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 terrylica/cc-skills --skill sharpe-ratio-non-iid-correctionsgit clone --depth 1 https://github.com/terrylica/cc-skillsWrote 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/terrylica/cc-skills/sharpe-ratio-non-iid-corrections)<a href="https://agentmods.dev/skills/terrylica/cc-skills/sharpe-ratio-non-iid-corrections"><img src="https://agentmods.dev/badge/skills/terrylica/cc-skills/sharpe-ratio-non-iid-corrections/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/terrylica/cc-skills/sharpe-ratio-non-iid-corrections"><img src="https://agentmods.dev/badge/skills/terrylica/cc-skills/sharpe-ratio-non-iid-corrections.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 4 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Rogue Agent · line 170 Skill modifies its own code, configuration, or behavior at runtime. Self-modification enables an agent to escalate privileges, disable safety constraints, or install persistent backdoors.Fix: Prevent the skill from modifying its own code, SKILL.md, or configuration files. Treat skill files as read-only at runtime.
- medium analysis-evasion · line 1 Suspicious Unicode normalization or mixed-script contentFix: Review the flagged content for security risks. Ensure no credentials, secrets, or sensitive data are exposed.
- medium Prompt Injection · line 66 Large whitespace padding was detected (a block of blank lines or a long run of spaces). This can push injected instructions below or to the right of the visible area so a human reviewer never sees them while the agent still reads them. Manual review of the hidden content is recommended.Fix: Remove the large whitespace padding (blank-line blocks or long space runs) and review any content hidden below or to the right of it. Keep skill files compact and reviewable so no instructions can be
- medium Prompt Injection · line 72 Large whitespace padding was detected (a block of blank lines or a long run of spaces). This can push injected instructions below or to the right of the visible area so a human reviewer never sees them while the agent still reads them. Manual review of the hidden content is recommended.Fix: Remove the large whitespace padding (blank-line blocks or long space runs) and review any content hidden below or to the right of it. Keep skill files compact and reviewable so no instructions can be
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 | $0.00172 | $0.02355 |
| Opus 5.5 | $0.00069 | $0.00942 |
| Sonnet 5.5 | $0.00034 | $0.00471 |
| Haiku 4.5 | $0.00017 | $0.00235 |
Grade A, and why
sharpe-ratio-non-iid-corrections 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 7d 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 — 133 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Sharpe Ratio Non-IID Corrections
Sharpe ratio inference for a single strategy whose returns are non-Normal and serially correlated. Reference implementation of the generalized sampling variance in López de Prado, Lipton & Zoonekynd (2026), and of the single-test statistics that use it.
Self-Evolving Skill: This skill improves through use. If instructions are wrong, parameters drifted, or a workaround was needed — fix this file immediately, don't defer. Only update for real, reproducible issues.
Out of scope — do not re-add (operator rule, 2026-10-15)
Never compute, report, rank by or cite the Deflated Sharpe Ratio or any other trial-count correction: FWER and FDR procedures (Holm, Bonferroni/Šidák, Benjamini-Yekutieli, the paper's pFDR/oFDR/SFDR), e-LOND and other online multiple testing, expected-maximum-Sharpe deflation, effective-number-of-trials estimates, and the minimum backtest length (MinBTL), which is a function of the number of trials. MinTRL is a different quantity (one rule's own track record) and stays. They were removed from this skill on purpose. Instead:
- Judge parsimony on the model itself (how many free parameters, how much was tuned), not by deflating a Sharpe for the number of trials.
- Guard selection with registration before runs and with unused data: fix the hypothesis, rule and evaluation window before running, and confirm on data no run has touched.
- Report each rule's own statistics: its Sharpe, ρ-corrected σ[SR̂], PSR, MinTRL and power.
PSR, MinTRL, the ρ-corrected variance and power are single-test statistics and stay. PBO/CSCV describes a selection procedure and is not covered by the rule.
Quick Start
# Run the 9 validation tests (paper Exhibit 1 numerical examples)
uv run --no-project --with numpy --with numba \
python plugins/quant-research/skills/sharpe-ratio-non-iid-corrections/references/sharpe_numba.py
Paper Metadata
| Field | Value |
|---|---|
| Title | How to Use the Sharpe Ratio |
| Authors | Marcos López de Prado, Alexander Lipton, Vincent Zoonekynd |
| Series | ADIA Lab Research Paper Series, No. 19 |
| Date | March 7, 2026 |
| SSRN | 5520741 |
| Replication | https://github.com/zoonek/2025-sharpe-ratio |
| Source PDF | lopez-de-prado-sharpe-2026.pdf |
What ships with it
8 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.
- references/how-to-use-the-sharpe-ratio-2026.md 34 KB
- references/lopez-de-prado-sharpe-2026.pdf 1841 KB
- references/media/fig-p12-01.png 149 KB
- references/media/fig-p12-02.png 160 KB
- references/media/fig-p13-01.png 152 KB
- references/media/fig-p13-02.png 158 KB
- references/sharpe_numba.py 15 KB runs code
- references/sharpe-paper-tracker.md 5.5 KB
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.
- 7d ago Changed · -40 lines · +61 tokens per session cd53f46a20a4
- 1mo ago First seen · 173 lines · 111 tokens per session scan A af089a5627e3
sharpe-ratio-non-iid-corrections is a skill published in the GitHub repository terrylica/cc-skills (1 stars, last pushed today), licensed MIT. It adds 172 tokens to every session and 2,355 once invoked, about $0.0007 per session on Opus 5.5. 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 2026-09-05.
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