loyalty-modeling

I noticed you're tackling the complexity of predictive loyalty modeling—that's a meaty problem where small implementation details can make the difference between useful insights and noise. Your score of 64 suggests the foundation is solid, but there might be some gaps in how the skill guides developers through the trickier parts of the system.

Links:

• GitHub
• Your agentic skill in the SkillzWave marketplace

The TL;DR

You're at 64/100, solidly in D territory. This is based on Anthropic's skill grading standards across five pillars. Your strongest area is Spec Compliance (11/15)—the YAML and naming conventions are locked down. The weakest is Utility (11/20)—the framework exists but lacks the concrete templates, examples, and validation steps that would make it actually actionable for someone building a loyalty model.

What's Working Well

• Valid YAML structure with required frontmatter fields in place
• Hyphen-case naming follows conventions properly
• Clear component breakdown (Inputs, Calculations, Outputs, Validation) provides decent conceptual scaffolding
• Modular philosophy around keeping calculators separate is sound thinking

The Big One: Missing Reference Files and Templates

The framework mentions three specific templates (driver sheet, sensitivity table, executive summary) but doesn't actually provide them. This is your biggest bang-for-buck fix. Right now, when someone tries to use this skill, they get a conceptual outline without the concrete tools to execute it.

Here's the fix: Create references/ directory with:

• driver-sheet-template.md – Shows the actual structure: Base Metrics tab (member count, transaction frequency, AOV, churn, CAC), tier structure columns, rewards cost breakdown
• `sensiti...