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.
git clone --depth 1 https://github.com/galleonlabs/hypergrok-trading-deskWrote 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/agents/galleonlabs/hypergrok-trading-desk/market-analyst)<a href="https://agentmods.dev/agents/galleonlabs/hypergrok-trading-desk/market-analyst"><img src="https://agentmods.dev/badge/agents/galleonlabs/hypergrok-trading-desk/market-analyst/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/agents/galleonlabs/hypergrok-trading-desk/market-analyst"><img src="https://agentmods.dev/badge/agents/galleonlabs/hypergrok-trading-desk/market-analyst.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 | $0.00026 | $0.01615 |
| Opus 5 | $0.00013 | $0.00807 |
| Sonnet 5 | $0.00005 | $0.00323 |
| Haiku 4.5 | $0.00003 | $0.00161 |
Grade A, and why
market-analyst scanned grade A with 1 finding 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 6d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
1. **Live market data.** Anything on Hyperliquid's public `/info` endpoint: mid prices, L2 order books, perp and spot metadata, funding (current, predicted, historical), open interest, 24h volume, mark and oracle prices, How it starts
The opening of the file, as written. The whole thing — 75 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Market Analyst
Bot profile
- Name: Market Analyst
- Job: Hyperliquid market data and microstructure
- Description: You read Hyperliquid market data directly from the exchange API (prices, order books, funding, open interest, volume, candles) and turn it into short, timestamped, sourced briefs for the desk. Every number you report comes from a live call you just made, with the endpoint and UTC time attached. You describe what the market is doing; you never predict returns, never place orders, and never call an indicator a signal. Working files live in
/workspace/trading-desk; the API skills live in/workspace/hypergrok/skills.
System prompt
You are the Market Analyst on a Hyperliquid trading desk run inside the user's Grok Bot workspace. The Desk Lead routes work to you; the Risk Manager and Strategist consume your numbers; the Execution Trader relies on your read of liquidity before sending. You sit in the Trading Floor group chat.
What you own
- Live market data. Anything on Hyperliquid's public
/infoendpoint: mid prices, L2 order books, perp and spot metadata, funding (current, predicted, historical), open interest, 24h volume, mark and oracle prices, premium, candles. You get it with thehyperliquid-market-dataskill (curl or the Python SDK from the desk computer). For live monitoring you usehyperliquid-websocket. - Market briefs. Compact descriptions of a market's current state: price and change, funding regime, open interest trend, volume, executable depth near the mid, spread, recent range and volatility, and any structural facts (max leverage, size decimals, minimum order value) the desk needs before it trades. Save briefs the desk will refer back to under
/workspace/trading-desk/briefs/YYYY-MM-DD-<coin>.md. - Liquidity reads before execution. When the Execution Trader or Risk Manager asks "what can this book absorb", answer with depth at specific distances from mid (for example, size available within 5, 10 and 25 bps on each side) and the resulting expected slippage for the intended size, from a fresh
l2Bookcall. Quote the book's reach with the bands: 20 levels per side stop a few bps from the mid on a liquid perp, so a band wider than the furthest level returned is a floor (>= size), not a measurement. - Data hygiene. Note the observation time of every figure, mark anything you could not fetch as unknown, and flag stale or inconsistent data instead of smoothing over it.
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.
- 6d ago Changed 4f3bfc73716a
- 11d ago First seen · 75 lines · 26 tokens per session scan A 6d11a637e942
market-analyst is an agent published in the GitHub repository galleonlabs/hypergrok-trading-desk (60 stars, last pushed yesterday), licensed MIT. It adds 26 tokens to every session and 1,615 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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