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.
30
25%
Does it follow best practices?
Impact
—
No eval scenarios have been run
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.mdQuality
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 description also uses second/third person inconsistently and fails to provide explicit, actionable trigger terms that would help Claude select this skill appropriately.
Suggestions
Replace the generic boilerplate 'Use when appropriate context detected. Trigger with relevant phrases based on skill purpose.' with specific trigger guidance, e.g., 'Use when the user asks to forecast, predict, or project future values from historical data, mentions time series, or references terms like trend analysis, seasonality, or ARIMA.'
Complete the truncated sentence and list concrete actions such as 'Fits ARIMA/exponential smoothing models, detects seasonal patterns, generates forecast intervals, and visualizes predicted vs. actual values.'
Remove first/second person references and ensure consistent third-person voice throughout, e.g., 'Forecasts future values based on historical time series data' instead of 'this skill enables AI assistant to forecast'.
| 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 generic boilerplate ('Use when appropriate context detected') and the description appears truncated mid-sentence, so explicit trigger guidance is effectively missing. | 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 its distinctiveness. It could overlap with general data analysis or statistics skills. | 2 / 3 |
Total | 8 / 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 almost entirely boilerplate and abstract description with no actionable content. It contains no executable code, no specific library imports or configurations, no concrete workflows, and no validation steps. It reads like a marketing description of a feature rather than an instruction set Claude could use to actually perform time series forecasting.
Suggestions
Replace abstract descriptions with executable Python code examples showing actual ARIMA/Prophet usage (e.g., `from statsmodels.tsa.arima.model import ARIMA; model = ARIMA(data, order=(1,1,1)); results = model.fit()`)
Add a concrete workflow with validation checkpoints: data loading → stationarity check (ADF test) → model fitting → residual diagnostics → forecast generation with specific commands at each step
Remove all generic boilerplate sections (Overview, Integration, Prerequisites, Instructions, Output, Error Handling, Resources) that provide no skill-specific information
Include at least one complete end-to-end example with actual input data format, code, and expected output format
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Extremely verbose with extensive explanation of concepts Claude already knows. The 'Overview', 'How It Works', 'When to Use', and 'Integration' sections are padded filler. Generic boilerplate sections like 'Error Handling', 'Output', 'Resources', and 'Instructions' add zero actionable information. Nearly every token is wasted. | 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 showing how to do it. Instructions like 'Invoke this skill when the trigger conditions are met' are completely vacuous. | 1 / 3 |
Workflow Clarity | The numbered steps are abstract descriptions ('analyze', 'select', 'generate') with no concrete commands, validation checkpoints, or error recovery loops. There is no actual workflow Claude could follow to perform time series forecasting—just vague descriptions of what should happen. | 1 / 3 |
Progressive Disclosure | Monolithic wall of text with no references to external files and no bundle files to support it. Content is poorly organized with multiple redundant sections (Overview, How It Works, Examples all repeat the same abstract information). No clear navigation or layered structure. | 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
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- Reviewed
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