview — copy .report verbatim + append KOL Signal line from signals data; (2) 📰 Key News — use info/feed score>=50 as primary source, format [source] Title → Impact: assessment, cross-ref .report Key News; (3) 🔥 Sector Pulse — table with heating/cooling/stable ratings based on .report + info/feed sector news; (4) 💡 Notable Alpha — use info/feed score>=60 as primary source, format [source] Title → Alpha: insight, cross-ref .report Notable Alpha; (5) 📈 Trending Tokens — list only mention_count>=2 sorted by mention_count desc, mark ⚡ if in signals, add warning for cg_rank<=5 AND mention_count=0; (6) 🎯 DCA 参考信号 — BTC dominance from price/summary.global, DCA recommendation in ≤2 sentences. Use price_change field (not price_change_24h). Never fabricate source names." \
  --announce \
  --channel telegram

Combo 2 — Alpha signal alert (every 15 min, conditional):

openclaw cron add \
  --name "CT Signal Alert" \
  --cron "*/15 * * * *" \
  --session isolated \
  --message "Call CT Monitor /signals/recent?hours=0.25&min_score=60. If any signal has kol_count >= 3, run the full Combo 2 deep dive on that token (price + KOL tweets + news) and send an alert. If no qualifying signals, stay silent." \
  --announce \
  --channel telegram

Combo 4 — Security watch (every 15 min, conditional):

openclaw cron add \
  --name "CT Security Watch" \
  --cron "*/15 * * * *" \
  --session isolated \
  --message "Call CT Monitor /tweets/feed?limit=100 and filter for hack/exploit/rug/drain/emergency/pause/vulnerability. Also check /info/feed?limit=30 for security news. If 3+ KOLs mention the same security event, run the full Combo 4 analysis and send an URGENT alert. If nothing found, stay silent." \
  --announce \
  --channel telegram

Combo 5 — Narrative pulse (daily 8pm):

openclaw cron add \
  --name "CT Narrative Pulse" \
  --cron "0 20 * * *" \
  --tz "Asia/Shanghai" \
  --session isolated \
  --message "Run CT Monitor Combo 5: scan /tweets/feed?limit=3000 for sector keywords (agent, AI, RWA, DePIN, meme, Solana, stablecoin, DeFi, NFT, restaking, BTCFi, GameFi) — for 'agent' keyword exclude non-crypto contexts (real estate/travel/insurance/FBI agents). Check /signals/recent?hours=24&min_score=50. Check /price/trending?hours=24 for mention_count>=2 tokens, then query Binance spot ticker/24hr for those tokens (append USDT suffix). Call Binance Social Hype API (chainId=56, pageSize=20, extract top 10). Generate narrative heat ranking table with Social Hype column (🔥 if token in narrative appears in Social Hype Top 10), four-layer signal interpretation matrix (Twitter Hype/Smart Money/Volume/Social Hype), price validation, overheating warnings, and emerging narrative alerts." \
  --announce \
  --channel telegram

Combo 6 — Airdrop hunter (daily 7am):

openclaw cron add \
  --name "CT Airdrop Hunter" \
  --cron "0 7 * * *" \
  --tz "Asia/Shanghai" \
  --session isolated \
  --message "Run CT Monitor Combo 6: scan /tweets/feed for airdrop/snapshot/TGE/unlock/claim/whitelist/mint keywords, check /info/feed for event news, check /signals/recent?hours=24. Generate an event list sorted by urgency with participation value assessment and action checklist." \
  --announce \
  --channel telegram

Combo 7 — Whale watch (daily noon):

openclaw cron add \
  --name "CT Whale Watch" \
  --cron "0 12 * * *" \
  --tz "Asia/Shanghai" \
  --session isolated \
  --message "Run CT Monitor Combo 7: get /users/top?limit=20 (Source A), call Binance Trading Signal smart-money API (Source B), call Binance Smart Money Inflow API (Source C), call Binance Top Trader PnL API (Source D), cross-reference top inflow tokens with CT Monitor /tweets/feed to count KOL mentions (Source E). Generate 5-section report: (1) 聪明钱净流入 Top 10 with KOL Mentions column (🔥 if >=5), (2) 聪明钱建仓信号 (BUY>SELL tokens, highlight ratio>3:1), (3) 聪明钱出货警告 (SELL>BUY tokens, warn ratio>3:1), (4) Top Trader 持仓动向 (top 5 traders + shared positions), (5) KOL × 聪明钱共振 (tokens in both inflow top 10 AND KOL mentions >=5). Use exact field names from APIs. Never fabricate data." \
  --announce \
  --channel telegram

Combo 8 — Sector rotation (weekly Sunday 9pm):

openclaw cron add \
  --name "CT Sector Rotation" \
  --cron "0 21 * * 0" \
  --tz "Asia/Shanghai" \
  --session isolated \
  --message "Run CT Monitor Combo 8: compare /price/trending?hours=24 vs hours=168 (Source A), compare /signals/recent?hours=6 vs hours=24 (Source B), scan /info/feed for sector media attention (Source C), call Binance Smart Money Inflow API pageSize=50 (Source D). Generate sector heat change matrix with 聪明钱流向 column (🔥Inflow if 3+ tokens in sector in Top 30, —Neutral if 1-2, 📤Outflow if 0 but appeared before), rotation direction judgment, early/late-stage identification, and reallocation suggestions." \
  --announce \
  --channel telegram

Manage your jobs:

openclaw cron list
openclaw cron runs --id <job-id>
openclaw cron edit <job-id> --message "Updated prompt"
openclaw cron remove <job-id>

Pricing Reference

Endpoint	Cost	Notes
/signals/recent hours<2	3¢	Real-time data (6551 source)
/signals/recent hours≥2	1¢	Internal historical database
/twitter/realtime	2¢	Real-time tweets (6551 source)
/brief/generate hours=1	6¢	1H flash briefing (Grok 4.1 Fast)
/brief/generate hours=8	4¢	8H briefing
/brief/generate hours=12/24	2¢	12/24H briefing
/info/feed	1¢	Unified news + RSS feed
/price/token	1¢	Token price query — 3-level fallback: CoinGecko→Binance→DexScreener; new fields: source (data origin), chain, dex, pair_address (DexScreener only)
/price/trending	1¢	Trending token analysis
/price/summary	1¢	Market overview

Error Handling

• API returns []: Inform user "no data available, sync may still be in progress"
• API returns 401: Invalid API Key — check $CT_MONITOR_API_KEY environment variable
• API returns 402: Insufficient balance — top up required
• API returns 404: User not in watchlist — suggest adding subscription first
• Network timeout: Retry once; if still failing, ask user to try again later