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 galleonlabs/hypergrok-trading-desk --skill desk-strategy-labgit 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/skills/galleonlabs/hypergrok-trading-desk/desk-strategy-lab)<a href="https://agentmods.dev/skills/galleonlabs/hypergrok-trading-desk/desk-strategy-lab"><img src="https://agentmods.dev/badge/skills/galleonlabs/hypergrok-trading-desk/desk-strategy-lab/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/galleonlabs/hypergrok-trading-desk/desk-strategy-lab"><img src="https://agentmods.dev/badge/skills/galleonlabs/hypergrok-trading-desk/desk-strategy-lab.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00082 | $0.02331 |
| Opus 5 | $0.00041 | $0.01166 |
| Sonnet 5 | $0.00016 | $0.00466 |
| Haiku 4.5 | $0.00008 | $0.00233 |
Grade A, and why
desk-strategy-lab 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 — 122 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Strategy lab
The lab exists so the user can find out whether their idea holds up before risking money on it. The Strategist supplies method, code and honesty; the ideas are the user's. Nothing here recommends what to trade.
Workspace
/workspace/trading-desk/strategies/<name>/
RULES.md the rules, in words, agreed with the user before any code
data.md exact data requests used (coin, interval, start, end, fetched at)
backtest.py readable, single-file backtest
runs/YYYY-MM-DD-HHMM.md one file per run: parameters, results, caveats
POSTMORTEM.md if the idea is abandoned: why, in one paragraph
/workspace/trading-desk/data/<coin>-<interval>-<start>-<end>.csv fetched by the Market Analyst or the Strategist
1. Rules first
Interview the user until every field is unambiguous. Write RULES.md:
# funding-fade-v1
- universe: ETH, BTC, SOL perps on Hyperliquid
- data: 4h candles (close), hourly funding from fundingHistory
- entry: when the average hourly funding over the last 8 hours is above +0.005%/h, sell at the next 4h open
- exit: when funding <= 0, or after 72h, or stop hit
- stop: 2 x 24h ATR above entry (ATR from prior 24 bars)
- sizing rule: risk 0.5% of equity to the stop (the desk's limits apply on top)
- one position per market; no adds
- what would make me abandon this: no edge after fees on 12 months of data across 3 markets
If a rule needs "it depends", it is not a rule yet. Do not proceed to code.
When the idea comes from outside
Most ideas arrive as a claim, not a rule: a repository of indicator settings, a thread with a chart, a video, a screenshot of an equity curve. A claim is a candidate for RULES.md; it is never a result the desk inherits. Published periods, backtests, win rates and "this called the top" screenshots carry no validation into this desk.
Before an imported claim becomes a draft thesis, the user and the Strategist freeze all of it, in writing:
- Instrument and venue. A Hyperliquid perp does not inherit an equity-index or spot claim.
- Timeframe and how a bar is closed; which session, which exchange's clock.
- Price field and the exact formula, including how the first values are seeded.
- The transition rule and every abstention case, not just the entry.
- Next observable entry, signal expiry, mandatory stop, target and time exit.
- Costs, funding, spread, slippage and what happens to a rejected or partial fill.
- The full family the claim came from: every parameter set, asset and timeframe that was tried before this one was presented. That count is the multiplicity correction below.
- Chronological in-sample and out-of-sample boundaries and an untouched holdout.
- A prospective paper-trade duration agreed before the first run.
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 122 lines · 82 tokens per session scan A 43c08289dd25
desk-strategy-lab is a skill published in the GitHub repository galleonlabs/hypergrok-trading-desk (60 stars, last pushed 2d ago), licensed MIT. It adds 82 tokens to every session and 2,331 once invoked, about $0.0004 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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