Conciseness

The body never explains concepts Claude already knows (no primer on XGBoost, SHAP, churn, or Treasure Data) and is dense with directives, but has minor trimmable spots: "Read each referenced file once per conversation: when this skill is invoked again later in the same conversation..." is a long compound sentence, and the Quick Reference repeats Phase 3 defaults ("Default workflow project name: classifier_ais"). Not 5: a few tokens are duplicated or over-conditioned; not 3: there is no unnecessary explanation, only tightening opportunities.

Actionability

Guidance is copy-paste executable: exact commands ("tdx tables \"<database>.*\"", "tdx ps desc \"<name>\" -o agent_outputs/<project_slug>/ps_schema.json", the information_schema.columns query), exact config keys ("sink_database", "model_name", "target_column", "user_id_column", "columns_to_drop", "label_window_days"), exact file paths, and explicit naming rules with placeholders. Not 4: no material gaps — placeholders are clearly marked and the common cases are covered.

Workflow Clarity

Five phases are sequenced in a summary table plus numbered per-phase steps with explicit validation and feedback loops: "Validate with references/workflow-setup.md §4", "If data_prep fails, fix the SQL and re-run; if check 2 suggests leakage (AUC above ~98, one dominant feature), remove the feature and re-run", and approval checkpoints ("wait for approval", "read it only after the user approves the plan", "Only after the user says the model is ready"). Not 4: validation steps, error-recovery loops, and checklists are all explicit, matching the level 5 anchor including for the batch/database operations involved.

Progressive Disclosure

All six bundle files in references/ are cited one level deep with clear when-to-read signals: "read references/ai-signals-guide.md first... It is not needed for the standard setup steps (Phases 1–3) — references/workflow-setup.md covers those", plus section-anchored pointers ("§1", "§2", "Steps 1–3"). Verified against the actual bundle: every references/*.md path named in the body exists. Not 4: navigation, signaling, and content split are essentially ideal for an orchestration runbook.

Specificity

"builds the project's feature table, trains the AI Signals classifier (XGBoost or CatBoost with tuning, threshold selection and SHAP) and writes scores for Parent Segment attributes, then builds the project's classifier agent and dashboard" lists the complete end-to-end chain of concrete actions, matching the level 5 anchor of multiple specific actions with comprehensive coverage. Not 4: coverage spans data prep through training, scoring, activation, agent and dashboard with no gaps.

Completeness

It explicitly answers what ("Sets up a binary classification (propensity scoring) model on Treasure Data... trains... writes scores... then builds the project's classifier agent and dashboard") and when ("Use it to start, set up, configure, change, re-run or resume a propensity project... Trigger on: ...") with concrete trigger phrases — an exact match for the level 5 anchor. Not 4: the 'when' is fully explicit, not merely present.

Trigger Term Quality

The trigger list is comprehensive and natural: "predict which customers will churn", "score leads by likelihood to convert", "predict campaign response", "build a churn model", "build a lead scoring model", "configure the classifier", "classifier runbook", with synonyms across all the use cases named in the opening. Not 4: it goes well beyond 'a few natural terms missing' — these are phrases users would actually say.

Distinctiveness Conflict Risk

"binary classification (propensity scoring) model on Treasure Data" and "AI Signals classifier" carve out a clear niche, and the routing rule "Once the project's classifier agent is installed, questions about model performance, drivers and scores go to it" adds explicit boundary handling against the wrong skill answering. Not 4: the triggers are distinct and conflict risk is minimal.