token-economics
Token supply dynamics, vesting analysis, inflation modeling, and valuation frameworks for crypto tokens
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/agiprolabs/claude-trading-skills --skill token-economics- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads token-economics automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
Token Economics
Tokenomics — the study of token supply dynamics, distribution, and value accrual — is one of the most important factors in crypto asset analysis. Supply changes directly affect price: new tokens entering circulation create selling pressure, while burns and locks reduce it. Understanding these dynamics lets you estimate dilution risk, identify overvalued or undervalued tokens, and anticipate price-moving unlock events.
Why Tokenomics Matters
Price is a function of demand and supply. In crypto, supply is programmable and constantly changing:
- A token inflating at 50%/year needs 50% demand growth just to maintain price
- A large unlock releasing 10% of circulating supply in one day often causes 5-20% drawdowns
- Tokens with >80% of supply locked have extreme dilution risk ahead
- Protocols that burn fees can become net deflationary, creating structural price support
Key Supply Concepts
Total Supply vs Circulating Supply
total_supply = maximum tokens that will ever exist (or current total minted)
circulating_supply = tokens currently available for trading
locked_supply = total_supply - circulating_supply
circulating_pct = circulating_supply / total_supply * 100
Market Cap vs Fully Diluted Valuation
market_cap = price * circulating_supply
fdv = price * total_supply
fdv_mcap_ratio = fdv / market_cap
The FDV/MCap ratio measures future dilution risk:
| FDV/MCap | Dilution Risk | Interpretation |
|---|---|---|
| 1.0-1.5 | Low | Most supply already circulating |
| 1.5-3.0 | Moderate | Significant supply still locked |
| 3.0-5.0 | High | Majority of supply not yet released |
| >5.0 | Very High | Token will face massive dilution |
Net Inflation Rate
annual_new_tokens = emissions + vesting_unlocks + rewards
annual_burned = fee_burns + buyback_burns
net_new_tokens = annual_new_tokens - annual_burned
net_inflation_rate = net_new_tokens / circulating_supply * 100 # percent per year
Supply Dynamics
Inflationary Pressure (tokens entering circulation)
- Emissions: Block rewards, liquidity mining, staking rewards
- Vesting unlocks: Team, investor, and advisor tokens unlocking on schedule
- Unlock events: Large one-time releases (cliff expirations)
- Treasury spending: DAO or foundation distributing tokens
Deflationary Pressure (tokens leaving circulation)
- Fee burns: Protocol burns a portion of transaction fees (like EIP-1559)
- Buyback and burn: Protocol uses revenue to buy and permanently destroy tokens
- Staking locks: Tokens locked in staking (temporarily removed from circulation)
- Lost tokens: Permanently inaccessible tokens (lost keys, burn addresses)
Selling Pressure Estimation
daily_emissions_usd = daily_new_tokens * token_price
percent_sold = 0.50 # assume 50% of new tokens are sold (conservative)
daily_sell_pressure = daily_emissions_usd * percent_sold
sell_pressure_ratio = daily_sell_pressure / daily_volume
# > 0.05 (5%) = significant selling pressure
# > 0.10 (10%) = heavy selling pressure
Vesting and Unlock Schedules
Key Concepts
- Cliff: Period before any tokens unlock (typically 6-12 months)
- Linear vesting: Constant rate of unlock after cliff (monthly or daily)
- Stepped vesting: Periodic unlocks at set intervals (quarterly)
- TGE unlock: Percentage released at Token Generation Event
Analyzing Unlock Impact
unlock_amount_tokens = 10_000_000
avg_daily_volume_tokens = 5_000_000
unlock_volume_ratio = unlock_amount_tokens / avg_daily_volume_tokens
# Impact assessment:
# < 1x daily volume: minor impact
# 1-5x daily volume: moderate impact, expect 2-5% drawdown
# 5-10x daily volume: major impact, expect 5-15% drawdown
# > 10x daily volume: severe impact, expect 10-30% drawdown
Tracking Sources
- CoinGecko / CoinMarketCap: Basic supply data
- Token Terminal: Revenue and valuation metrics
- Token Unlocks (token.unlocks.app): Detailed unlock schedules
- Project documentation: Whitepapers, tokenomics pages
- On-chain: Vesting contract state, treasury balances
Token Distribution Analysis
Typical Allocation Ranges
| Category | Typical Range | Red Flag |
|---|---|---|
| Team/Founders | 15-25% | >30% |
| Investors (Seed+Series) | 10-30% | >40% |
| Community/Ecosystem | 20-40% | <15% |
| Treasury/DAO | 10-20% | <5% |
| Public Sale | 5-20% | <2% |
| Advisors | 2-5% | >10% |
Distribution Red Flags
- >50% insider allocation (team + investors): Insiders control price
- Short vesting (<1 year): Quick dump risk
- No cliff: Immediate selling from day one
- Large single wallets: Concentration risk (use
token-holder-analysisskill) - Unlabeled large allocations: Hidden insider holdings
Distribution Quality Score
def distribution_score(team_pct: float, investor_pct: float,
community_pct: float, cliff_months: int,
vesting_months: int) -> str:
"""Rate token distribution quality."""
score = 0
insider_pct = team_pct + investor_pct
if insider_pct < 30: score += 3
elif insider_pct < 50: score += 1
if community_pct > 30: score += 2
elif community_pct > 20: score += 1
if cliff_months >= 12: score += 2
elif cliff_months >= 6: score += 1
if vesting_months >= 36: score += 2
elif vesting_months >= 24: score += 1
if score >= 8: return "Excellent"
if score >= 6: return "Good"
if score >= 4: return "Moderate"
return "Poor"
Valuation Frameworks
Revenue-Based Metrics
# Price-to-Earnings (for fee-generating protocols)
pe_ratio = fdv / annualized_net_revenue
# Price-to-Sales
ps_ratio = fdv / annualized_total_volume
# Price-to-Fees
pf_ratio = fdv / annualized_protocol_fees
# Revenue Multiple (adjusted for token value accrual)
rev_multiple = fdv / (annualized_fees * fee_share_to_token_holders)
Typical ranges (crypto, highly variable):
- P/E: 10x-100x+ (DeFi protocols)
- P/S: 0.5x-50x
- P/F: 20x-500x
Network Value Metrics
# Network Value to Transactions (NVT)
nvt = market_cap / daily_transaction_volume_usd
# High NVT (>100): potentially overvalued or store-of-value
# Low NVT (<20): potentially undervalued or high activity
# Market Value to Realized Value (MVRV)
# realized_value = sum of each token at its last-moved price
mvrv = market_cap / realized_value
# MVRV > 3.0: historically overvalued zone
# MVRV < 1.0: historically undervalued zone
Comparable Analysis
def comparable_analysis(target: dict, peers: list[dict]) -> dict:
"""Compare target token metrics against peer group.
Each dict has: name, fdv, revenue, tvl, users
Returns premium/discount percentages.
"""
peer_fdv_rev = [p["fdv"] / p["revenue"] for p in peers if p["revenue"] > 0]
peer_fdv_tvl = [p["fdv"] / p["tvl"] for p in peers if p["tvl"] > 0]
avg_fdv_rev = sum(peer_fdv_rev) / len(peer_fdv_rev) if peer_fdv_rev else 0
avg_fdv_tvl = sum(peer_fdv_tvl) / len(peer_fdv_tvl) if peer_fdv_tvl else 0
target_fdv_rev = target["fdv"] / target["revenue"] if target["revenue"] > 0 else 0
target_fdv_tvl = target["fdv"] / target["tvl"] if target["tvl"] > 0 else 0
return {
"fdv_rev_premium": (target_fdv_rev / avg_fdv_rev - 1) * 100 if avg_fdv_rev else None,
"fdv_tvl_premium": (target_fdv_tvl / avg_fdv_tvl - 1) * 100 if avg_fdv_tvl else None,
}
Token Value Accrual Mechanisms
| Mechanism | Description | Valuation Impact |
|---|---|---|
| Fee sharing | Holders receive protocol revenue | Direct cash flow, use DCF |
| Governance | Voting rights on protocol | Hard to value, often overpriced |
| Utility | Required for protocol use | Demand scales with usage |
| Buyback & burn | Protocol buys and burns | Reduces supply, structural bid |
| Staking rewards | Yield from staking | Inflationary if from emissions |
| veToken model | Lock for boosted rewards + governance | Reduces circulating supply |
PumpFun Token Economics
PumpFun tokens on Solana have simplified tokenomics:
- Fixed supply: 1,000,000,000 tokens (1 billion)
- No vesting: All tokens available immediately at launch
- No team allocation: 100% available on bonding curve
- Bonding curve pricing: Price determined by curve math, not supply changes
- Post-graduation: After bonding curve completes, supply is fully liquid on Raydium
- No inflation: No emissions, no staking rewards, no additional minting
Analysis focus for PumpFun tokens shifts from supply dynamics to:
- Holder concentration (use
token-holder-analysis) - Volume sustainability
- Liquidity depth (use
liquidity-analysis) - Dev wallet behavior
Integration with Other Skills
| Skill | Integration |
|---|---|
defillama-api | Fetch TVL, revenue, fees for valuation metrics |
token-holder-analysis | Analyze holder concentration and whale behavior |
coingecko-api | Fetch supply data, market cap, FDV |
liquidity-analysis | Assess trading liquidity relative to supply |
risk-management | Supply dilution as risk factor |
position-sizing | Adjust size for dilution risk |
Files
References
references/supply_analysis.md— Circulating supply tracking, inflation modeling, unlock analysis, burn mechanicsreferences/valuation_frameworks.md— Revenue-based valuation, NVT, MVRV, comparable analysis, value accrual
Scripts
scripts/tokenomics_analyzer.py— Fetch and analyze token supply metrics from CoinGecko, calculate dilution risk and basic valuationsscripts/supply_modeler.py— Project token supply over 12 months given emission and burn parameters, scenario analysis
FAQ
What does the token-economics skill do?
Token supply dynamics, vesting analysis, inflation modeling, and valuation frameworks for crypto tokens
How do I install token-economics?
Run npx skills add https://github.com/agiprolabs/claude-trading-skills --skill token-economics in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/token-economics/ (personal) or .claude/skills/token-economics/ (project).
Which AI agents can use token-economics?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is token-economics safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains token-economics?
It is published by agiprolabs in agiprolabs/claude-trading-skills. It has 444 recorded installs.
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