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coinglass▌

starchild-ai-agent/official-skills · updated Apr 8, 2026

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$npx skills add https://github.com/starchild-ai-agent/official-skills --skill coinglass
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skill.md

Coinglass

Coinglass provides the most comprehensive crypto derivatives and institutional data available. 37 tools covering futures positioning, whale tracking, volume analysis, liquidations, and ETF flows.

API Plan: Professional ($699/month) Rate Limit: 6000 requests/minute API Version: V4 (with V2 backward compatibility) Total Tools: 37 across 8 categories

Tool Selection Guide

Decision Tree

Step 1: Is this about LIQUIDATIONS?

Liquidation query?
├─ YES → How many coins?
│   ├─ ALL coins / ranking / 排行 / 汇总
│   │   └─ → cg_liquidation_coin_list  ✅ (most liquidation queries land here)
│   ├─ ONE coin, need history over time
│   │   └─ → cg_coin_liquidation_history
│   ├─ ONE coin, specific orders (price/side/USD)
│   │   └─ → cg_liquidation_orders
│   └─ ONE coin, just a quick total + sentiment label
│       └─ → cg_liquidation_analysis  (rarely needed; only if explicitly "simple summary")

Step 2: Is this about LONG/SHORT RATIO?

Long/short query?
├─ Historical time-series, trend over time, 多空比变化
│   └─ → cg_global_account_ratio  (ALL accounts)
│      or cg_top_account_ratio    (top traders only)
│      or cg_top_position_ratio   (by position size)
└─ Current snapshot only (no history needed)
    └─ → long_short_ratio

Step 3: Is this about OPEN INTEREST?

OI query?
└─ → cg_open_interest  (always — do NOT use cg_coins_market_data for OI)

Step 4: Is this a MARKET OVERVIEW / SENTIMENT query?

Sentiment / 市场情绪 / pre-trade check?
└─ Use: funding_rate + long_short_ratio + cg_open_interest
   DO NOT use cg_coins_market_data as a substitute for any of the above

Keyword → Tool Lookup

Keyword / Pattern Correct Tool ❌ Do NOT use
爆仓排行 / 今日爆仓 / all coins liquidation cg_liquidation_coin_list cg_liquidations
24h爆仓汇总 / liquidation summary cg_liquidation_coin_list cg_liquidation_analysis
全网账户多空比 / account L/S ratio cg_global_account_ratio long_short_ratio
头部交易者多空 / top trader ratio cg_top_account_ratio long_short_ratio
未平仓合约 / open interest cg_open_interest cg_coins_market_data
市场情绪多空分析 funding_rate + long_short_ratio + cg_open_interest cg_coins_market_data
BTC做多检查 / pre-trade checklist funding_rate + cg_global_account_ratio + cg_liquidation_coin_list —

Common Mistakes

Mistake 1 (most common — 8x failure): Using cg_liquidations when you need cg_liquidation_coin_list

  • cg_liquidations → one coin, one timeframe, basic total only
  • cg_liquidation_coin_list(exchange) → ALL coins, multi-timeframe (1h/4h/12h/24h), per-exchange breakdown
  • Rule: If the question asks for a ranking, overview, or doesn't specify a single coin → use cg_liquidation_coin_list

Mistake 2 (5x failure): Using cg_liquidation_analysis for liquidation rankings

  • cg_liquidation_analysis adds a sentiment label to a single-coin total — it is NOT a ranking tool
  • Rule: "今日爆仓排行" / "各币种爆仓" → always cg_liquidation_coin_list

Mistake 3 (3x failure): Using long_short_ratio for historical L/S analysis

  • long_short_ratio is a current snapshot (no time-series)
  • cg_global_account_ratio returns history — use it when the user wants trends or comparison over time
  • Rule: If the question compares 全网 (global) vs 头部 (top traders) → call BOTH cg_global_account_ratio AND cg_top_account_ratio

Mistake 4 (2x failure): Using cg_coins_market_data for open interest

  • cg_coins_market_data is a bulk snapshot of many coins — not a replacement for dedicated OI or L/S tools
  • Rule: OI question → cg_open_interest. L/S question → long_short_ratio or cg_global_account_ratio. Never route either to cg_coins_market_data.

Rules

Tool Call Guidance

❌ FORBIDDEN TOOLS — NEVER USE:

  • bash — Do NOT write scripts to process/format data. Use natural language.
  • write_file / read_file / edit_file — Do NOT save intermediate data. Answer directly.
  • learning_log — ONLY for genuine skill bugs or persistent API errors. NOT for empty responses.
  • echo — Do NOT use for debugging or output.

✅ CORRECT PATTERN:

  • Tool returns data → Summarize in natural language → Done
  • Tool returns empty/null → Report "no data available" → Done
  • Need calculation (%, change, ratio) → Do mental math in reply

Match tool count to question scope:

  • 单一指标问题（"BTC 资金费率"、"ETH 多空比"）→ 1 个工具，直接返回
  • 多维度分析（"做多是否合适"、"衍生品体检"）→ 3-5 个工具，综合分析
  • 对比问题（"ETH vs SOL"）→ 每个币种调相同工具，并列对比
  • 避免重复调用同一工具。 除非用户明确要求不同币种/交易所的对比。

Learning Log Usage (CRITICAL)

learning_log is FORBIDDEN for:

  • ❌ Empty API responses — just report "no data available"
  • ❌ Tool returning None/null — handle gracefully
  • ❌ Uncertainty about tool selection — check decision tree first
  • ❌ Normal tool errors — retry once, then report failure

learning_log is ONLY for:

  • ✅ Genuine bugs in skill code (wrong data format returned)
  • ✅ Persistent API rate limit errors after 2+ retries
  • ✅ Missing tools that should exist per skill definition

ETF Tool Selection

Query Primary Tool Secondary Tool
BTC ETF 资金流入/流出 cg_btc_etf_flows() cg_btc_etf_history() for detailed history
ETH ETF 资金流入/流出 cg_eth_etf_flows() —
SOL/XRP ETF flows cg_sol_etf_flows() / cg_xrp_etf_flows() —
HK ETF flows cg_hk_btc_etf_flows() / cg_hk_eth_etf_flows() —
ETF 列表/代码 cg_btc_etf_list() / cg_eth_etf_list() —
ETF 溢价/折价 cg_btc_etf_premium_discount() —

ETF 对比问题 workflow:

# BTC vs ETH ETF 对比
btc = cg_btc_etf_flows()
eth = cg_eth_etf_flows()
# Compare the latest day's net flows, summarize in 2-3 sentences

Quick Routing (use this first)

Query type Tool
爆仓/liquidation summary (24h, by coin) cg_liquidation_coin_list
Individual liquidation orders cg_liquidation_orders
Liquidation history for a coin cg_coin_liquidation_history
Funding rate funding_rate
Long/short ratio (global) cg_global_account_ratio
Open interest cg_open_interest
Whale activity on Hyperliquid cg_hyperliquid_whale_alerts
ETF flows (BTC) cg_btc_etf_flows

When to Use Coinglass

Use Coinglass for:

  • Derivatives positioning - What are leveraged traders doing?
  • Whale tracking - Track large positions on Hyperliquid DEX
  • Funding rates - Cost of holding perpetual futures
  • Open interest - Total notional value of open positions
  • Long/Short ratios - Sentiment among leveraged traders (global, top accounts, top positions)
  • Liquidations - Forced position closures with heatmaps and individual orders
  • Volume analysis - Taker volume, CVD, netflow patterns
  • ETF flows - Institutional adoption (Bitcoin, Ethereum, Solana, XRP, Hong Kong)
  • Whale transfers - Large on-chain movements (>$10M)
  • Futures market data - Supported coins, exchanges, pairs, and OHLC price history

Tool Categories

1. Basic Derivatives Analytics (7 tools)

Core derivatives data for market analysis:

  • funding_rate(symbol, exchange?) - Current funding rates
  • long_short_ratio(symbol, exchange?, interval?) - Basic L/S ratios
  • cg_open_interest(symbol) - Current OI across exchanges
  • cg_liquidations(symbol, time?) - Recent liquidations
  • cg_liquidation_analysis(symbol) - Liquidation heatmap analysis
  • cg_supported_coins() - All supported coins
  • cg_supported_exchanges() - All exchanges with pairs

2. Advanced Long/Short Ratios (6 tools)

Deep positioning analysis with multiple metrics:

  • cg_global_account_ratio(symbol, interval?) - Global account-based L/S ratio
  • cg_top_account_ratio(symbol, exchange, interval?) - Top trader accounts ratio
  • cg_top_position_ratio(symbol, exchange, interval?) - Top positions by size
  • cg_taker_exchanges(symbol) - Taker buy/sell by exchange
  • cg_net_position(symbol, exchange) - Net long/short positions
  • cg_net_position_v2(symbol) - Enhanced net position data

Use cases:

  • Smart money tracking (top accounts vs retail)
  • Exchange-specific sentiment
  • Position size distribution analysis

3. Advanced Liquidations (4 tools)

Granular liquidation tracking for cascade prediction:

  • cg_coin_liquidation_history(symbol, interval?, limit?, start_time?, end_time?) - Aggregated across all exchanges
  • cg_pair_liquidation_history(symbol, exchange, interval?, limit?, start_time?, end_time?) - Exchange-specific pair
  • cg_liquidation_coin_list(exchange) - All coins on an exchange
  • cg_liquidation_orders(symbol, exchange, min_liquidation_amount, start_time?, end_time?) - Individual orders (past 7 days, max 200)

Use cases:

  • Identifying liquidation clusters
  • Tracking liquidation patterns over time
  • Finding large liquidation events

4. Hyperliquid Whale Tracking (4 tools)

Track large traders on Hyperliquid DEX (~200 recent alerts):

  • cg_hyperliquid_whale_alerts() - Recent large position opens/closes (>$1M)
  • cg_hyperliquid_whale_positions() - Current whale positions with PnL
  • cg_hyperliquid_positions_by_coin() - All positions grouped by coin
  • cg_hyperliquid_position_distribution() - Distribution by size with sentiment

Use cases:

  • Following smart money on Hyperliquid
  • Detecting large position changes
  • Tracking whale PnL and sentiment

5. Futures Market Data (5 tools)

Market overview and price data:

  • cg_coins_market_data() - ALL coins data in one call (100+ coins)
  • cg_pair_market_data(symbol, exchange) - Specific pair metrics
  • cg_ohlc_history(symbol, exchange, interval, limit?) - OHLC candlesticks
  • cg_taker_volume_history(symbol, exchange, interval, limit?, start_time?, end_time?) - Pair-specific taker volume
  • cg_aggregated_taker_volume(symbol, interval, limit?, start_time?, end_time?) - Aggregated across exchanges

Use cases:

  • Market screening (scan all coins at once)
  • Price action analysis
  • Volume pattern recognition

6. Volume & Flow Analysis (4 tools)

Order flow and capital movement tracking:

  • cg_cumulative_volume_delta(symbol, exchange, interval, limit?, start_time?, end_time?) - CVD = Running total of (buy - sell)
  • cg_coin_netflow() - Capital flowing into/out of coins
  • cg_whale_transfers() - Large on-chain transfers (>$10M, past 6 months)

Use cases:

  • Order flow divergence detection
  • Smart money tracking
  • Institutional movement monitoring

7. Bitcoin ETF Data (5 tools)

Track institutional Bitcoin adoption:

  • cg_btc_etf_flows() - Daily net inflows/outflows
  • cg_btc_etf_premium_discount() - ETF price vs NAV
  • cg_btc_etf_history() - Comprehensive history (price, NAV, premium%, shares, assets)
  • cg_btc_etf_list() - List of Bitcoin ETFs
  • cg_hk_btc_etf_flows() - Hong Kong Bitcoin ETF flows

Use cases:

  • Institutional demand tracking
  • Premium/discount arbitrage
  • Regional flow comparison (US vs Hong Kong)

8. Other ETF Data (8 tools)

Ethereum, Solana, XRP, and Hong Kong ETFs:

  • cg_eth_etf_flows() - Ethereum ETF flows
  • cg_eth_etf_list() - Ethereum ETF list
  • cg_eth_etf_premium_discount() - ETH ETF premium/discount
  • cg_sol_etf_flows() - Solana ETF flows
  • cg_sol_etf_list() - Solana ETF list
  • cg_xrp_etf_flows() - XRP ETF flows
  • cg_xrp_etf_list() - XRP ETF list
  • cg_hk_eth_etf_flows() - Hong Kong Ethereum ETF flows

Use cases:

  • Multi-asset institutional tracking
  • Comparative flow analysis
  • Regional preference analysis

Common Workflows

Quick Market Scan

# Get everything in 3 calls
all_coins = cg_coins_market_data()  # 100+ coins with full metrics
btc_liquidations = cg_liquidations("BTC")
whale_alerts = cg_hyperliquid_whale_alerts()

Deep Position Analysis

# BTC positioning across metrics
cg_global_account_ratio("BTC")  # Retail sentiment
cg_top_account_ratio("BTC", "Binance")  # Smart money
cg_net_position_v2("BTC")  # Net positioning
cg_liquidation_heatmap("BTC", "Binance")  # Cascade levels

ETF Flow Monitoring

# Institutional demand
btc_flows = cg_btc_etf_flows()
eth_flows = cg_eth_etf_flows()
sol_flows = cg_sol_etf_flows()

Whale Tracking

# Follow the whales
hyperliquid_whales = cg_hyperliquid_whale_alerts()
whale_positions = cg_hyperliquid_whale_positions()
onchain_whales = cg_whale_transfers()  # >$10M on-chain

Volume Analysis

# Order flow
cvd = cg_cumulative_volume_delta("BTC", "Binance", "1h", 100)
netflow = cg_coin_netflow()  # All coins
taker_vol = cg_aggregated_taker_volume("BTC", "1h", 100)

Interpretation Guides

Funding Rates

Rate (8h) Read
> +0.05% Extreme greed — crowded long, squeeze risk
+0.01% to +0.05% Bullish bias, normal
-0.005% to +0.01% Neutral
-0.05% to -0.005% Bearish bias, normal
< -0.05% Extreme fear — crowded short, bounce risk

Extreme funding often precedes reversals. The crowd is usually wrong at extremes.

Open Interest + Price Matrix

OI Price Read
Up Up New longs entering — bullish conviction
Up Down New shorts entering — bearish conviction
Down Up Short covering — weaker rally, less conviction
Down Down Long liquidation — weaker selloff, capitulation

Long/Short Ratio

Ratio Read
> 1.5 Crowded long — contrarian bearish
1.1–1.5 Moderately bullish
0.9–1.1 Balanced
0.7–0.9 Moderately bearish
< 0.7 Crowded short — contrarian bullish

CVD (Cumulative Volume Delta)

Pattern Read
CVD rising, price rising Strong buy pressure, healthy uptrend
CVD falling, price rising Weak rally, distribution
CVD rising, price falling Accumulation, potential bottom
CVD falling, price falling Strong sell pressure, healthy downtrend

ETF Flows

Flow Read
Large inflows Institutional buying, bullish
Consistent inflows Sustained demand
Large outflows Institutional selling, bearish
Premium to NAV High demand, bullish sentiment
Discount to NAV Weak demand, bearish sentiment

Analysis Patterns

Multi-metric confirmation: Combine tools across categories for high-confidence signals:

  • Funding + L/S ratio + liquidations = positioning extremes
  • CVD + taker volume + whale alerts = smart money direction
  • ETF flows + whale transfers + open interest = institutional conviction

Smart money vs retail: Compare metrics to identify divergence:

  • cg_top_account_ratio (smart money) vs cg_global_account_ratio (retail)
  • Hyperliquid whale positions vs overall long/short ratios

Cascade prediction: Use liquidation tools to predict volatility:

  • cg_coin_liquidation_history shows liquidation patterns over time
  • cg_liquidation_orders reveals recent forced closures
  • Large liquidation events = cascade risk zones

Flow divergence: Track capital movements:

  • cg_coin_netflow shows where money is flowing
  • cg_whale_transfers reveals large movements
  • ETF flows show institutional demand

Performance Optimization

Batch vs Individual Calls

✅ OPTIMAL: Use batch endpoints

# One call gets 100+ coins
all_coins = cg_coins_market_data()

# One call gets all whale alerts
whales = cg_hyperliquid_whale_alerts()

# One call gets all ETF flows
btc_etf = cg_btc_etf_flows()

❌ INEFFICIENT: Multiple individual calls

# Don't do this - wastes API quota
btc = cg_pair_market_data("BTC", "Binance")
eth = cg_pair_market_data("ETH", "Binance")
sol = cg_pair_market_data("SOL", "Binance")

Query Parameters

Most history endpoints support:

  • interval: Time granularity (1h, 4h, 12h, 24h, etc.)
  • limit: Number of records (default varies, max 1000)
  • start_time: Unix timestamp (milliseconds)
  • end_time: Unix timestamp (milliseconds)

Example:

cg_coin_liquidation_history(
    symbol="BTC",
    interval="1h",
    limit=100,
    start_time=1704067200000,  # 2024-01-01
how to use coinglass

How to use coinglass on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • ›Cursor installed and configured on your development machine
  • ›Node.js version 16.0+ with npm package manager (verify with node --version)
  • ›Active project directory or workspace where you want to add coinglass
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/starchild-ai-agent/official-skills --skill coinglass

The skills CLI fetches coinglass from GitHub repository starchild-ai-agent/official-skills and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/coinglass

Reload or restart Cursor to activate coinglass. Access the skill through slash commands (e.g., /coinglass) or your agent's skill management interface.

⚠

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

Additional Resources

›View source code on GitHub›Skills CLI documentation›Learn more about Cursor›What are agent skills?

List & Monetize Your Skill

Submit your Claude Code skill and start earning

GET_STARTED →

Use Cases▌

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

✓

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

✓

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

✓

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft PRDs, status updates, and stakeholder presentations

Example

Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement

✓

Save 3-5 hours/week on communication overhead

Implementation Guide▌

Prerequisites

  • ›Claude Desktop or compatible AI client
  • ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • ›Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • ⚠Not validating competitive research—verify facts before sharing
  • ⚠Accepting user stories without involving engineering team
  • ⚠Over-relying on frameworks without qualitative judgment
  • ⚠Not customizing outputs to company culture and communication style
  • ⚠Skipping stakeholder validation of generated requirements

Best Practices▌

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • −Don't publish competitive analysis without fact-checking
  • −Don't finalize user stories without engineering review
  • −Don't make prioritization decisions solely on AI scoring
  • −Don't skip customer validation of generated requirements
  • −Don't ignore company-specific context and culture

💡 Pro Tips

  • ★Provide context: company goals, constraints, customer feedback
  • ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • ★Use skill for 70% generation + 30% customization to company needs

When to Use This▌

✓ Use When

Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.

✗ Avoid When

Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.

Learning Path▌

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.6★★★★★57 reviews
  • ★★★★★Kiara Sethi· Dec 28, 2024

    coinglass reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • ★★★★★Chen Martinez· Dec 24, 2024

    coinglass fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • ★★★★★Anika Diallo· Dec 20, 2024

    We added coinglass from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • ★★★★★Amelia White· Dec 8, 2024

    Useful defaults in coinglass — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • ★★★★★Aarav Sharma· Dec 4, 2024

    coinglass has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • ★★★★★Layla Reddy· Dec 4, 2024

    Solid pick for teams standardizing on skills: coinglass is focused, and the summary matches what you get after install.

  • ★★★★★Dev Rao· Nov 27, 2024

    I recommend coinglass for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • ★★★★★Aarav Torres· Nov 23, 2024

    Registry listing for coinglass matched our evaluation — installs cleanly and behaves as described in the markdown.

  • ★★★★★Arjun Bansal· Nov 19, 2024

    We added coinglass from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • ★★★★★Chen Iyer· Nov 11, 2024

    coinglass reduced setup friction for our internal harness; good balance of opinion and flexibility.

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