anomaly-detector
Anomaly Detector - Auto-activating skill for Data Analytics. Triggers on: anomaly detector, anomaly detector Part of the Data Analytics skill category.
33
0%
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
0%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 essentially a placeholder with no substantive content. It fails to describe what the skill does, when it should be used, or how it differs from other data analytics skills. The repeated trigger term and lack of any concrete actions or use-case guidance make it ineffective for skill selection.
Suggestions
Add specific concrete actions the skill performs, e.g., 'Detects outliers and anomalous patterns in time series data, flags statistical deviations, and identifies unusual spikes or drops in metrics.'
Add an explicit 'Use when...' clause with natural trigger terms, e.g., 'Use when the user mentions outliers, anomalies, unusual patterns, spikes, deviations, or asks to find abnormal data points.'
Include distinct trigger terms users would naturally say, such as 'outlier detection', 'unusual behavior', 'spike', 'abnormal values', 'statistical anomaly', to differentiate from generic data analytics skills.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | The description only names the skill ('Anomaly Detector') and its category ('Data Analytics') but provides no concrete actions—no mention of what it actually does, such as detecting outliers, flagging unusual patterns, or analyzing time series data. | 1 / 3 |
Completeness | Neither the 'what does this do' nor the 'when should Claude use it' questions are meaningfully answered. There is no explanation of capabilities and no explicit 'Use when...' clause with trigger guidance. | 1 / 3 |
Trigger Term Quality | The only trigger terms listed are 'anomaly detector' repeated twice. There are no natural user-facing keywords like 'outlier', 'unusual pattern', 'spike', 'deviation', or 'anomalous data' that a user would naturally say. | 1 / 3 |
Distinctiveness Conflict Risk | The description is extremely generic—'Data Analytics' could overlap with many other skills, and 'anomaly detector' alone without specifying the type of data, methods, or outputs makes it indistinguishable from any analytics-related skill. | 1 / 3 |
Total | 4 / 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 is an empty shell with no actual content. It contains only meta-descriptions and trigger phrases but provides zero actionable guidance on anomaly detection—no statistical methods (e.g., Z-score, IQR, isolation forests), no code examples, no workflows, and no concrete instructions. It would be entirely useless to Claude as it adds nothing beyond what the skill's title already conveys.
Suggestions
Add concrete, executable code examples for common anomaly detection techniques (e.g., Z-score method, IQR-based detection, Isolation Forest with scikit-learn) with sample data and expected outputs.
Define a clear workflow: data preparation → method selection → detection → validation → reporting, with explicit validation checkpoints at each stage.
Replace the abstract 'Capabilities' and 'Example Triggers' sections with actual technical content—SQL queries for detecting anomalies in datasets, Python/pandas code for statistical analysis, and visualization examples.
Add references to detailed sub-documents for advanced topics like time-series anomaly detection, multivariate methods, or domain-specific thresholds.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is entirely filler and meta-description. It explains what the skill does in abstract terms without providing any actual knowledge or instructions. Every section restates the same vague information about 'anomaly detector' without adding substance. | 1 / 3 |
Actionability | There is zero concrete guidance—no code, no commands, no algorithms, no specific techniques for anomaly detection. The content describes rather than instructs, offering only vague promises like 'provides step-by-step guidance' without actually providing any. | 1 / 3 |
Workflow Clarity | No workflow, steps, or process is defined. There are no sequences, no validation checkpoints, and no actual procedure for performing anomaly detection. The skill merely states it will provide guidance without doing so. | 1 / 3 |
Progressive Disclosure | The content is a flat, repetitive structure with no references to detailed materials, no links to examples or advanced topics, and no meaningful organization of content across sections. Each section essentially repeats the same non-information. | 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
c8a915c
- Reviewed
Table of Contents
Is this your skill?
If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.