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-post-trade-reviewgit 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-post-trade-review)<a href="https://agentmods.dev/skills/galleonlabs/hypergrok-trading-desk/desk-post-trade-review"><img src="https://agentmods.dev/badge/skills/galleonlabs/hypergrok-trading-desk/desk-post-trade-review/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-post-trade-review"><img src="https://agentmods.dev/badge/skills/galleonlabs/hypergrok-trading-desk/desk-post-trade-review.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 | $0.00073 | $0.01317 |
| Opus 5 | $0.00036 | $0.00659 |
| Sonnet 5 | $0.00015 | $0.00263 |
| Haiku 4.5 | $0.00007 | $0.00132 |
Grade A, and why
desk-post-trade-review 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 — 89 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Journal and post-trade review
The journal is the desk's memory; the review is how the desk learns. Both come from the exchange record first and chat second, and both keep process and outcome apart.
1. Journal
/workspace/trading-desk/journal/YYYY-MM-DD.md, one file per active day, appended in time order. Entry types:
14:02 UTC HG-20260816-01 opened ETH-PERP long idea (user); status evidence
14:12 UTC HG-20260816-01 risk PASS 0.4827 ETH, stop 2,900, 0.5% risk stressed (risk-limits v3)
14:29 UTC HG-20260816-01 approval "approve HG-20260816-01" (user)
14:31 UTC HG-20260816-01 sent buy 0.4827 @ 3,000 Gtc + sl 2,900 (normalTpsl); cloid 0x9f3e...; oid 1839201122, sl waitingForFill
16:05 UTC HG-20260816-01 fill 0.4827 @ 3,000.0 maker, fee $0.29
09:12 UTC HG-20260816-01 closed tp 3,089.6, fee $1.44; position flat; sl 2,900 cancelled 09:13
10:00 UTC limits risk-limits v3 -> v4: max positions 3 -> 4 (user, reason: adding HYPE)
11:40 UTC incident INC-20260817-01 send timeout on HG-20260817-02; not on exchange, waited out expiresAfter 11:41, re-checked clean, re-approved and sent 11:47
18:00 UTC note maker entries at 10 bps depth filled within 2h on both attempts this week
Rules: append only; corrections are new lines with correction:; every line has a UTC time and an id where one exists; no opinions in the journal (those go in reviews).
2. Trade review
Trigger: a proposal reaches closed, or the user asks. Inputs, always listed with timestamps:
- the proposal file (ticket, PASS, approval, execution, reconciliation)
userFills/userFillsByTimefor the window: price, size, fee, side,crossedhistoricalOrdersandorderStatusby cloid: what rested when, what cancelleduserFundingfor the holding window- optionally the Market Analyst's depth read at send time for expected slippage
Compute:
| Measure | How |
|---|---|
| Entry slippage | (fill avg - ticket price) / ticket price in bps, signed against the trade |
| Exit slippage | same for the exit versus its ticket or trigger price |
| Fees | sum of fill fees in USD and as bps of notional; note maker vs taker |
| Funding | sum of funding payments over the window, USD |
| Net result | realised PnL after fees and funding, in USD and in R (R from the ticket) |
| Protection | was a reduce-only stop resting on the exchange for the entire life of the position? gaps in minutes |
| Lifecycle | each stage present, in order, with timestamps; approval by id; single send; reconciled |
| Holding time | fill to flat |
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 · 89 lines · 73 tokens per session scan A 5e8bf9337481
desk-post-trade-review is a skill published in the GitHub repository galleonlabs/hypergrok-trading-desk (60 stars, last pushed 2d ago), licensed MIT. It adds 73 tokens to every session and 1,317 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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