anomaly-detector
Install with Tessl CLI
npx tessl i github:jeremylongshore/claude-code-plugins-plus-skills --skill anomaly-detectorAnomaly Detector - Auto-activating skill for Data Analytics. Triggers on: anomaly detector, anomaly detector Part of the Data Analytics skill category.
Overall
score
19%
Does it follow best practices?
Validation for skill structure
Activation
7%This description is severely underdeveloped, essentially serving as a placeholder rather than a functional skill description. It lacks any concrete actions, meaningful trigger terms, or guidance on when to use the skill. The redundant trigger terms and absence of a 'Use when...' clause make it nearly useless for skill selection among multiple options.
Suggestions
Add specific concrete actions the skill performs, e.g., 'Detects statistical outliers, identifies unusual patterns in time series data, flags data points that deviate from expected distributions'
Include a 'Use when...' clause with natural trigger terms users would say, e.g., 'Use when the user mentions outliers, unusual data points, anomalies, irregularities, or asks to find unexpected values in datasets'
Specify what types of data or domains this anomaly detection applies to (e.g., time series, tabular data, sensor readings) to distinguish it from other analytics skills
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | The description only names the skill ('Anomaly Detector') and category ('Data Analytics') without describing any concrete actions. There are no specific capabilities listed like 'detect outliers', 'identify unusual patterns', or 'flag statistical anomalies'. | 1 / 3 |
Completeness | The description fails to answer 'what does this do' beyond the name, and provides no 'when should Claude use it' guidance. There is no 'Use when...' clause or equivalent explicit trigger guidance. | 1 / 3 |
Trigger Term Quality | The triggers listed are just 'anomaly detector' repeated twice, which is redundant and misses natural variations users would say like 'outliers', 'unusual data', 'detect anomalies', 'find irregularities', or 'statistical outliers'. | 1 / 3 |
Distinctiveness Conflict Risk | While 'anomaly detector' is somewhat specific to a niche, the lack of detail about what types of anomalies or data it handles means it could overlap with other data analysis skills. The category mention 'Data Analytics' is too broad to differentiate. | 2 / 3 |
Total | 5 / 12 Passed |
Implementation
0%This skill is entirely meta-content describing what an anomaly detector skill would do, without providing any actual anomaly detection guidance. It contains no executable code, no specific algorithms (isolation forest, z-score, DBSCAN, etc.), no SQL examples, and no concrete instructions. The content is essentially a placeholder that fails all evaluation dimensions.
Suggestions
Add concrete, executable code examples for common anomaly detection techniques (e.g., z-score method, IQR-based detection, isolation forest with sklearn)
Include specific SQL queries for detecting anomalies in time-series data or identifying outliers in datasets
Provide a clear workflow: data preparation → method selection → detection → validation → interpretation of results
Replace generic capability claims with actual implementation guidance - show don't tell
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is padded with generic boilerplate that provides no actual value. Phrases like 'provides automated assistance' and 'follows industry best practices' are vague filler that Claude doesn't need. | 1 / 3 |
Actionability | There is zero concrete guidance - no code, no commands, no specific techniques for anomaly detection. The content describes what the skill supposedly does but never instructs how to actually detect anomalies. | 1 / 3 |
Workflow Clarity | No workflow is provided whatsoever. Claims to provide 'step-by-step guidance' but contains no actual steps, no sequence, and no validation checkpoints for anomaly detection tasks. | 1 / 3 |
Progressive Disclosure | The content is a monolithic block of meta-description with no structure pointing to actual implementation details, examples, or reference materials. No useful navigation or content organization. | 1 / 3 |
Total | 4 / 12 Passed |
Validation
69%Validation — 11 / 16 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
description_trigger_hint | Description may be missing an explicit 'when to use' trigger hint (e.g., 'Use when...') | Warning |
allowed_tools_field | 'allowed-tools' contains unusual tool name(s) | Warning |
metadata_version | 'metadata' field is not a dictionary | Warning |
frontmatter_unknown_keys | Unknown frontmatter key(s) found; consider removing or moving to metadata | Warning |
body_steps | No step-by-step structure detected (no ordered list); consider adding a simple workflow | Warning |
Total | 11 / 16 Passed | |
Reviewed
Table of Contents
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