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.

Quality

—

Does it follow best practices?

Impact

—

No eval scenarios have been run

Securityby

Passed

No known issues

Quality

Content

27%

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

The body is a generic, descriptive overview with no executable guidance and no navigation to the bundled materials, several placeholder sections, and broken script references in the bundle.

Suggestions

Add concrete, executable guidance: show how to run the bundled scripts (e.g., a code block invoking forecast.py with sample arguments) or a minimal ARIMA/Prophet snippet with expected output.

Wire the body to the real bundle — link to assets/configuration_template.json and assets/example_data.csv, and either ship the listed scripts or remove the broken references in scripts/README.md.

Insert a validation checkpoint into the workflow (e.g., evaluate the model with RMSE/MAE and only report the forecast if error is acceptable) and delete placeholder sections like 'Output' and 'Resources'.

Dimension	Reasoning	Score
Conciseness	The body avoids explaining basic concepts Claude already knows, but contains generic placeholder sections ('Prerequisites', 'Instructions', 'Output', 'Resources') such as 'The skill produces structured output relevant to the task.' that add little and could be trimmed.	2 / 3
Actionability	There is no executable code, no commands, and no specific parameters; steps like 'Select and train a suitable forecasting model (e.g., ARIMA or Prophet)' describe rather than instruct, and the bundled scripts are never referenced.	1 / 3
Workflow Clarity	'How It Works' gives a three-step sequence (analyze, select model, predict), but the steps are conceptual and lack validation/verification checkpoints such as evaluating the model before reporting forecasts.	2 / 3
Progressive Disclosure	The body is a self-contained overview that never links to the bundled assets or scripts, the 'Resources' section is placeholder text, and the scripts README references files (forecast.py, evaluate.py, preprocess.py) that do not exist.	1 / 3
	Total	6 / 12 Passed

Description

50%

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

The description conveys the skill's purpose and includes a trigger clause, but it reads as auto-generated: truncated text and generic boilerplate ('Use when appropriate context detected') weaken the trigger guidance and distinctiveness.

Suggestions

Replace the truncated/generic trigger clause with concrete natural phrasing, e.g. 'Use when the user asks to forecast, predict, or project future values of a time series (e.g., sales, traffic, demand).'

List specific concrete actions (e.g., 'fit ARIMA/Prophet models, decompose trend and seasonality, produce forecasts with confidence intervals') to raise specificity.

Remove placeholder boilerplate ('Trigger with relevant phrases based on skill purpose') that adds no real trigger signal.

Dimension	Reasoning	Score
Specificity	Phrases like 'forecast future values based on historical time series data' and 'analyzes time-dependent data to identify trends, seasonality, and other patterns' name the domain and a couple of actions, but do not enumerate multiple concrete operations.	2 / 3
Completeness	It states what the skill does and includes a 'use this skill when...' clause, but the trigger guidance is truncated and diluted by placeholder text, so the 'when' is weak rather than crisp and explicit.	2 / 3
Trigger Term Quality	'use this skill when the user asks to predict future values of a time ser...' gives some natural keywords (predict, forecast) but is truncated and followed by generic boilerplate ('Use when appropriate context detected. Trigger with relevant phrases based on skill purpose') instead of covering common variations.	2 / 3
Distinctiveness Conflict Risk	Time series forecasting is a recognizable niche, yet broad phrasing ('trends, seasonality, and other patterns') and generic 'appropriate context detected' wording could overlap with general data-analysis skills.	2 / 3
	Total	8 / 12 Passed

Validation

87%

Warnings & errors only

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 14 / 16 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	14 / 16 Passed

Repository: jeremylongshore/claude-code-plugins-plus-skills
Commit: 2cafb23

Reviewed: 2 days ago

Table of Contents

Discovery Implementation Validation

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