anomaly-detector
Anomaly Detector - Auto-activating skill for Data Analytics. Triggers on: anomaly detector, anomaly detector Part of the Data Analytics skill category.
35
Quality
3%
Does it follow best practices?
Impact
93%
0.97xAverage score across 3 eval scenarios
Passed
No known issues
Optimize this skill with Tessl
npx tessl skill review --optimize ./planned-skills/generated/12-data-analytics/anomaly-detector/SKILL.mdQuality
Discovery
7%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 description is severely underdeveloped, functioning more as a label than a useful skill description. It provides no information about what anomaly detection capabilities are offered, what data types it works with, or when Claude should select this skill. The redundant trigger terms and boilerplate category mention add no value for skill selection.
Suggestions
Add specific actions the skill performs, e.g., 'Identifies statistical outliers, detects unusual patterns in time series data, flags data points that deviate from expected ranges'
Include a 'Use when...' clause with natural trigger terms like 'Use when analyzing data for outliers, unusual patterns, spikes, anomalies, or when the user mentions detecting abnormal values'
Specify the data types or contexts this applies to, e.g., 'Works with numerical datasets, time series, sensor data, or transaction logs'
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | The description only names the skill ('Anomaly Detector') and category ('Data Analytics') without describing any concrete actions. There are no verbs indicating what the skill actually does - no mention of detecting, identifying, flagging, or analyzing anomalies. | 1 / 3 |
Completeness | The description fails to answer both 'what does this do' and 'when should Claude use it'. There is no explanation of capabilities and no 'Use when...' clause or equivalent trigger guidance. | 1 / 3 |
Trigger Term Quality | The triggers listed are redundant ('anomaly detector, anomaly detector') and only cover the exact skill name. Missing natural variations users would say like 'outliers', 'unusual patterns', 'detect anomalies', 'find outliers', 'abnormal data', or 'spikes'. | 1 / 3 |
Distinctiveness Conflict Risk | While 'Anomaly Detector' is somewhat specific compared to generic 'data analysis', the lack of detail about what types of anomalies, data formats, or use cases means it could overlap with other data analytics skills. The category mention doesn't help distinguish it. | 2 / 3 |
Total | 5 / 12 Passed |
Implementation
0%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This skill content is an empty template with no substantive guidance on anomaly detection. It contains only meta-descriptions of what the skill claims to do without any actual techniques, code examples, statistical methods, or actionable instructions for detecting anomalies in data.
Suggestions
Add concrete anomaly detection techniques with executable code examples (e.g., Z-score method, IQR-based detection, isolation forests, DBSCAN clustering)
Include specific SQL queries or Python/pandas code for identifying outliers in datasets
Define a clear workflow: data preparation → method selection → threshold configuration → validation → interpretation of results
Remove generic boilerplate sections (Purpose, Capabilities, Example Triggers) and replace with actionable content that teaches anomaly detection
| 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 | No concrete code, commands, algorithms, or specific techniques for anomaly detection are provided. The content describes what the skill does abstractly but never instructs how to actually detect anomalies. | 1 / 3 |
Workflow Clarity | No workflow, steps, or process is defined. Claims to provide 'step-by-step guidance' but contains zero actual steps for performing anomaly detection. | 1 / 3 |
Progressive Disclosure | No references to detailed documentation, examples, or related files. The content is a shallow placeholder with no structure pointing to deeper resources. | 1 / 3 |
Total | 4 / 12 Passed |
Validation
81%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 9 / 11 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
allowed_tools_field | 'allowed-tools' contains unusual tool name(s) | Warning |
frontmatter_unknown_keys | Unknown frontmatter key(s) found; consider removing or moving to metadata | Warning |
Total | 9 / 11 Passed | |
- Commit
0c08951
- Reviewed
Table of Contents
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