forecasting-time-series-data

tessl i github:jeremylongshore/claude-code-plugins-plus-skills --skill forecasting-time-series-data

Process this skill enables AI assistant to forecast future values based on historical time series data. it analyzes time-dependent data to identify trends, seasonality, and other patterns. use this skill when the user asks to predict future values of a time ser... Use when appropriate context detected. Trigger with relevant phrases based on skill purpose.

44%

Overall

Validation — 81%

Implementation — 20%

Activation — 50%

SKILL.md

Review

Evals

Validation

81%

Warnings & errors only

Criteria	Description	Result
allowed_tools_field	'allowed-tools' contains unusual tool name(s)	Warning
metadata_version	'metadata' field is not a dictionary	Warning
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning

	Total	13 / 16 Passed

Implementation

20%

This skill is a high-level description of time series forecasting rather than actionable guidance for Claude. It lacks any executable code, specific library usage, or concrete implementation details. The content is padded with generic boilerplate sections and explanations of concepts Claude already understands.

Suggestions

Replace abstract descriptions with executable Python code examples using specific libraries (e.g., statsmodels for ARIMA, prophet for Prophet) with actual import statements and function calls

Remove generic boilerplate sections (Prerequisites, Instructions, Error Handling, Integration) that provide no skill-specific value

Add concrete validation steps: how to check data stationarity, how to evaluate model fit (with actual code for MAE/RMSE calculation), and when to retry with different parameters

Eliminate the redundant overview paragraph and 'When to Use This Skill' section - Claude doesn't need to be told when to use forecasting

Dimension	Reasoning	Score
Conciseness	Extremely verbose with redundant explanations (overview repeated twice), explains concepts Claude already knows (what time series forecasting is, what ARIMA does), and includes generic boilerplate sections (Prerequisites, Instructions, Error Handling) that add no value.	1 / 3
Actionability	No executable code, no specific commands, no concrete implementation details. The entire skill describes what 'the skill will do' abstractly without providing any actual Python code, library imports, or copy-paste ready examples.	1 / 3
Workflow Clarity	Steps are listed (analyze, select model, predict) but lack any validation checkpoints, error recovery steps, or concrete verification methods. No guidance on how to validate model performance or handle forecasting failures.	2 / 3
Progressive Disclosure	Content is organized into sections but is monolithic with no references to external files for detailed API usage, model parameters, or advanced configurations. The 'Resources' section mentions documentation but provides no actual links.	2 / 3
	Total	6 / 12 Passed

Activation

50%

This description has a clear domain focus on time series forecasting but suffers from truncation and generic boilerplate language. The core capability is identifiable, but the 'Use when appropriate context detected. Trigger with relevant phrases based on skill purpose.' ending is meaningless filler that provides no selection guidance.

Suggestions

Complete the truncated description and remove the generic boilerplate 'Use when appropriate context detected. Trigger with relevant phrases based on skill purpose.' - replace with specific trigger terms like 'forecast', 'predict', 'time series', 'future values', 'trend analysis'

Add explicit 'Use when...' clause with concrete scenarios: 'Use when user mentions forecasting, predicting trends, time series analysis, or asks about future values of sequential data'

List specific concrete actions: 'Analyzes historical data patterns, detects seasonality and trends, generates forecasts with confidence intervals, handles missing values in time series'

Dimension	Reasoning	Score
Specificity	Names the domain (time series forecasting) and some actions (forecast future values, analyze time-dependent data, identify trends/seasonality/patterns), but the description is truncated and uses somewhat generic language like 'AI assistant' rather than concrete specific actions.	2 / 3
Completeness	The 'what' is partially addressed (forecasting time series data), and there's an attempt at 'when' ('when the user asks to predict future values'), but it's truncated and the generic 'Use when appropriate context detected' provides no meaningful guidance.	2 / 3
Trigger Term Quality	Contains some relevant keywords like 'forecast', 'time series', 'predict future values', 'trends', 'seasonality', but the truncation cuts off potentially useful terms. The boilerplate 'Trigger with relevant phrases based on skill purpose' adds no value.	2 / 3
Distinctiveness Conflict Risk	Time series forecasting is a reasonably specific niche that distinguishes it from general data analysis, but the truncated description and generic trigger phrases could cause overlap with other analytics or prediction skills.	2 / 3
	Total	8 / 12 Passed

Reviewed

16 days ago

Table of Contents

Validation Implementation Activation

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