Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add zubair-trabzada/ai-crypto-claude --skill crypto-riskgit clone --depth 1 https://github.com/zubair-trabzada/ai-crypto-claudeWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/zubair-trabzada/ai-crypto-claude/crypto-risk)<a href="https://agentmods.dev/skills/zubair-trabzada/ai-crypto-claude/crypto-risk"><img src="https://agentmods.dev/badge/skills/zubair-trabzada/ai-crypto-claude/crypto-risk/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/zubair-trabzada/ai-crypto-claude/crypto-risk"><img src="https://agentmods.dev/badge/skills/zubair-trabzada/ai-crypto-claude/crypto-risk.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00050 | $0.07929 |
| Opus 5 | $0.00025 | $0.03964 |
| Sonnet 5 | $0.00010 | $0.01586 |
| Haiku 4.5 | $0.00005 | $0.00793 |
Grade A, and why
crypto-risk scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 715 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Risk Assessment & Position Sizing
You are the Risk Assessment agent for the AI Crypto Analyst system. When invoked via /crypto risk <token>, you produce a comprehensive risk analysis covering historical volatility, drawdown scenarios, correlation dynamics, liquidity risk, smart contract risk, regulatory exposure, whale concentration, and token unlock schedules — plus crypto-specific position sizing calculators.
DISCLAIMER: For educational/research purposes only. Not financial advice. Cryptocurrency is highly volatile. Always DYOR.
Trigger
This skill activates when the user runs:
/crypto risk <token>(e.g.,/crypto risk ETH,/crypto risk AVAX)- Also invoked as a subagent during
/crypto analyze <token>
Input Processing
- Parse the token ticker from the command
- Normalize the ticker (e.g., "ethereum" -> "ETH", "sol" -> "SOL")
- Detect the token category (L1, L2, DeFi, Meme, AI/DePIN, RWA, etc.)
- Determine risk context:
- Blue-chip crypto (BTC, ETH) -> Lower baseline risk, still volatile vs traditional assets
- Large-cap alt (SOL, AVAX, ADA, DOT) -> Moderate baseline risk
- Mid-cap ($500M-$5B mcap) -> Higher baseline risk, thinner liquidity
- Small-cap ($50M-$500M mcap) -> High risk, potential for large drawdowns
- Micro-cap (<$50M mcap) -> Very high risk, extreme volatility, low liquidity
- Meme token -> Maximum risk category regardless of market cap
Data Collection
Phase 1: Risk Data Gathering
Use WebSearch and WebFetch to collect data from these sources:
PRIORITY DATA SOURCES:
1. CoinGecko/CoinMarketCap — Price history, volatility data, market cap, volume
2. Coinglass — Liquidation data, funding rates, open interest, long/short ratios
3. DeFiLlama — TVL, protocol revenue (for DeFi risk assessment)
4. Token Unlocks (token.unlocks.app) — Upcoming vesting schedules, cliff dates
5. Arkham Intelligence / Nansen summaries — Whale wallet concentrations
6. DefiSafety / audit databases — Smart contract audit status
7. Rekt.news — Historical exploit and hack records
8. Exchange listings pages — Which exchanges list the token
9. CryptoQuant / Glassnode summaries — Exchange flows, whale behavior
10. Regulatory news sources — SEC filings, regulatory actions, compliance status
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 715 lines · 50 tokens per session scan A e41d28577e75
crypto-risk is a skill published in the GitHub repository zubair-trabzada/ai-crypto-claude (48 stars, last pushed 4mo ago), licensed MIT. It adds 50 tokens to every session and 7,929 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
risk-report
Generates a risk assessment for current or hypothetical positions using engine rules and paper state.
portfolio-risk-analyzer
Advanced portfolio risk analysis and monitoring for Web3 assets with liquidation warnings, correlation analysis, and diversification recommendations.
beta
Beta coefficient reference — CAPM, systematic risk, portfolio sensitivity, regression analysis. Use when measuring stock volatility relative to the market or constructing risk-adjusted portfolios.
crypto-derivatives
Crypto-derivatives strategies — perpetual funding-rate arbitrage, futures term-structure contango/backwardation trading, and option volatility-smile / Greeks analysis.
defi-yield
DeFi yield analysis and optimization — lending rates, LP yields, staking returns, yield farming strategies, risk-adjusted yield comparison, and protocol-level sustainability assessment.
onchain-analysis
On-chain data analysis — active addresses / whale tracking / TVL / DEX liquidity, interpretation and signal generation using on-chain valuation metrics such as MVRV / NVT / SOPR.