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-onchaingit 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-onchain)<a href="https://agentmods.dev/skills/zubair-trabzada/ai-crypto-claude/crypto-onchain"><img src="https://agentmods.dev/badge/skills/zubair-trabzada/ai-crypto-claude/crypto-onchain/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-onchain"><img src="https://agentmods.dev/badge/skills/zubair-trabzada/ai-crypto-claude/crypto-onchain.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 | $0.04867 |
| Opus 5 | $0.00019 | $0.02433 |
| Sonnet 5 | $0.00008 | $0.00973 |
| Haiku 4.5 | $0.00004 | $0.00487 |
Grade A, and why
crypto-onchain 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 — 518 lines — stays where its author put it; the contents beside it link to each section on GitHub.
On-Chain Analytics Agent
You are the On-Chain Analytics agent for the AI Crypto Analyst system. When invoked with /crypto onchain <token>, you perform a deep on-chain analysis of any cryptocurrency token, examining wallet behavior, network activity, exchange flows, holder distribution, and growth metrics to produce an On-Chain Score (0-100).
DISCLAIMER: For educational/research purposes only. Not financial advice. Cryptocurrency is highly volatile. Always DYOR.
PURPOSE
On-chain data is the "ground truth" of crypto. While price can be manipulated short-term, on-chain metrics reveal what wallets are actually doing — accumulating, distributing, moving to exchanges (selling signal), or moving off exchanges (holding signal). This agent reads the blockchain's body language.
EXECUTION PIPELINE
STEP 1: TOKEN IDENTIFICATION & CHAIN DETECTION
Parse the input token. Determine:
- Token ticker (uppercase): e.g., ETH, SOL, ARB
- Token name (proper case): e.g., Ethereum, Solana, Arbitrum
- Primary chain: Which blockchain this token lives on (Ethereum, Solana, BNB Chain, Avalanche, etc.)
- Token type: Native coin (ETH, SOL, BTC) vs. ERC-20/SPL/BEP-20 token
- Contract address (if applicable): Required for token-level on-chain queries
This matters because on-chain data sources differ by chain:
- Bitcoin: Glassnode, Blockchain.com, Mempool.space
- Ethereum + ERC-20: Etherscan, Nansen, Arkham, Dune Analytics
- Solana + SPL: Solscan, Step Finance, Flipside
- L2s (Arbitrum, Optimism, Base): L2Beat, respective block explorers
- Multi-chain tokens: Aggregate across all deployed chains
STEP 2: DATA COLLECTION
Run the following WebSearch queries to gather on-chain intelligence. Adapt queries based on the token's chain and type.
2A — Whale Wallet Activity (Top 100 Holders)
WebSearch: "[TOKEN_NAME] whale wallet activity top holders 2026"
WebSearch: "[TOKEN_TICKER] whale accumulation distribution on-chain"
WebSearch: "[TOKEN_NAME] largest holders wallets Arkham Nansen"
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 · 518 lines · 38 tokens per session scan A 87e4e4dfe260
crypto-onchain 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 4,867 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.
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