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ct-alpha

@sendaifun · 收录于 1 周前

Crypto Twitter intelligence and alpha research. Search X/Twitter for real-time crypto narratives, trending tokens, yield strategies, smart money signals, and protocol research. Features TweetRank (PageRank-inspired credibility scoring), multi-signal token detection, coordinated raid detection, and dynamic tool discovery for execution suggestions. Solana-first but covers all major chains.

适合你,如果需要在X/Twitter上挖掘加密Alpha和实时叙事

/ 下载安装
ct-alpha.skill双击,或拖进 Claude 桌面版 / Cowork,即完成安装↓ .skill↓ .zip
用别的 agent?下载 .zip 解压,把文件夹放进它的技能目录
Claude Code~/.claude/skills/(项目级 .claude/skills/)
Codex CLI~/.codex/skills/
Cursor自动读取上面两处目录
其他工具见其文档的「skills」目录;两个下载是同一份文件,只是名字不同
/ 通过 npx 安装 校验哈希
npx oh-my-skill add sendaifun/skills/ct-alpha
/ 通过 bash 安装
curl -fsSL https://oh-my-skill.com/install.sh | bash -s -- sendaifun/skills/ct-alpha
/ 已经装过?验证本机副本,不用重装
npx oh-my-skill verify sendaifun/skills/ct-alpha
安装目标可用 --agent / --scope 或 --to 明确指定;省略时只会在唯一已存在的 agent 目录上自动选择,零命中或多命中会停止并提示。content_hash 缺失或不一致均拒装。
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怎么用

技能原文 SKILL.md作者撰写 · Apache-2.0 · 064d4de

CT Alpha — Crypto Twitter Intelligence

Turn X/Twitter into an actionable crypto intelligence layer. Search CT for narratives, alpha, strategies, and sentiment, then rank results using TweetRank (a PageRank-inspired credibility scoring system), extract tokens/CAs from multiple signals, detect coordinated raids, and suggest execution steps using available tools.

Overview
  • Search: Query CT with crypto-optimized noise filters and relevancy sorting
  • TweetRank: Score tweets by author credibility + engagement quality + recency
  • Multi-Signal Token Detection: Cashtags, name-phrases, crypto URLs, contract addresses
  • Raid Detection: Flag tickers promoted mostly by low-credibility accounts
  • Trending: Detect trending tokens across multiple search queries
  • Watchlist: Monitor trusted CT accounts by category
  • Thread Hydration: Fetch full conversation threads
  • X Articles: Full-text extraction of long-form posts (>280 chars)
  • Cost Tracking: Per-session API spend tracking (~$0.005/tweet)
  • Dynamic Tool Discovery: Suggest follow-up actions with DeFi Llama, Backpack, Polymarket, etc.
Prerequisites
  • Runtime: Bun (TypeScript runtime)
  • API Token: X API Bearer Token from developer.x.com (pay-per-use via xAI)
  • Cost: ~$0.005 per tweet read, ~$0.10 per quick search (20 tweets)
Quick Start
Installation
# Clone the skill
git clone https://github.com/yashhsm/skills.git
cd skills/skills/ct-alpha

# Run the installer (configures token, watchlist, cache)
bun run install.ts
Environment Setup
# Set your X API Bearer Token
export X_BEARER_TOKEN="your_token_here"

# Or save to persistent env file
mkdir -p ~/.config/env
echo 'export X_BEARER_TOKEN="your_token_here"' >> ~/.config/env/global.env
source ~/.config/env/global.env
Basic Usage
# Search for alpha on a token
bun run ct-search.ts search "$SOL alpha" --quick

# Detect trending tokens
bun run ct-search.ts trending --window 6h --solana-only

# Monitor watchlist accounts
bun run ct-search.ts watchlist --since 24h

# Read a specific tweet or article
bun run ct-search.ts read https://x.com/user/status/123456

# Check API spending
bun run ct-search.ts cost
CLI Reference
search — Core research command
bun run ct-search.ts search "<query>" [flags]

| Flag | Description | Default | |------|-------------|---------| | --quick | 20 tweets, 1hr cache, ~$0.10 | Default mode | | --full | Up to 100 tweets, 15min cache, ~$0.50 | Confirm cost first | | --limit N | Max total tweets | 20 (quick), 100 (full) | | --sort <field> | likes, recency, relevancy | relevancy | | --since <duration> | 1h, 6h, 24h, 7d | 24h | | --min-likes N | Engagement filter | 3 (quick) | | --from user1,user2 | Restrict to specific accounts | — | | --extract-tickers | Show extracted tickers | — | | --extract-cas | Show contract addresses and crypto URLs | — | | --raw | JSON output | — |

trending — Multi-signal trending detection
bun run ct-search.ts trending [flags]

| Flag | Description | Default | |------|-------------|---------| | --window <duration> | 1h, 6h, 24h | 6h | | --min-mentions N | Minimum mention count | 3 | | --solana-only | Solana ecosystem only | — | | --top N | Top N results | 20 |

watchlist — Monitor CT accounts
bun run ct-search.ts watchlist [flags]

| Flag | Description | Default | |------|-------------|---------| | --category <cat> | Filter by category | all | | --since <duration> | Time window | 24h |

read — Read a specific tweet/article
bun run ct-search.ts read <tweet_url_or_id> [--thread] [--raw]

Accepts x.com URLs, twitter.com URLs, or raw tweet IDs. Articles (long-form posts) are fetched in full.

thread — Hydrate conversation thread
bun run ct-search.ts thread <tweet_id>
cost — Track API spending
bun run ct-search.ts cost [--reset]
Core Features
TweetRank Scoring

Every tweet is scored by three factors multiplied together:

  1. AuthorCred (0-10): Watchlist membership (+5), follower/following ratio (capped +5), verification (+1), account age (log scale, capped +2), bot penalty (-3)
  2. EngagementQuality: Bookmarks (×3, unfakeable) > Quotes (×2.5, high effort) > Likes (×1.5) > Retweets (×1, easily botted). All log-scaled.
  3. RecencyBoost: 1 / (1 + hoursAgo / 24) — newer tweets score higher.
TweetRank = AuthorCred × EngagementQuality × RecencyBoost

Each tweet receives a source label:

  • [WATCHLIST] — Author is on your watchlist (highest trust)
  • [HIGH-CRED] — AuthorCred ≥ 5
  • [UNKNOWN] — Unverified author
  • [SUSPICIOUS] — Bot-like patterns detected
Multi-Signal Token Detection

Cashtag-only detection misses how tokens actually spread on CT. This extracts from four signal types:

| Signal | Example | Confidence | |--------|---------|------------| | Cashtag | $SOL, $JTO | High | | Name-phrase | "pendle" + crypto context | High/Medium | | Crypto URL | pump.fun, dexscreener, birdeye, jup.ag | High | | Contract Address | Base58 (Solana) / 0x (Ethereum) with context | High/Low |

Supported crypto URL domains: pump.fun, dexscreener.com, birdeye.so, jup.ag, raydium.io, solscan.io, etherscan.io.

Raid Detection

Detects coordinated pump campaigns by analyzing author credibility distribution per ticker:

  • If >70% of authors mentioning a ticker have low credibility (AuthorCred < 3), it's flagged as a potential raid
  • Output includes raid score, total/low-cred author counts
Noise Filtering

Every search auto-appends crypto noise filters:

  • -is:retweet (removes retweets)
  • -"airdrop", -"giveaway", -"whitelist" (spam removal)
  • -"follow and RT", -"follow & RT", -"free mint", -"dm to claim" (engagement bait)
  • Quick mode also adds -is:reply
Caching

Aggressive caching prevents redundant API calls:

| Cache Type | TTL | Use Case | |------------|-----|----------| | Quick search | 1 hour | Default searches | | Full search | 15 minutes | Deep dives | | Thread | 2 hours | Conversation threads | | Profile | 24 hours | User lookups | | Watchlist | 4 hours | Account monitoring |

Cache is file-based (JSON) with auto-pruning on startup (24h hard limit).

Research Methodology

Follow this 6-step loop for every research request:

1. Decompose

Break the user's question into 1-3 targeted search queries.

  • Token research: search both $TICKER and plain name with OR
  • Narratives: search thematic keywords, not just token names
  • Strategies: include strategy/yield/APY keywords
2. Pre-Filter

Before any API call:

  • Check cache (same query within TTL is free)
  • Noise filters are automatic
  • Estimate cost: Quick ~$0.10, Full ~$0.50-1.50
  • Narrow time window: 24h for trending, 7d for research
3. Search

Execute with --quick mode first (always):

bun run ct-search.ts search "$TOKEN alpha" --quick --extract-tickers
4. Extract

Analyze TweetRank scores and trust labels. Look for extracted tickers, contract addresses, and crypto URLs.

5. Deep-Dive (if needed)
  • Follow high-engagement threads: bun run ct-search.ts thread <id>
  • Search specific authors: --from author1,author2
  • Broaden with --full only if quick was insufficient
6. Synthesize

Combine findings into actionable intelligence:

  • Group by theme, not by query
  • Highlight tickers with strong multi-signal detection
  • Flag raid risks
  • Suggest verification and execution steps
Query String Rules

The query argument supports X API v2 operators:

| Operator | Example | Description | |----------|---------|-------------| | keyword | solana alpha | Both words | | "exact" | "yield strategy" | Exact phrase | | OR | $SOL OR solana | Either term | | - | -airdrop | Exclude term | | from: | from:username | Tweets by user | | has:links | has:links | Tweets with URLs | | lang: | lang:en | Language filter | | $ | $SOL | Cashtag |

Do NOT use these v1.1 operators (they cause 400 errors on v2 pay-per-use):

  • min_faves:N, min_retweets:N — use --min-likes CLI flag instead
  • place:, bio:, sample: — not available on v2

Do NOT manually include noise filters in the query — the CLI auto-appends them.

Dynamic Tool Discovery

After completing research, suggest execution steps using available MCP tools:

| Tool Prefix | Use Case | Example | |-------------|----------|---------| | mcp__defillama__* | TVL, yields, fees, prices | get_protocol_tvl("pendle") | | mcp__backpack__* | Exchange price, depth, trades | backpack_get_ticker("SOL_USDC") | | mcp__polymarket__* | Prediction markets | search_polymarket("solana ETF") | | mcp__coingecko__* | Token data, market charts | get_id_coins("solana") |

Always frame suggestions as "verify" not "confirm" — encourage skepticism about CT alpha.

Cost Protocol
  1. Always --quick first (~$0.10 for 20 tweets). Relevancy sort = best results come first.
  2. Only increase --limit if 20 results are genuinely insufficient.
  3. Display cost estimate before --full mode.
  4. Cache is aggressive — same query within TTL is free.
  5. Two-pass strategy: First search 20 results. If more depth needed on a sub-topic, do a targeted follow-up rather than re-running with higher limits.
Best Practices
  • Start narrow, broaden only if needed: Specific ticker + context words first
  • Use --from for signal: Restrict to watchlist accounts for highest signal-to-noise
  • Use has:links for substance: Analytical content, not hot takes
  • Never present CT findings as authoritative: Always include confidence levels and risk bullets
  • Contract addresses are always UNVERIFIED: Verify on-chain before interacting
  • Two-pass research: Quick search first, then targeted deep-dives
  • Track spending: Use cost command to monitor API usage
Security Considerations
  • X Bearer Token is stored in ~/.config/env/global.env — ensure proper file permissions
  • Never commit tokens to version control
  • Contract addresses extracted from tweets are ALWAYS unverified — always verify on-chain
  • Watchlist data is local-only (not synced or shared)
  • Cache files contain tweet data — consider cleanup for sensitive research
Skill Structure
ct-alpha/
├── SKILL.md                    # This file — agent instructions
├── ct-search.ts                # Main CLI entry point
├── setup.ts                    # Interactive setup script
├── install.ts                  # Full installer
├── lib/
│   ├── api.ts                  # X API v2 integration, pagination, caching
│   ├── extract.ts              # Multi-signal token extraction
│   ├── tweetrank.ts            # TweetRank scoring and raid detection
│   ├── format.ts               # Output formatting with trust labels
│   ├── cache.ts                # File-based caching layer
│   ├── cost.ts                 # API cost tracking
│   └── filters.ts              # Noise filtering, engagement filtering
├── resources/
│   ├── x-api.md                # X API v2 reference
│   ├── query-templates.md      # Pre-built search patterns
│   └── tool-discovery.md       # Dynamic tool suggestion map
├── data/
│   ├── known-tokens.json       # Token name → ticker mappings
│   └── watchlist.default.json  # Default watchlist categories
└── examples/
    └── basic-search.ts         # Quick start example
Resources
按 Apache-2.0 许可原样转载,未经改动 · 在 GitHub 查看 →

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