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
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 restates the skill name without providing any meaningful information about capabilities, actions, or usage triggers. It lacks concrete actions, natural trigger terms, and explicit guidance on when Claude should select this skill. It would be nearly indistinguishable from any generic data analytics skill in a large skill library.
Suggestions
Add specific concrete actions the skill performs, e.g., 'Decomposes time series data into trend, seasonal, and residual components using methods like STL, classical decomposition, or moving averages.'
Add an explicit 'Use when...' clause with natural trigger terms, e.g., 'Use when the user asks about seasonal patterns, trend extraction, time series decomposition, cyclical analysis, or separating signal from noise in temporal data.'
Remove the duplicate trigger term and expand with varied natural language phrases users might say, such as 'seasonality', 'trend analysis', 'decompose', 'time series components', 'STL decomposition'.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | The description names the domain ('time series decomposer', 'Data Analytics') but provides no concrete actions. There is no mention of what the skill actually does—no specific operations like 'decompose into trend, seasonal, and residual components' or 'identify cyclical patterns'. | 1 / 3 |
Completeness | The description fails to answer 'what does this do' beyond the name itself, and there is no explicit 'when should Claude use it' clause. The 'Triggers on' line is just the skill name repeated, not meaningful trigger guidance. | 1 / 3 |
Trigger Term Quality | The only trigger terms listed are 'time series decomposer' repeated twice. It misses natural user phrases like 'decompose time series', 'trend analysis', 'seasonal decomposition', 'STL decomposition', 'seasonality', or 'time series components'. | 1 / 3 |
Distinctiveness Conflict Risk | The term 'time series decomposer' is fairly niche and unlikely to conflict with many other skills, but the lack of specificity about what it does versus other time series or data analytics skills creates some overlap risk. | 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 substantive content about time series decomposition. It contains only generic boilerplate about activation triggers and vague capability claims, with no executable code, no concrete methodology, and no actionable guidance. It would provide no value to Claude beyond what it already knows.
Suggestions
Add concrete, executable code examples for time series decomposition (e.g., using statsmodels seasonal_decompose or STL with specific parameters and sample data).
Define a clear workflow: load data → check stationarity → choose decomposition method (additive/multiplicative) → decompose → validate components → visualize results, with validation checkpoints.
Remove all generic trigger/activation boilerplate and replace with domain-specific guidance on when to use additive vs multiplicative decomposition, handling missing values, and interpreting trend/seasonal/residual components.
Add concrete output examples showing what decomposed components look like and how to validate the decomposition quality (e.g., residual analysis).
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is padded with generic filler that tells Claude nothing useful. Phrases like 'Provides step-by-step guidance' and 'Follows industry best practices' are vacuous. It explains trigger mechanics Claude already knows and contains no domain-specific information about time series decomposition. | 1 / 3 |
Actionability | There is zero concrete guidance—no code, no commands, no algorithms, no examples of actual time series decomposition (e.g., STL, seasonal_decompose, additive vs multiplicative). The skill describes rather than instructs. | 1 / 3 |
Workflow Clarity | No workflow, steps, or process is defined. The skill claims to provide 'step-by-step guidance' but contains none. There are no validation checkpoints or sequenced operations. | 1 / 3 |
Progressive Disclosure | The content is a monolithic block of generic boilerplate with no references to supporting files, no structured sections with real content, and no bundle files to support it. There is nothing to navigate to. | 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
3a2d27d
- Reviewed
Table of Contents
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