aeon

This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.

Quality

78%

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Securityby

Passed

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tessl review fix ./backend/cli/skills/data-engineering/aeon/SKILL.md

Quality

Content

72%

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

A well-structured, highly actionable skill body with excellent progressive disclosure, held back slightly by minor redundancy and the absence of explicit validation checkpoints in its workflows.

Suggestions

De-duplicate the classification "Algorithm Selection" list with the later "Algorithm Selection Guide" to trim tokens.

Add an explicit validation step to the model-selection workflow (e.g. score against a held-out split before adopting an algorithm).

Dimension	Reasoning	Score
Conciseness	The body is mostly efficient and assumes Claude's competence, but the overview restates the description and the "Algorithm Selection" list under classification duplicates the later "Algorithm Selection Guide", leaving room to tighten.	2 / 3
Actionability	Each capability has executable, copy-paste-ready Python with correct imports and realistic dataset names, plus concrete per-goal algorithm recommendations — fully actionable.	3 / 3
Workflow Clarity	Pipelines and model-selection steps are sequenced, but there are no explicit validation checkpoints or error-recovery feedback loops, and guidance is advisory rather than a gated sequence.	2 / 3
Progressive Disclosure	Clear overview with one-level-deep references signaled in every section and a consolidated reference listing; all 11 referenced files exist and content is appropriately split out of the overview.	3 / 3
	Total	10 / 12 Passed

Description

85%

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

A strong description: concrete, third-person, and explicit about both capabilities and trigger conditions, with only minor technical phrasing holding back trigger-term naturalness.

Suggestions

Rephrase the opener to lead with the capability (e.g. "Time series machine learning for classification, regression, ...") instead of "This skill should be used".

Soften jargon like "scikit-learn compatible APIs" and "time-indexed observations" toward terms a user would naturally say.

Dimension	Reasoning	Score
Specificity	Enumerates multiple concrete tasks — "classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search" — matching the anchor for listing several specific actions.	3 / 3
Completeness	States both what it does (enumerated TS ML tasks) and when to use it via an explicit "Use when working with temporal data, sequential patterns, or time-indexed observations..." clause, satisfying the top anchor.	3 / 3
Trigger Term Quality	Includes natural terms like "time series", "forecasting", "anomaly detection", and "temporal data", but mixes in technical phrasing ("scikit-learn compatible APIs", "time-indexed observations") and the stilted opener "This skill should be used", so it falls short of full natural-term coverage.	2 / 3
Distinctiveness Conflict Risk	The time-series ML niche is clearly bounded and the "beyond standard ML approaches" qualifier separates it from generic ML skills, making wrong-skill triggering unlikely.	3 / 3
	Total	11 / 12 Passed

Validation

87%

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 — 14 / 16 Passed

Validation for skill structure

Criteria	Description	Result
metadata_version	'metadata.version' is missing	Warning
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning

	Total	14 / 16 Passed

Repository: synthetic-sciences/openscience
Path: backend/cli/skills/data-engineering/aeon/SKILL.md
Commit: 52845c3

Reviewed: about 10 hours ago

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