Conciseness

The body is mostly lean, but steps 1-5 and 6-11 of the Instructions duplicate references/implementation.md's Steps 1-2 almost verbatim, and prerequisites include items Claude already assumes ('Understanding of problem type', 'Knowledge of evaluation metrics'). This fits the 3 anchor — mostly efficient with some unnecessary content that could be tightened.

Actionability

The instructions are high-level directives with no code, commands, or library-specific usage anywhere in the body ('Perform initial data quality assessment', 'Initialize AutoML pipeline with configuration'), and the referenced implementation guide likewise contains only abstract lists. This matches the 2 anchor: minimal concrete guidance with the specific executable steps missing.

Workflow Clarity

Eleven numbered steps give a recognizable sequence from problem definition through pipeline initialization, but there are no validation checkpoints or feedback loops (e.g., verifying data quality or inspecting trained artifacts before proceeding). This matches the 3 anchor — sequence present but checkpoints missing or implicit.

Progressive Disclosure

The body is a clear overview with well-signaled, one-level-deep markdown links to real files (references/implementation.md, errors.md, examples.md) that contain actual content with no nested references. It falls short of 5 because the instructions duplicate reference content and the bundle's scripts/ and assets/ files are never linked or mentioned from the body.

Specificity

The description names the domain and three concrete capabilities — 'feature engineering, model selection, and hyperparameter tuning' — which is several specific actions, but coverage has minor gaps (e.g., data validation and evaluation are only implied via the 'Use when' clause). It exceeds the 3 anchor (only 1-2 actions) but falls short of 5's comprehensive coverage.

Completeness

It clearly answers both what ('Build automated machine learning pipelines with feature engineering, model selection, and hyperparameter tuning') and when ('Use when automating ML workflows from data preparation through model deployment'), and adds explicit concrete trigger phrases — matching the 5 anchor exactly.

Trigger Term Quality

Includes natural phrases users would say — 'build automl pipeline', 'automate ml workflow', 'create automated training pipeline' — giving good keyword coverage. It misses common variations such as 'auto machine learning', 'model tuning', or 'automated model training', so it does not reach the 5 anchor's comprehensive synonym coverage.

Distinctiveness Conflict Risk

The AutoML pipeline niche is mostly distinct with dedicated trigger phrases, but 'automate ml workflow' is broad enough to overlap with general ML-training or data-science skills, fitting the 4 anchor (minor overlap risk with closely related skills) rather than 5's minimal conflict risk.