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 skloxo/TideTrading --skill quant-statisticsgit clone --depth 1 https://github.com/skloxo/TideTradingWrote 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/skloxo/tidetrading/quant-statistics)<a href="https://agentmods.dev/skills/skloxo/tidetrading/quant-statistics"><img src="https://agentmods.dev/badge/skills/skloxo/tidetrading/quant-statistics/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/skloxo/tidetrading/quant-statistics"><img src="https://agentmods.dev/badge/skills/skloxo/tidetrading/quant-statistics.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
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 →
- 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.
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 | $0.00041 | $0.03972 |
| Opus 5.5 | $0.00016 | $0.01589 |
| Sonnet 5.5 | $0.00008 | $0.00794 |
| Haiku 4.5 | $0.00004 | $0.00397 |
Grade A, and why
quant-statistics 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 1mo 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 — 451 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Quantitative Statistical Methods
Overview
Common statistical methodology used in quantitative investing, covering time-series testing, volatility modeling, regression diagnostics, and statistical inference. Provides the statistical foundation for strategy development and factor research.
Time-Series Tests
1. ADF Unit-Root Test (Stationarity Test)
Why it matters: regressing non-stationary series directly can produce spurious regression, making conclusions unreliable.
from statsmodels.tsa.stattools import adfuller
def adf_test(series: pd.Series, significance: float = 0.05) -> dict:
"""
ADF test: H0 = unit root exists (non-stationary), H1 = stationary
Args:
series: Time series
significance: Significance level
Returns:
Test result
"""
result = adfuller(series.dropna(), autolag='AIC')
return {
'adf_statistic': result[0],
'p_value': result[1],
'lags_used': result[2],
'is_stationary': result[1] < significance,
'critical_values': result[4], # 1%, 5%, 10%
}
Decision rules:
| p-value | Conclusion | Action |
|---|---|---|
| < 0.01 | Strongly stationary | Can be used directly for regression / modeling |
| 0.01-0.05 | Stationary | Usable |
| 0.05-0.10 | Weak evidence | Difference the series and retest |
| > 0.10 | Non-stationary | Must difference or handle with cointegration |
Stationarity of common financial series:
| Series | Typical Result | Treatment |
|---|---|---|
| Price series | Non-stationary (unit root) | Use log returns |
| Log returns | Stationary | Can be used directly |
| PE / PB series | Usually non-stationary | Use changes or logs |
| Volatility series | Usually stationary | Can be used directly |
| Volume | May be non-stationary | Use logs or standardization |
2. Cointegration Test
Purpose: determine whether two non-stationary series share a long-run equilibrium relationship (the foundation of pair trading / statistical arbitrage).
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.
- 1mo ago First seen · 451 lines · 41 tokens per session scan A 45f3c6d5cd6f
quant-statistics is a skill published in the GitHub repository skloxo/TideTrading (13 stars, last pushed 5d ago), licensed MIT. It adds 41 tokens to every session and 3,972 once invoked, about $0.0002 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 2026-09-03.
Other skills, from other repositories
daily-deep-brief
A scheduled, pre-market investment briefing for Hong Kong and United States stocks. A deterministic preparation step gathers data and an agent adds judgment, while a later step validates and publishes the result.
hk-stock-analysis
A workspace-aware analysis workflow for Hong Kong-listed stocks. It retrieves prices, technical indicators, market comparisons, and news through a local data pipeline, then adds Hong Kong-specific investment context.
qpanda-qubo
A workflow for turning small binary optimisation problems into QUBO models. QUBO represents choices as zero-or-one variables and uses a formula to score each combination.
us-stock-analysis
Workspace-aware US stock analysis for kcn. Routes through clawock analyze-us / clawock us-quotes instead of generic web search, then layers fundamental/technical/news analysis on top. Use when user asks to analyze a US ticker (e.g. "analyze AAPL", "look at RKLB", "compare TSLA vs NVDA"), check earnings, run…
portfolio-swarm-review
Multi-agent swarm review of kcn's current holdings. Inspired by TauricResearch/TradingAgents framework already in workspace — three-tier analysis (analysts → bull/bear debate → risk debate + judge) with confidence scoring. Use for post-close reviews, holiday/next-session planning, pre-add sizing decisions, and any…
investment-decision
Run a clawock investment decision — read the prepared request, research with the host's own tools, write decision.json with evidence and an explicit bull/bear debate, and let Python validate and settle. Use when the user asks for an investment decision or a clawock run request is present.