Content
63%Weight 40%Scale 1-5Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
A well-structured, domain-appropriate set of ML analysis playbooks with concrete, technique-specific steps and logical sequencing including some conditional feedback. Its main weaknesses are token inefficiency from repeated boilerplate steps across sections and the absence of any progressive disclosure — all seven plans sit inline in one file with no references to offload the unused ones.
Suggestions
Factor the ~8 preprocessing bullets repeated across Clustering, Classification, Regression, and No match into a single shared 'Universal preprocessing' section (or the existing 'Essential ML Practices') and reference it from each task plan.
Move each per-task plan (clustering, classification, regression, time series, model comparison) into references/<task>.md and keep SKILL.md as a short overview with one-level-deep pointers, so only the matched plan loads into context.
Make evaluation steps concrete: name the error metrics per task (e.g., RMSE/MAE for regression, F1/PR-AUC for classification) and specify candidate model families for 'Train two time series forecasting models'.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is mostly lean checklist guidance, but the same preprocessing bullets ("Understand the schema and field descriptions", "Transform ordinal data with an ordinal encoder", "Standardize numerical features", "Prefer to keep data instead of dropping it when possible") are repeated nearly verbatim across the Clustering, Classification, Regression, and No match sections — noticeably more than minor trimming. No concept over-explanation, but the duplication inflates token cost. | 3 / 5 |
Actionability | As an instruction-only skill the guidance is concrete and executable, naming specific techniques ("collect the silhouette score", "chronological split", "$k$-fold cross-validation", "bootstrapping to calculate 95% confidence intervals", "SHAP or LIME"). Minor gaps remain: steps like "Evaluate the models", "Select a model", and "Train two time series forecasting models" name no metrics or model families, so they read as high-level hints in places. | 4 / 5 |
Workflow Clarity | Each task section is a clearly ordered pipeline from schema understanding through preprocessing, training, evaluation, to a "Conclusion: comprehensively answer the prompt" cell, and conditional checkpoints exist ("If there is evidence of overfitting, regularize and retrain the model", silhouette-based cluster selection). Not a 5 because there are no explicit verify-then-proceed gates (e.g., confirm stationarity test results before choosing model hyperparameters). | 4 / 5 |
Progressive Disclosure | The single SKILL.md inlines ~190 lines containing seven mutually exclusive task plans, only one of which is used per invocation; content that clearly belongs in per-task reference files (e.g., references/clustering.md) is inlined, so unused plans consume context. Sections are well-headered, but no references/ files exist and nothing is split out. | 3 / 5 |
Total | 14 / 20 Passed |