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 agentmods add skills/avelikiy/great_cto/quant-validationnpx skills add avelikiy/great_cto --skill quant-validationgit clone --depth 1 https://github.com/avelikiy/great_ctoWrote 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/avelikiy/great_cto/quant-validation)<a href="https://agentmods.dev/skills/avelikiy/great_cto/quant-validation"><img src="https://agentmods.dev/badge/skills/avelikiy/great_cto/quant-validation.svg" alt="Measured on agentmods" 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 | $0.00104 | $0.01687 |
| Opus 5 | $0.00052 | $0.00843 |
| Sonnet 5 | $0.00021 | $0.00337 |
| Haiku 4.5 | $0.00010 | $0.00169 |
Grade A, and why
quant-validation 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 2d 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 — 146 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Validating a financial model — the five ways the number lies
A backtest that looks excellent and loses money live is not usually a bad strategy. It is a good measurement of the wrong thing. Each section below is one mechanism by which a number becomes convincing without becoming true.
On sourcing. The methods here are standard and attributable — most of them to Marcos López de Prado's Advances in Financial Machine Learning, with the information-ratio framing from Grinold & Kahn. This file states the MECHANISM and what to check, and deliberately does not restate formulas from memory. Where an implementation needs an exact expression — the deflated Sharpe ratio in particular — verify it against the primary source before shipping a number that depends on it. A formula recalled approximately is worse here than no formula: it produces a specific, wrong, confident figure.
1. Purged cross-validation with an embargo
The leak. In a normal k-fold split, training and test rows are disjoint. In a financial series they are not independent: a label at time t is computed from data spanning t to t+h. A training observation inside that window has seen the future the test observation is being asked to predict.
Purging. Drop from the training set every observation whose label window overlaps the label window of any test observation. Not the observation's timestamp — its label window. This is the step people skip, because a plain timestamp split looks like it already separates them.
The embargo. Purging is not enough when features are serially correlated: a training row immediately AFTER the test set still carries information about it. Drop a further band after each test fold. The band is a fraction of the total sample; there is no universal value, so state the one used and why.
Combinatorial purged CV. A single train/test split yields one backtest path and one Sharpe. Splitting combinatorially yields many paths and therefore a distribution, which is what you actually want: a strategy whose single path looks good and whose distribution straddles zero has told you something a point estimate hid.
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.
- 2d ago First seen · 146 lines · 104 tokens per session scan A 6401c815cd83
quant-validation is a skill published in the GitHub repository avelikiy/great_cto (89 stars, last pushed yesterday), licensed MIT. It adds 104 tokens to every session and 1,687 once invoked, about $0.0005 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 2026-09-03.
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