Technical Analyst
tradermonty/claude-trading-skills
This skill should be used when analyzing weekly price charts for stocks, stock indices, cryptocurrencies, or forex pairs.
Token supply dynamics, vesting analysis, inflation modeling, and valuation frameworks for crypto tokens
Install Token Economics
$ npx skills add agiprolabs/claude-trading-skills --skill token-economics -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install agiprolabs/claude-trading-skills token-economics --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/agiprolabs/claude-trading-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/token-economics .claude/skills/token-economics && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "token-economics" agent skill from https://github.com/agiprolabs/claude-trading-skills/tree/main/skills/token-economics into .claude/skills/token-economics/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "token-economics", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/agiprolabs/claude-trading-skills/tree/main/skills/token-economicsType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add agiprolabs/claude-trading-skills --skill token-economics -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install agiprolabs/claude-trading-skills token-economics --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/agiprolabs/claude-trading-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/token-economics .agents/skills/token-economics && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "token-economics" agent skill from https://github.com/agiprolabs/claude-trading-skills/tree/main/skills/token-economics into .agents/skills/token-economics/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "token-economics", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add agiprolabs/claude-trading-skills --skill token-economics -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install agiprolabs/claude-trading-skills token-economics --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/agiprolabs/claude-trading-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/token-economics .cursor/skills/token-economics && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "token-economics" agent skill from https://github.com/agiprolabs/claude-trading-skills/tree/main/skills/token-economics into .cursor/skills/token-economics/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "token-economics", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/agiprolabs/claude-trading-skills.git --path skills/token-economics--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add agiprolabs/claude-trading-skills --skill token-economics -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install agiprolabs/claude-trading-skills token-economics --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/agiprolabs/claude-trading-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/token-economics .gemini/skills/token-economics && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "token-economics" agent skill from https://github.com/agiprolabs/claude-trading-skills/tree/main/skills/token-economics into .gemini/skills/token-economics/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "token-economics", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install agiprolabs/claude-trading-skills token-economicsInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add agiprolabs/claude-trading-skills --skill token-economics -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/agiprolabs/claude-trading-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/token-economics .github/skills/token-economics && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "token-economics" agent skill from https://github.com/agiprolabs/claude-trading-skills/tree/main/skills/token-economics into .github/skills/token-economics/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "token-economics", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add agiprolabs/claude-trading-skills --skill token-economics -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install agiprolabs/claude-trading-skills token-economics --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/agiprolabs/claude-trading-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/token-economics .opencode/skills/token-economics && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "token-economics" agent skill from https://github.com/agiprolabs/claude-trading-skills/tree/main/skills/token-economics into .opencode/skills/token-economics/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "token-economics", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
token-economics- GitHub stars
- 410
- Token cost
- ~2.5k tokens
- SKILL.md length
- 696 words
- Files
- 5 (incl. scripts, references)
- Skills in repo
- 68
- Repo updated
- First seen
- Licence
- MIT
At a glance
Token supply dynamics, vesting analysis, inflation modeling, and valuation frameworks for crypto tokens
- Tasks that involve Crypto and DeFi analysis
- SKILL.md covers Why Tokenomics Matters, Key Supply Concepts, Supply Dynamics and Vesting and Unlock Schedules, plus 5 more sections
- Runs Python scripts from its folder
What it does
Token Economics is an agent skill from agiprolabs/claude-trading-skills. Token supply dynamics, vesting analysis, inflation modeling, and valuation frameworks for crypto tokens
Its SKILL.md is about 2.5k tokens, which your agent loads only when the skill is triggered. The skill folder holds 6 other files, including scripts and reference files (for example `references/supply_analysis.md`, `references/valuation_frameworks.md` and `scripts/supply_modeler.py`).
It sits in Business, Finance & HR, covering Crypto and DeFi analysis. The repository describes itself as: 68 trading, DeFi, and quantitative finance Agent Skills. Works with Claude Code, Cursor, Codex, Gemini CLI, and 30+ other tools. The licence is MIT.
When your agent uses it
- Tasks that involve Crypto and DeFi analysis
Example prompts
- “/token-economics”
Requirements
- Python 3
What it can do on your machine
Read from SKILL.md and the folder at commit 981e1d7. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Ships 2 files in scripts/ (Python), which the agent can run.
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Token Economics loads about 2.5k tokens when it runs, and up to ~7.2k if it reads all its reference files. Until then it costs about 30 tokens; SKILL.md has 696 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.
SKILL.md
The full file from agiprolabs/claude-trading-skills at commit 981e1d7, republished under its MIT licence (© agiprolabs). 696 words, ~2,519 tokens.
Download SKILL.mdSave it as
.claude/skills/token-economics/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.- name
- token-economics
- description
- Token supply dynamics, vesting analysis, inflation modeling, and valuation frameworks for crypto tokens
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 * 100Market Cap vs Fully Diluted Valuation
market_cap = price * circulating_supply
fdv = price * total_supply
fdv_mcap_ratio = fdv / market_capThe 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 yearSupply 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 pressureVesting 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% drawdownTracking 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
Show full SKILL.md (277 more words)Show less
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 zoneComparable 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
© agiprolabs, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 4 other files (scripts, references) in skills/token-economics of agiprolabs/claude-trading-skills.
- SKILL.md
- references/supply_analysis.md
- references/valuation_frameworks.md
- scripts/supply_modeler.py
- scripts/tokenomics_analyzer.py
Open the folder on GitHubat commit 981e1d7
Compare with similar skills
Token Economics next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Token Economics this skillagiprolabs/claude-trading-skills | 410 | — | ~2.5k | Automated safety check: Pass | MIT | |
| Technical Analysttradermonty/claude-trading-skills | 3k | 4 repos | ~4.6k | Automated safety check: Pass | MIT | |
| Polyclawchainstacklabs/polyclaw | 359 | 1 repos | ~2k | Automated safety check: Pass | Apache-2.0 | |
| Longbridge Researchhelsome/folio | 271 | 3 repos | ~2.1k | Automated safety check: Pass | MIT | |
| Stock Analysis24mlight/StockClaw | 101 | 2 repos | ~2k | Automated safety check: Notes | MIT | |
| Swapper Depositswapperfinance/swapper-toolkit | 852 | — | ~1.8k | Automated safety check: Pass | MIT |
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Categories
Questions about Token Economics
What does Token Economics do?
Token supply dynamics, vesting analysis, inflation modeling, and valuation frameworks for crypto tokens. Token Economics is an agent skill from agiprolabs/claude-trading-skills.
When should I use Token Economics?
Token Economics fits situations like: tasks that involve Crypto and DeFi analysis.
How do I install Token Economics in Claude Code?
Run `npx skills add agiprolabs/claude-trading-skills --skill token-economics -a claude-code`. Or copy the skill folder (skills/token-economics in agiprolabs/claude-trading-skills) into .claude/skills/token-economics in your project. Claude Code loads it when a task matches its description.
How do I install Token Economics in Codex?
Run `npx skills add agiprolabs/claude-trading-skills --skill token-economics -a codex`. Or copy the skill folder (skills/token-economics in agiprolabs/claude-trading-skills) into .agents/skills/token-economics in your project. Codex loads it when a task matches its description.
Can I use Token Economics in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add agiprolabs/claude-trading-skills --skill token-economics -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/token-economics, .gemini/skills/token-economics, .github/skills/token-economics and .opencode/skills/token-economics in your project.
What does Token Economics need to run?
Going by SKILL.md and its folder, Token Economics needs Python for the scripts in its folder. Our summary lists: Python 3.
Does Token Economics access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Token Economics safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
What licence does Token Economics use?
Token Economics is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Token Economics use?
About 2.5k tokens (SKILL.md is roughly 10k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 4.7k tokens, read only when the agent opens those files.
What are the alternatives to Token Economics?
Skills that share tags, products or a category with Token Economics: Technical Analyst (tradermonty/claude-trading-skills, 3k stars), Polyclaw (chainstacklabs/polyclaw, 359 stars), Longbridge Research (helsome/folio, 271 stars) and Stock Analysis (24mlight/StockClaw, 101 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Token Economics?
agiprolabs (a GitHub user) maintains it in agiprolabs/claude-trading-skills, which has 410 GitHub stars. The repository holds 68 skills in this directory. The repository was last updated on September 3, 2026.
Source: agiprolabs/claude-trading-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.