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

89%

Does it follow best practices?

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Securityby

Passed

No findings from the security scan

Quality

Content

86%Weight 40%Scale 1-5

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 that pairs executable quick-starts with one-level-deep references for detail. It loses a little conciseness to repetition and lacks explicit validation feedback loops in its workflows, but is otherwise strong across all dimensions.

Suggestions

Add an explicit validation/checkpoint step to at least one workflow (e.g., verify pipeline output shape or score before reporting results) to model error-recovery feedback loops.

Consolidate the per-capability algorithm recommendations and the separate 'Algorithm Selection Guide' / 'For Maximum Accuracy' lists, which currently repeat overlapping suggestions (ROCKET, HIVECOTEV2, InceptionTime).

Trim a few redundant code blocks (e.g., ROCKET feature extraction appears in both the Feature Extraction section and the Common Workflows section).

Dimension	Reasoning	Score
Conciseness	Mostly efficient and assumes Claude's competence, but some content repeats across sections (e.g., algorithm-selection recommendations and ROCKET usage appear in multiple places) that could be tightened.	4 / 5
Actionability	Provides copy-paste ready, fully executable code with real imports, dataset names, and scenario-specific algorithm recommendations covering the common cases for every capability.	5 / 5
Workflow Clarity	Sequenced multi-step workflows are present (pipelines, feature-extraction + traditional ML, anomaly detection with visualization), but validation checkpoints and error-recovery feedback loops are implicit rather than explicit.	4 / 5
Progressive Disclosure	Clear overview body with well-signaled, one-level-deep references to references/*.md files, all of which exist and match the in-body listing, with no nested reference chains.	5 / 5
	Total	18 / 20 Passed

Description

92%Weight 40%Scale 1-5

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, specific description that lists concrete capabilities and provides an explicit 'Use when...' trigger clause. It is clearly distinct from generic ML skills and answers both what and when. Minor room to add synonyms or file extensions for even broader trigger coverage.

Dimension	Reasoning	Score
Specificity	Names seven concrete capability domains (classification, regression, clustering, forecasting, anomaly detection, segmentation, similarity search), giving comprehensive coverage of specific actions rather than vague language.	5 / 5
Completeness	Explicitly answers both 'what' (the seven listed tasks) and 'when' via the concrete 'Use when working with temporal data, sequential patterns...' trigger clause.	5 / 5
Trigger Term Quality	Includes natural phrases users say ('time series', 'temporal data', 'sequential patterns', 'time-indexed observations', 'forecasting') but is missing common synonyms and file extensions that would round out coverage.	4 / 5
Distinctiveness Conflict Risk	Occupies a clear niche (time series ML with scikit-learn compatible APIs) with distinct triggers that are unlikely to fire for general-purpose ML skills.	5 / 5
	Total	19 / 20 Passed

Validation

100%

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 — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository: foryourhealth111-pixel/Vibe-Skills
Path: bundled/skills/aeon/SKILL.md
Commit: d5ae560

Reviewed: 1 day ago

Table of Contents

Discovery Implementation Validation

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