spending-analysis
Patrones para análisis profundo de gastos por subcategoría, detección de anomalías, y comparaciones temporales. Incluye algoritmos de clustering de gastos, detección de outliers, y análisis de tendencias. Usar cuando se trabaje con análisis detallado, reportes, o detección de patrones de gasto.
77
Does it follow best practices?
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npx tessl skill review --optimize ./path/to/skillValidation for skill structure
Discovery
85%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 well-crafted skill description that clearly articulates specific capabilities (expense analysis, anomaly detection, clustering, trend analysis) and includes explicit 'Usar cuando' guidance. The main weakness is the use of some technical jargon (clustering, outliers) that users might not naturally use when requesting this functionality.
Suggestions
Add more natural user-facing trigger terms alongside technical ones, e.g., 'gastos inusuales' instead of just 'outliers', 'agrupación de gastos' alongside 'clustering'
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: 'análisis profundo de gastos por subcategoría', 'detección de anomalías', 'comparaciones temporales', 'algoritmos de clustering', 'detección de outliers', 'análisis de tendencias'. | 3 / 3 |
Completeness | Clearly answers both what (expense analysis, anomaly detection, temporal comparisons, clustering algorithms) AND when ('Usar cuando se trabaje con análisis detallado, reportes, o detección de patrones de gasto') with explicit trigger guidance. | 3 / 3 |
Trigger Term Quality | Contains relevant domain keywords like 'gastos', 'subcategoría', 'anomalías', 'reportes', 'patrones de gasto', but uses some technical jargon ('clustering', 'outliers') that users may not naturally say. Missing common variations like 'expenses', 'spending analysis', or simpler terms. | 2 / 3 |
Distinctiveness Conflict Risk | Has a clear niche focused specifically on expense/spending analysis with subcategory breakdown, anomaly detection, and pattern recognition. The combination of financial analysis + anomaly detection + clustering creates distinct triggers unlikely to conflict with general data analysis skills. | 3 / 3 |
Total | 11 / 12 Passed |
Implementation
64%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This skill provides comprehensive, executable Dart code for spending analysis with solid pattern detection algorithms and budget rule analysis. Its main strengths are actionability and concrete implementations. However, it could be more concise by removing obvious explanations, and would benefit from explicit validation workflows and better progressive disclosure through external references.
Suggestions
Add explicit validation steps for data quality checks (e.g., 'If transactions.length < 60, return InsufficientDataError with specific guidance')
Move the visualization recommendations to a separate VISUALIZATIONS.md file and link to it
Remove the 'Notas de Implementación' section or make it more specific - items like 'Privacidad - No enviar datos detallados a la nube' are obvious guidance
Add error handling workflows showing what to do when pattern detection returns null or anomaly detection has insufficient historical data
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is mostly efficient with well-structured code examples, but includes some unnecessary explanations (e.g., visualization descriptions that Claude could infer, verbose comments in Spanish). The 'Notas de Implementación' section contains basic guidance Claude already knows. | 2 / 3 |
Actionability | Provides fully executable Dart code with complete class definitions, methods, and algorithms. The pattern detection, anomaly detection, and budget rule analysis are copy-paste ready with concrete implementations. | 3 / 3 |
Workflow Clarity | The code shows clear algorithms and data flow within each analyzer, but lacks explicit validation checkpoints or error handling workflows. No guidance on what to do when analysis fails or data is insufficient beyond a simple minDaysOfData parameter. | 2 / 3 |
Progressive Disclosure | Content is organized into logical sections (Subcategory Analysis, Pattern Detection, Budget Rule), but everything is in one monolithic file. The visualization section could be a separate reference, and there are no links to related skills or detailed documentation. | 2 / 3 |
Total | 9 / 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 |
|---|---|---|
skill_md_line_count | SKILL.md is long (608 lines); consider splitting into references/ and linking | Warning |
frontmatter_unknown_keys | Unknown frontmatter key(s) found; consider removing or moving to metadata | Warning |
Total | 9 / 11 Passed | |
- Reviewed
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