running-clustering-algorithms
Analyze datasets by running clustering algorithms (K-means, DBSCAN, hierarchical) to identify data groups. Use when requesting "run clustering", "cluster analysis", or "group data points". Trigger with relevant phrases based on skill purpose.
41
42%
Does it follow best practices?
Impact
—
No eval scenarios have been run
Passed
No known issues
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npx tessl skill review --optimize ./plugins/ai-ml/clustering-algorithm-runner/skills/running-clustering-algorithms/SKILL.mdQuality
Discovery
85%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 is solid with specific algorithms listed, a clear 'Use when' clause, and good distinctiveness. However, the final sentence 'Trigger with relevant phrases based on skill purpose' is meaningless filler that adds no value, and the trigger term coverage could be broader to include natural variations users might say.
Suggestions
Remove the vague filler sentence 'Trigger with relevant phrases based on skill purpose' as it provides no actionable information for skill selection.
Expand trigger terms to include natural variations like 'segmentation', 'unsupervised learning', 'find groups', 'k-means', 'DBSCAN', or 'dendogram'.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions and algorithms: 'running clustering algorithms (K-means, DBSCAN, hierarchical) to identify data groups.' This names the domain (datasets), the technique (clustering), and specific algorithm variants. | 3 / 3 |
Completeness | Clearly answers both 'what' (analyze datasets by running clustering algorithms to identify data groups) and 'when' (explicit 'Use when' clause with trigger phrases like 'run clustering', 'cluster analysis', 'group data points'). | 3 / 3 |
Trigger Term Quality | Includes some good trigger terms like 'run clustering', 'cluster analysis', 'group data points', but the final sentence 'Trigger with relevant phrases based on skill purpose' is vague filler. Missing natural variations like 'segmentation', 'unsupervised learning', 'find patterns', or 'k-means'. | 2 / 3 |
Distinctiveness Conflict Risk | Clustering is a well-defined niche within data analysis. The specific algorithm names (K-means, DBSCAN, hierarchical) and trigger terms like 'cluster analysis' make it clearly distinguishable from other data analysis or machine learning skills. | 3 / 3 |
Total | 11 / 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 with no actionable content. It describes what clustering is and what the skill would theoretically do, but provides zero executable code, no concrete parameter guidance, no validation steps, and no real examples. It reads like a template that was never filled in with actual implementation details.
Suggestions
Replace the abstract 'How It Works' section with concrete, executable Python code examples for each algorithm (K-means, DBSCAN, hierarchical) using scikit-learn, including data loading, preprocessing, fitting, and evaluation.
Add specific validation steps such as computing silhouette scores, elbow method for K-means k selection, and concrete code for generating cluster visualizations.
Remove all generic boilerplate sections (Overview, Integration, Prerequisites, Instructions, Output, Error Handling, Resources) that provide no clustering-specific guidance and waste tokens.
Include concrete parameter tuning guidance with actual code, e.g., how to iterate over epsilon values for DBSCAN or use the elbow method for K-means, rather than just mentioning these concepts abstractly.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Extremely verbose with extensive explanation of concepts Claude already knows (what clustering is, how algorithms work, what preprocessing means). The 'Overview', 'How It Works', 'Integration', 'Prerequisites', 'Instructions', 'Output', 'Error Handling', and 'Resources' sections are all filler that add no actionable value. Most of the content describes rather than instructs. | 1 / 3 |
Actionability | No executable code, no concrete commands, no specific examples with actual code. The examples describe what the skill 'will do' in abstract terms rather than providing copy-paste ready Python code for clustering. The 'Instructions' section is entirely generic ('Invoke this skill when the trigger conditions are met'). | 1 / 3 |
Workflow Clarity | The 'How It Works' section lists abstract steps (analyzing context, generating code, executing, providing results) but provides no concrete sequence, no validation checkpoints, no error recovery loops. There's no guidance on how to validate clustering results or handle common failure modes like convergence issues or inappropriate parameter choices. | 1 / 3 |
Progressive Disclosure | Monolithic wall of text with no references to external files and no meaningful content organization. Sections like 'Resources' point to vague 'Project documentation' and 'Related skills' without any actual links. The content that exists is all surface-level with nothing substantive to disclose progressively. | 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 | |
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