time-series-decomposer
Time Series Decomposer - Auto-activating skill for Data Analytics. Triggers on: time series decomposer, time series decomposer Part of the Data Analytics skill category.
36
Quality
3%
Does it follow best practices?
Impact
96%
1.00xAverage 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/time-series-decomposer/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 essentially a placeholder that provides almost no useful information for skill selection. It lacks concrete actions, natural trigger terms, and explicit guidance on when to use it. The only distinguishing element is the specific technique name, but even that is undermined by the duplicate trigger term and missing capability details.
Suggestions
Add specific capabilities: 'Decomposes time series data into trend, seasonal, and residual components using STL or classical decomposition methods'
Include a 'Use when...' clause with natural triggers: 'Use when analyzing seasonality, extracting trends from time series, or when user mentions decomposition, seasonal patterns, or trend analysis'
Add relevant file types and data formats: 'Works with CSV, Excel time series data, or pandas DataFrames with datetime indices'
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | The description only names the skill ('Time Series Decomposer') without describing any concrete actions. There are no specific capabilities listed like 'decompose seasonal patterns', 'extract trends', or 'identify cyclical components'. | 1 / 3 |
Completeness | The description fails to answer 'what does this do' beyond the name, and has no 'Use when...' clause or equivalent guidance for when Claude should select this skill. Both what and when are very weak. | 1 / 3 |
Trigger Term Quality | The trigger terms are just the skill name repeated twice ('time series decomposer, time series decomposer'). Missing natural user terms like 'seasonal analysis', 'trend extraction', 'decomposition', 'STL', 'seasonality', or 'time series analysis'. | 1 / 3 |
Distinctiveness Conflict Risk | The term 'Time Series Decomposer' is somewhat specific to a particular analytical technique, but without describing what it actually does, it could overlap with other time series or data analytics skills in a large skill library. | 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 is an empty template with no actual content about time series decomposition. It contains only meta-descriptions of what a skill should do without any concrete guidance, code examples, or domain-specific knowledge. The content provides zero value as it teaches Claude nothing about decomposing time series data (trend, seasonality, residuals, etc.).
Suggestions
Add executable Python code examples using statsmodels or similar libraries for seasonal decomposition (e.g., seasonal_decompose function)
Include concrete guidance on choosing decomposition methods (additive vs multiplicative) based on data characteristics
Provide specific workflow steps: load data → check stationarity → select decomposition type → extract components → validate results
Remove all boilerplate 'when to use' and 'example triggers' sections and replace with actual technical content about STL decomposition, classical decomposition, or other methods
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is entirely boilerplate with no actual time series decomposition information. It explains what the skill does in abstract terms without providing any concrete knowledge Claude doesn't already have. | 1 / 3 |
Actionability | No concrete code, commands, or specific guidance is provided. The content only describes capabilities in vague terms like 'provides step-by-step guidance' without actually providing any guidance. | 1 / 3 |
Workflow Clarity | No workflow, steps, or process is defined. The skill claims to provide 'step-by-step guidance' but contains zero actual steps for performing time series decomposition. | 1 / 3 |
Progressive Disclosure | No meaningful structure or references to additional resources. The content is a template with placeholder sections that don't organize any real 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
0c08951
- 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.