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

25%

Does it follow best practices?

Impact

Pending

No eval scenarios have been run

Securityby

Passed

No known issues

Optimize this skill with Tessl

npx tessl skill review --optimize ./plugins/ai-ml/time-series-forecaster/skills/forecasting-time-series-data/SKILL.md

Quality

Discovery

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 identifies a clear domain (time series forecasting) and mentions some relevant capabilities, but it is significantly weakened by a truncated sentence and completely generic boilerplate trigger guidance ('Use when appropriate context detected'). The lack of specific, natural trigger terms and explicit 'when' guidance reduces its effectiveness for skill selection among many options.

Suggestions

Replace the generic boilerplate trigger clause ('Use when appropriate context detected. Trigger with relevant phrases based on skill purpose.') with specific triggers like 'Use when the user asks to forecast, predict trends, analyze time series, project future values, or mentions terms like ARIMA, seasonal patterns, or historical data prediction.'

Complete the truncated sentence and list concrete actions such as 'Fits time series models (ARIMA, exponential smoothing), detects seasonality and trends, generates forecasts with confidence intervals, and visualizes predicted vs actual values.'

Remove first/second person references ('this skill enables AI assistant') and use third person voice (e.g., 'Forecasts future values based on historical time series data').

Dimension	Reasoning	Score
Specificity	It names the domain (time series forecasting) and some actions (forecast future values, analyze time-dependent data, identify trends/seasonality), but the description is truncated and not comprehensive in listing concrete actions.	2 / 3
Completeness	The 'what' is partially addressed (forecasting time series data), but the 'when' clause is pure boilerplate filler ('Use when appropriate context detected') with no explicit, actionable triggers. The description also appears truncated ('time ser...').	2 / 3
Trigger Term Quality	Includes some relevant keywords like 'forecast', 'time series', 'predict future values', 'trends', 'seasonality', but the trigger guidance is entirely generic boilerplate ('Use when appropriate context detected. Trigger with relevant phrases based on skill purpose') rather than listing natural user phrases.	2 / 3
Distinctiveness Conflict Risk	Time series forecasting is a reasonably specific niche, but the generic trigger language and truncated description weaken distinctiveness. It could overlap with general data analysis or statistics skills.	2 / 3
	Total	8 / 12 Passed

Implementation

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

This skill is almost entirely boilerplate and abstract description with zero actionable content. It explains what time series forecasting is and what the skill 'will do' without providing any executable code, specific library usage, concrete commands, or real implementation guidance. It reads like a marketing description rather than a technical skill file.

Suggestions

Replace the abstract descriptions with executable Python code examples showing actual ARIMA and Prophet implementations (e.g., `from statsmodels.tsa.arima.model import ARIMA; model = ARIMA(data, order=(1,1,1)); results = model.fit()`)

Remove all boilerplate sections (Overview, Integration, Prerequisites, Instructions, Output, Error Handling, Resources) that contain only generic placeholder text and provide no skill-specific information

Add a concrete workflow with validation steps: load data → check stationarity (ADF test) → fit model → evaluate with MAE/RMSE → generate forecast with confidence intervals, including actual code for each step

Include a concrete input/output example showing actual data format (e.g., CSV with dates and values) and the expected forecast output format

Dimension	Reasoning	Score
Conciseness	Extremely verbose with extensive explanation of concepts Claude already knows. The 'Overview', 'How It Works', 'When to Use', 'Integration', 'Prerequisites', 'Instructions', 'Output', 'Error Handling', and 'Resources' sections are all filler that provide no actionable information. The skill explains what time series forecasting is rather than how to do it.	1 / 3
Actionability	No executable code, no concrete commands, no specific library usage examples, no actual implementation guidance. The examples describe what 'the skill will' do in abstract terms rather than providing copy-paste ready code for ARIMA or Prophet. The 'Instructions' section is entirely generic boilerplate ('Invoke this skill when trigger conditions are met').	1 / 3
Workflow Clarity	The workflow steps are abstract descriptions ('analyze the data', 'select a model', 'generate a forecast') with no concrete commands, validation checkpoints, or error recovery loops. There is no guidance on how to actually perform any of these steps, and no validation between steps.	1 / 3
Progressive Disclosure	Monolithic wall of text with no references to external files, no bundle files to support it, and sections like 'Resources' that list 'Project documentation' and 'Related skills and commands' without any actual links. Content is poorly organized with many boilerplate sections that add no value.	1 / 3
	Total	4 / 12 Passed

Validation

81%

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 — 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

Repository: jeremylongshore/claude-code-plugins-plus-skills
Commit: 3a2d27d

Reviewed: 2 days ago

Table of Contents

Discovery Implementation Validation

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