crypto-token-analysis skill

Crypto Token Deep Analysis Framework

Systematic approach for analyzing crypto tokens with focus on profit opportunities and risk avoidance. Used when user requests token analysis, investment research, or airdrop evaluation.

When to Use

• User asks to analyze a specific token (e.g., "$HPL analysis")
• Requests for "deep dive", "fundamental analysis", or "is X token good?"
• Airdrop opportunity evaluation (token OR project-level)
• Risk assessment before investment
• Comparing tokens in an ecosystem
• Analyzing crypto projects/protocols for airdrop potential (AI agents, DeFi protocols, infrastructure)
• Evaluating X/Twitter announcements for alpha/farming opportunities
• DePIN project analysis (Decentralized Physical Infrastructure Networks — Helium, IoTeX, Peaq, Render, etc.)
• Stock market dividend analysis (IDX/Indonesian stocks) — see references/idx-dividend-analysis.md for workflow when APIs fail
• NFT collection analysis (OpenSea, Blur, etc.) — floor price, volume, holder distribution, project status

Analysis Types

Type A: Token Analysis (existing token with market data)

Use when: Token already listed on CoinGecko/CMC, has trading volume, circulating supply.

Type B: Project/Protocol Analysis (pre-token or early-stage)

Use when: Analyzing for airdrop potential, product is live but no token yet, or token just launched with minimal data.

Key difference: Type B focuses on product reality, user onboarding, VC backing, and airdrop signals rather than liquidity metrics.

Type C: DePIN Project Analysis (hardware-based infrastructure)

Use when: Analyzing Decentralized Physical Infrastructure Networks — projects requiring hardware (IoT devices, sensors, nodes, GPUs, wireless hotspots).

Key difference: Type C evaluates hardware requirements, operator economics, network effects, and adoption barriers unique to physical infrastructure.

Type D: NFT Collection Analysis (floor price, volume, holders)

Use when: Analyzing NFT collections on OpenSea, Blur, or other marketplaces — evaluating floor price trends, trading volume, holder distribution, and project status.

Key difference: Type D focuses on floor price trends, volume/holder ratio, chart patterns (pump & dump, death spiral), and project abandonment signals rather than tokenomics.

Core Analysis Framework (Type A: Token Analysis)

1. Market Data (CoinGecko API)

Primary endpoint:

curl -s "https://api.coingecko.com/api/v3/coins/{token_id}"

Key metrics to extract:

• Market cap & FDV (Fully Diluted Valuation)
• 24h volume
• Circulating vs total supply
• Price performance (24h, 7d, 30d)
• ATH/ATL with dates
• Contract addresses

Critical calculation:

vol_mcap_ratio = (volume_24h / market_cap) * 100

# Benchmarks:
# Healthy: >10%
# Acceptable: 3-10%
# Weak: 1-3%
# Dead: <1%

2. Liquidity Health Assessment

Volume/MCap ratio is THE critical metric for exit risk.

If ratio < 1%, token is illiquid regardless of other fundamentals.

Slippage estimation (rough):

• <0.1% ratio: Expect 10-30% slippage on $5K trade
• 0.1-1%: Expect 3-10% slippage on $5K trade
• 1-5%: Expect 1-3% slippage on $5K trade
• >5%: Liquid, <1% slippage

3. Tokenomics & Unlock Risk

Supply breakdown:

circulating_pct = (circulating_supply / total_supply) * 100
locked_pct = 100 - circulating_pct

# Risk levels:
# <20% locked: Low risk
# 20-50% locked: Medium risk
# 50-70% locked: High risk
# >70% locked: Extreme risk (avoid)

Unlock schedule:

• Check project docs, GitHub, or token vesting contracts
• If not disclosed = RED FLAG
• Calculate potential sell pressure per unlock event

4. Protocol Fundamentals (for DeFi tokens)

For lending/DEX/yield protocols:

# Revenue efficiency
revenue_tvl_ratio = (annual_revenue / tvl) * 100

# Benchmarks (lending protocols):
# Excellent: >2%
# Good: 1-2%
# Acceptable: 0.5-1%
# Poor: <0.5%

# Utilization rate
utilization = (total_borrowed / total_supplied) * 100

# Healthy range: 40-80%
# Too low (<30%): Dead capital
# Too high (>90%): Liquidity risk

TVL quality check:

• Look for "dead capital" (high TVL, low utilization)
• Check if TVL is real or wash farming
• Compare TVL to actual user count

5. Ecosystem & Narrative

Context matters:

• Is the base chain/ecosystem growing or dying?
• Compare token performance to ecosystem native token
• Check if token is listed on major trackers (CMC, CoinGecko)

Example comparison:

# If analyzing token on Hyperliquid:
hype_vol_mcap = 1.2%  # Native token
target_vol_mcap = 0.02%  # Your token

relative_health = target_vol_mcap / hype_vol_mcap
# If <0.1 (10x worse), token is dying relative to ecosystem

6. Sentiment & Catalyst Check

Use delegation for:

• Recent X/Twitter mentions (sentiment, KOL coverage)
• Upcoming events (CEX listings, partnerships, protocol upgrades)
• Community activity (Discord, Telegram, Reddit)

Red flags:

• No social media presence
• Last announcement >1 month ago
• Community size << TVL (fake TVL indicator)

Type B: Project/Protocol Airdrop Analysis Framework

Use when: Analyzing projects for airdrop potential (no token yet, or token just launched with minimal market data).

1. Context Extraction (from X/Twitter or announcement)

What to extract:

• Tweet content (product update? funding? campaign? teaser?)
• Account type (official project? founder? influencer?)
• Engagement metrics (likes, RTs, replies)
• Media attached (video, images, links)

Tools:

# Get tweet data
curl -s "https://api.fxtwitter.com/{handle}/status/{tweet_id}" | jq -r '.tweet | {text, author, likes, retweets, replies, media}'

# Get account info
curl -s "https://api.fxtwitter.com/{handle}" | jq -r '.user | {name, description, followers, website}'

2. Project Identification

Core questions:

• What problem does it solve?
• Category: AI / Infra / DeFi / Identity / L2 / Gaming / etc.
• Unique edge vs competitors?
• Target users (devs? traders? consumers?)

Research sources: