quant-statistics

Quantitative statistical methods: ADF unit-root / cointegration tests, GARCH volatility modeling, regression diagnostics (heteroskedasticity / autocorrelation), Bootstrap, and hypothesis testing.

Quality

73%

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Securityby

Passed

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tessl review fix ./agent/src/skills/quant-statistics/SKILL.md

Quality

Content

80%

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

The body is highly actionable and token-efficient with executable code and decision tables, but it is a monolithic reference with no progressive-disclosure split and only implicit feedback loops.

Suggestions

Add explicit validation/retry checkpoints to the fitting workflows (e.g. check GARCH parameter stationarity α+β<1 and re-fit with alternate distribution if diagnostics fail).

Split the dense reference into a separate file (e.g. REFERENCE.md) and keep SKILL.md as a concise overview with one-level-deep links.

Dimension	Reasoning	Score
Conciseness	Lean, executable code with decision tables and minimal prose; it assumes Claude's competence and does not explain what libraries or basic statistics are.	3 / 3
Actionability	Provides complete, copy-paste-ready functions (e.g. adf_test, fit_garch, bootstrap_statistic) with concrete decision-rule tables and fix recommendations.	3 / 3
Workflow Clarity	Steps and a regression-diagnostics checklist are present, but the modeling sections lack explicit validate→fix→retry feedback loops despite fragility in fitting/forecast workflows.	2 / 3
Progressive Disclosure	A single ~440-line file is well-sectioned but monolithic; there are no bundle files and no references split out, so an overview-vs-detail split is absent.	2 / 3
	Total	10 / 12 Passed

Description

67%

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

The description is specific and well-scoped to quantitative statistics, but it omits any explicit 'Use when' guidance, leaving the when-to-use half of the question only implied.

Suggestions

Add an explicit 'Use when...' clause naming natural user triggers, e.g. 'Use when testing stationarity/cointegration, modeling volatility, or diagnosing regressions in quant research.'

Soften pure jargon with common phrasings users say (e.g. 'volatility modeling', 'spurious regression checks') alongside ADF/GARCH.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete capabilities — 'ADF unit-root / cointegration tests, GARCH volatility modeling, regression diagnostics (heteroskedasticity / autocorrelation), Bootstrap, and hypothesis testing' — rather than vague language.	3 / 3
Completeness	Clearly states what the skill does but provides no explicit 'Use when...' trigger guidance, which the rubric caps at 2.	2 / 3
Trigger Term Quality	Relevant technical terms (ADF, cointegration, GARCH) appear but are jargon-heavy and lack common phrasings a user might naturally say; no everyday variations are covered.	2 / 3
Distinctiveness Conflict Risk	Occupies a clear niche (quantitative statistical methods for investing) with distinct triggers unlikely to overlap with unrelated skills.	3 / 3
	Total	10 / 12 Passed

Validation

93%

Warnings & errors only

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 15 / 16 Passed

Validation for skill structure

Criteria	Description	Result
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning

	Total	15 / 16 Passed

Repository: HKUDS/Vibe-Trading
Path: agent/src/skills/quant-statistics/SKILL.md
Commit: 8643fcd

Reviewed: 2 days ago

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