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.
87
86%
Does it follow best practices?
Impact
84%
1.33xAverage score across 3 eval scenarios
Passed
No known issues
Quality
Discovery
100%Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.
This is a strong skill description that clearly enumerates specific time series ML capabilities, includes natural trigger terms users would employ, and explicitly states both what the skill does and when to use it. The description effectively differentiates itself from general ML skills by emphasizing temporal/sequential data and specialized algorithms. It uses proper third-person voice throughout.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. These are well-defined ML task types. | 3 / 3 |
Completeness | Clearly answers both what (time series ML tasks including classification, regression, clustering, etc.) and when ('Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches'). Explicit 'Use when' clause is present. | 3 / 3 |
Trigger Term Quality | Includes strong natural keywords users would say: 'time series', 'classification', 'regression', 'forecasting', 'anomaly detection', 'temporal data', 'sequential patterns', 'univariate', 'multivariate', 'scikit-learn'. Good coverage of both technical and natural terms. | 3 / 3 |
Distinctiveness Conflict Risk | Clearly distinguished from general ML skills by focusing specifically on time series tasks and temporal data. The phrase 'beyond standard ML approaches' and 'scikit-learn compatible APIs' further narrow the niche, making it unlikely to conflict with general ML or non-temporal data skills. | 3 / 3 |
Total | 12 / 12 Passed |
Implementation
72%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This is a well-structured skill that provides comprehensive, actionable coverage of the aeon toolkit with excellent progressive disclosure to reference files. Its main weaknesses are moderate verbosity (some sections explain things Claude already knows, like general ML best practices) and lack of validation checkpoints in workflows. The executable code examples and algorithm selection guides are strong assets.
Suggestions
Trim the 'When to Use This Skill' section and 'Best Practices > Model Selection' items 1-4, as these are general ML advice Claude already knows—focus on aeon-specific gotchas instead.
Add validation checkpoints to workflows, e.g., verifying X_train.shape matches expected (n_samples, n_channels, n_timepoints) format before fitting, and checking for NaN in predictions after model inference.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is fairly comprehensive but includes some unnecessary sections like 'When to Use This Skill' (which restates what the description already covers) and the 'Best Practices' section contains advice Claude already knows (use validation, start simple, compare baselines). The algorithm selection guide and code examples are valuable, but the overall document is quite long (~250 lines) with some redundancy between sections. | 2 / 3 |
Actionability | Every section includes fully executable, copy-paste ready Python code with proper imports, realistic dataset names, and complete workflows. The algorithm selection tables provide concrete recommendations mapped to specific use cases, and the pipeline examples show real sklearn integration patterns. | 3 / 3 |
Workflow Clarity | The 'Common Workflows' section provides clear multi-step pipelines, but there are no validation checkpoints or error handling steps. For ML workflows involving data preparation, model training, and prediction, there's no guidance on verifying data shapes, checking for NaN outputs, or validating model performance before deployment. The data preparation steps are listed but lack a clear sequential workflow with verification. | 2 / 3 |
Progressive Disclosure | Excellent progressive disclosure with a clear overview in the main file and well-signaled one-level-deep references to 11 separate reference files covering each capability area. Each section provides a quick start inline and points to detailed documentation for the full algorithm catalog. | 3 / 3 |
Total | 10 / 12 Passed |
Validation
90%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 10 / 11 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
metadata_version | 'metadata.version' is missing | Warning |
Total | 10 / 11 Passed | |
- Repository
- K-Dense-AI/claude-scientific-skills
- Commit
b58ad7e
- Reviewed
Table of Contents
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