CtrlK
BlogDocsLog inGet started
Tessl Logo

svm-model-importance-analysis

Use when you need a standardized R CLI workflow to run two-class SVM-RFE feature ranking on an expression-like matrix, choose an informative feature count from cross-validated error, and generate reproducible ranking and error plots. NOT for regression, multi-class classification, missing-value imputation, or remote data fetching.

66

Quality

83%

Does it follow best practices?

Run evals on this skill

Adds up to 20 points to the overall score

View guide
SecuritybySnyk

Passed

No findings from the security scan

SKILL.md
Quality
Evals
Security

Quality

Content

67%Weight 40%Scale 1-5

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

A well-structured CLI skill body with strong navigation, executable usage examples, thorough input/output/error documentation, and built-in validation. Its main weaknesses are the inlined plot-styling argument table that inflates the overview at the expense of the dedicated CLI reference, and Testing-section commands that reference a tests/ directory that does not exist in the bundle.

Suggestions

Move the ~25 plot-styling arguments (--svm_error_*, --svm_rank_* cosmetic options such as colors, labels, cex, pos) into references/cli-guide.md, keeping only the core analysis arguments (input, group, seed, svm_k, select rule, etc.) inline in SKILL.md.

Fix or remove the references to tests/run_tests.R and tests/data/ — these files are absent from the bundle, so the 'When to Read External Files' row and the Testing section commands fail as written; either bundle the test dataset and runner or drop those commands.

Add one or two sentences of interpretation guidance (e.g., how to read the error curve when choosing between --svm_select_rule min and tolerance) or link to references/algorithm.md from the Arguments section, so users know when to deviate from the defaults.

DimensionReasoningScore

Conciseness

The body is mostly dense, useful reference material (commands, tables, format examples) with no padding or explanations of known concepts, but roughly a third of it is a ~25-row table of plot-styling arguments ('--svm_error_label_cex', '--svm_error_label_pos', '--svm_rank_color', etc.) that belongs in the already-existing references/cli-guide.md rather than the overview. This matches anchor 3 — mostly efficient but could be meaningfully tightened — rather than anchor 4's 'minor instances'.

3 / 5

Actionability

Guidance is largely copy-paste executable: a complete Rscript command with all flags, typed argument table with defaults and required flags, concrete input-format examples with sample CSV rows, an output-file table, and a --plot_only reuse mode. The gap keeping it from anchor 5 is the Testing section: 'Rscript tests/run_tests.R' and the direct test command reference tests/run_tests.R and tests/data/expression_matrix.csv, which do not exist in the bundle, so those commands fail as written.

4 / 5

Workflow Clarity

The single CLI action is unambiguous, with a complete usage command, documented exit codes, a standardized error-code table mapping failures to causes, and verification steps (--help check, test run). This sits at anchor 4 rather than 5 because the verification commands themselves point at non-existent test files, leaving a minor checkpoint that cannot actually be executed.

4 / 5

Progressive Disclosure

Excellent navigation via the 'When to Read External Files' situation→file table, and the referenced files references/algorithm.md, references/troubleshooting.md, references/cli-guide.md, and scripts/main.R all exist one level deep. However, the table also points to 'tests/data/' and the Testing section to 'tests/run_tests.R', which are absent from the bundle (dangling references), and the bulk styling-argument table duplicates material that should live in cli-guide.md — minor organization gaps consistent with anchor 4.

4 / 5

Total

15

/

20

Passed

Description

92%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

A strong description that explicitly answers both what the skill does and when to use it, with concrete actions and explicit negative boundaries that minimize mis-triggering. The only weakness is slightly technical phrasing (e.g., no 'feature selection' synonym) that leaves a small gap in natural trigger terms.

DimensionReasoningScore

Specificity

The description lists three concrete, comprehensive actions covering the full workflow: 'run two-class SVM-RFE feature ranking on an expression-like matrix', 'choose an informative feature count from cross-validated error', and 'generate reproducible ranking and error plots'. This matches the anchor for multiple specific concrete actions with comprehensive coverage, exceeding the anchor-4 example which has minor gaps in coverage.

5 / 5

Completeness

Both 'what' (SVM-RFE ranking, feature-count selection from cross-validated error, plot generation) and 'when' ('Use when you need a standardized R CLI workflow to...') are explicitly stated with concrete trigger phrases, and negative boundaries ('NOT for regression, multi-class classification...') further sharpen the when. This matches the anchor-5 exemplar structure exactly.

5 / 5

Trigger Term Quality

Good natural keywords are present: 'SVM-RFE feature ranking', 'expression-like matrix', 'cross-validated error', 'ranking and error plots', 'R CLI'. However, common user phrasings like 'feature selection', 'SVM', or 'rank features' are missing, so a few natural terms users would actually say are absent — matching the anchor-4 example ('PDF files, forms, document extraction') rather than the synonym-and-extension coverage of anchor 5.

4 / 5

Distinctiveness Conflict Risk

A clear niche is staked out — two-class SVM-RFE on an expression matrix via an R CLI — with explicit exclusions ('NOT for regression, multi-class classification, missing-value imputation, or remote data fetching') that eliminate overlap with adjacent skills. Minimal conflict risk, matching anchor 5.

5 / 5

Total

19

/

20

Passed

Validation

93%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

Total

15

/

16

Passed

Repository
aipoch/medical-research-skills
Reviewed

Table of Contents

Is this your skill?

If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.