forecast-generator
Forecast Generator - Auto-activating skill for Data Analytics. Triggers on: forecast generator, forecast generator Part of the Data Analytics skill category.
33
0%
Does it follow best practices?
Impact
95%
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/forecast-generator/SKILL.mdQuality
Discovery
0%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 with no substantive content. It fails to describe what the skill does, provides no natural trigger terms beyond a duplicated skill name, and lacks any 'Use when...' guidance. It would be nearly useless for Claude to distinguish this skill from others in a multi-skill environment.
Suggestions
Add concrete actions the skill performs, e.g., 'Generates time-series forecasts, builds predictive models, and produces trend projections from historical data.'
Include an explicit 'Use when...' clause with natural trigger terms, e.g., 'Use when the user asks to predict future values, create sales forecasts, project trends, or perform time-series analysis.'
Remove the duplicated trigger term and expand with varied natural keywords users would say, such as 'forecast', 'predict', 'projection', 'trend', 'future estimate', 'demand planning'.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | The description only names the skill ('Forecast Generator') and its category ('Data Analytics') but lists no concrete actions like generating time-series forecasts, building predictive models, or producing trend analyses. It is entirely vague about what the skill actually does. | 1 / 3 |
Completeness | The 'what' is essentially absent beyond the name, and the 'when' is limited to a redundant trigger phrase with no explicit 'Use when...' clause or meaningful guidance on activation scenarios. | 1 / 3 |
Trigger Term Quality | The only trigger terms listed are 'forecast generator' repeated twice. There are no natural user phrases like 'predict sales', 'time series', 'projection', 'trend analysis', or 'forecasting' that a user would naturally say. | 1 / 3 |
Distinctiveness Conflict Risk | The description is so generic ('Data Analytics' category, 'forecast generator' label) that it could easily conflict with any analytics, prediction, or data processing skill. There is nothing that carves out a clear niche. | 1 / 3 |
Total | 4 / 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. It contains only generic boilerplate descriptions that repeat the phrase 'forecast generator' without providing any actual instructions, code, methods, or domain knowledge about forecasting, time series analysis, or data analytics. It fails on every dimension of the rubric.
Suggestions
Add concrete, executable code examples for common forecasting tasks (e.g., time series forecasting with pandas/statsmodels/Prophet, including data preparation, model fitting, and prediction).
Define a clear multi-step workflow for generating forecasts: data loading → exploratory analysis → model selection → fitting → validation → output generation, with explicit validation checkpoints.
Remove all boilerplate sections (Purpose, When to Use, Capabilities, Example Triggers) and replace with actionable content that teaches specific forecasting techniques, parameters, and output formats.
Add references to supplementary files for advanced topics (e.g., ARIMA tuning, seasonal decomposition, ensemble methods) to enable progressive disclosure.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is entirely filler and boilerplate. It explains nothing Claude doesn't already know and provides zero domain-specific information about forecast generation. Every section restates the same vague concept without adding value. | 1 / 3 |
Actionability | There is no concrete guidance whatsoever—no code, no commands, no specific methods, no examples of forecast generation techniques, no libraries mentioned, no data formats discussed. It only describes what the skill claims to do without actually doing it. | 1 / 3 |
Workflow Clarity | No workflow is defined. There are no steps, no sequence, no validation checkpoints. The phrase 'step-by-step guidance' is promised but never delivered. | 1 / 3 |
Progressive Disclosure | The content is a flat, monolithic block of placeholder text with no references to detailed materials, no links to examples or advanced guides, and no meaningful structural organization beyond generic headings. | 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
4dee593
- Reviewed
Table of Contents
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