feature-importance-analyzer
Feature Importance Analyzer - Auto-activating skill for ML Training. Triggers on: feature importance analyzer, feature importance analyzer Part of the ML Training skill category.
34
3%
Does it follow best practices?
Impact
90%
1.11xAverage score across 3 eval scenarios
Passed
No known issues
Optimize this skill with Tessl
npx tessl skill review --optimize ./planned-skills/generated/07-ml-training/feature-importance-analyzer/SKILL.mdQuality
Discovery
7%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 extremely weak—it reads more like a label than a functional description. It provides no concrete actions, no meaningful trigger terms beyond a duplicated phrase, and no explicit guidance on when Claude should select this skill. It would be nearly indistinguishable from other ML skills in a large skill library.
Suggestions
Add specific concrete actions the skill performs, e.g., 'Computes and visualizes feature importance using SHAP values, permutation importance, and tree-based importance scores for trained ML models.'
Add an explicit 'Use when...' clause with natural trigger terms, e.g., 'Use when the user asks about which features matter, feature ranking, variable importance, SHAP analysis, or understanding model predictions.'
Include diverse natural-language trigger terms users might say, such as 'feature selection', 'important features', 'SHAP values', 'permutation importance', 'model interpretability', 'feature ranking'.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | The description names a domain ('Feature Importance Analyzer', 'ML Training') but provides no concrete actions. There is no indication of what the skill actually does—no verbs describing specific capabilities like 'ranks features', 'computes SHAP values', 'generates importance plots', etc. | 1 / 3 |
Completeness | The description fails to answer both 'what does this do' and 'when should Claude use it'. There is no explanation of capabilities and no explicit 'Use when...' clause with meaningful trigger guidance. | 1 / 3 |
Trigger Term Quality | The trigger terms listed are just 'feature importance analyzer' repeated twice. There are no natural user-language variations such as 'feature selection', 'variable importance', 'SHAP', 'permutation importance', 'which features matter', etc. | 1 / 3 |
Distinctiveness Conflict Risk | The term 'Feature Importance Analyzer' is somewhat specific to a niche within ML, which provides some distinctiveness. However, the vague 'ML Training' category and lack of concrete scope could cause overlap with other ML-related skills. | 2 / 3 |
Total | 5 / 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 actual instructional content. It repeatedly references 'feature importance analyzer' without ever defining what it means, how to do it, or providing any executable code or concrete guidance. It fails on every dimension of the rubric.
Suggestions
Add concrete, executable code examples for feature importance techniques (e.g., SHAP values, permutation importance with sklearn, tree-based feature importances) with specific imports and function calls.
Define a clear workflow: e.g., 1) Train model, 2) Compute feature importances using method X, 3) Validate results, 4) Visualize/report—with actual code at each step.
Remove all meta-description sections (Purpose, When to Use, Capabilities, Example Triggers) that describe the skill rather than teaching the task. Replace with actionable content.
Add specific examples showing input data format, expected output (e.g., ranked feature list, importance scores), and interpretation guidance.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is entirely filler and meta-description. It explains what the skill does in abstract terms without providing any actual technical content. Every section restates the same vague idea ('feature importance analyzer') without adding substance. | 1 / 3 |
Actionability | There is zero concrete guidance—no code, no commands, no specific techniques, no examples of feature importance methods (e.g., SHAP, permutation importance, tree-based importance). It only describes rather than instructs. | 1 / 3 |
Workflow Clarity | No workflow, steps, or process is defined. The skill claims to provide 'step-by-step guidance' but contains none. There are no validation checkpoints or sequenced instructions. | 1 / 3 |
Progressive Disclosure | The content is a flat, repetitive document with no references to detailed materials, no links to examples or advanced guides, and no meaningful structural organization beyond boilerplate 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
3076d78
- Reviewed
Table of Contents
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