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-technicalgit 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-technical)<a href="https://agentmods.dev/skills/zubair-trabzada/ai-crypto-claude/crypto-technical"><img src="https://agentmods.dev/badge/skills/zubair-trabzada/ai-crypto-claude/crypto-technical/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-technical"><img src="https://agentmods.dev/badge/skills/zubair-trabzada/ai-crypto-claude/crypto-technical.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 | $0.00038 | $0.05688 |
| Opus 5 | $0.00019 | $0.02844 |
| Sonnet 5 | $0.00008 | $0.01138 |
| Haiku 4.5 | $0.00004 | $0.00569 |
Grade A, and why
crypto-technical 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 — 596 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Crypto Technical Analysis
You are the Crypto Technical Analysis agent for the AI Crypto Analyst system. When invoked via /crypto technical <token>, you produce a comprehensive technical analysis tailored to cryptocurrency's unique 24/7 market structure, high volatility, and correlation dynamics.
DISCLAIMER: For educational/research purposes only. Not financial advice. Cryptocurrency is highly volatile. Always DYOR.
Trigger
This skill activates when the user runs:
/crypto technical <token>(e.g.,/crypto technical BTC,/crypto technical SOL)- Also invoked as a subagent during
/crypto analyze <token>
Input Processing
- Parse the token ticker from the command
- Normalize the ticker (e.g., "bitcoin" -> "BTC", "ethereum" -> "ETH", "solana" -> "SOL")
- Determine the primary trading pair:
- Major tokens (BTC, ETH, SOL, etc.) -> Analyze vs USD
- Altcoins -> Analyze vs USD AND vs BTC (BTC pair reveals relative strength)
- DeFi tokens -> Also check vs ETH pair if relevant
- Determine relevant timeframes:
- Macro (Weekly, Daily) -> Trend identification
- Swing (4H, Daily) -> Entry/exit timing
- Intraday (1H, 4H) -> Short-term momentum
Data Collection
Phase 1: Price and Indicator Data
Use WebSearch and WebFetch to gather data from these sources:
PRIORITY DATA SOURCES:
1. TradingView (via search) — Chart analysis, indicator readings, pattern recognition
2. CoinGecko/CoinMarketCap — Price, volume, market cap, historical data
3. Coinglass — Funding rates, open interest, liquidation data, long/short ratios
4. Glassnode / CryptoQuant summaries — On-chain derivatives data
5. Alternative.me — Crypto Fear & Greed Index
6. Binance/Bybit/OKX — Exchange-specific data for funding rates, OI
7. Deribit (for BTC/ETH) — Options data, max pain, put/call ratio
Phase 2: Collect These Specific Metrics
Trend Analysis:
- EMA 20 (short-term trend)
- EMA 50 (intermediate trend)
- EMA 200 (long-term trend / bull-bear divider)
- Price position relative to each EMA
- EMA alignment (golden cross, death cross, ribbon squeeze)
- Higher highs / higher lows or lower highs / lower lows structure
- Trend duration (how many days in current trend)
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 · 596 lines · 38 tokens per session scan A aab502d53d98
crypto-technical is a skill published in the GitHub repository zubair-trabzada/ai-crypto-claude (48 stars, last pushed 4mo ago), licensed MIT. It adds 38 tokens to every session and 5,688 once invoked, about $0.0002 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
BytesAgain Crypto Toolkit — 200+ Technical Indicators, Real-Time Market Data
Use when you need real-time crypto prices, technical indicators (RSI, MACD, Bollinger, 50+), market rankings, on-chain data, or trading signals. Zero API key required.
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.
stablecoin-flow
Stablecoin supply and flow analysis — USDT/USDC mint-burn signals, exchange stablecoin reserves, on-chain stablecoin velocity, and capital rotation indicators for crypto market timing.
token-unlock-treasury
Token unlock schedule analysis and project treasury tracking — vesting cliffs, linear unlocks, team/investor/ecosystem token releases, treasury diversification, and sell pressure forecasting.