AB ecommerce-growth-strategy
E-commerce growth strategy advisor. Diagnoses current business health using unit economics (CAC, LTV, AOV, contribution margin), identifies the highest-impact growth opportunities across 5 levers (traffic, conversion, AOV, retention, expansion), and builds a prioritized 90-day growth roadmap. Uses the Ansoff Matrix adapted for e-commerce to evaluate market penetration, channel expansion, product expansion, and new market entry. Includes multichannel readiness assessment (Amazon, Walmart, TikTok Shop, Etsy, DTC/Shopify/Shopify) and product line expansion analysis. No API key required. Use when: (1) planning next phase of e-commerce growth, (2) deciding whether to expand to new channels or products, (3) diagnosing why growth has stalled, (4) prioritizing what to fix or build next.
As a process B 67/100 · Nearly there — weak spots: when it triggers, inputs and preconditions, progress reporting
How to improve
- The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
- Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
- A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.
Guard findings · 0
✓ No critical or high findings
Files scanned: 2. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
body-longSKILL.md body ≈ 5282 tokens (recommended < 5000); move details to references/
Process rating: all ten parameters 67/100
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Progress reporting. Says nothing while it works
- 20When it triggers. No condition that starts the skill
- 60Result and completion. Output format stated, no completion criterion
- 70Failures and branches. 5 branches
- 70Execution cost. Instruction body is 5282 tokens
- 100Tools and files. No external tools needed
- 100Steps. 139 steps
- 100Consistency. Name and required fields are in place
- 100Running it twice. No mutating operations
- low 11 top-level sections: this looks like several domains in one skill
Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.
Quality signals
- +5Description has no quoted example phrases that should trigger the skill
- +4Description does not say when NOT to use the skill (false activations)
- -214 emoji in the instructions: noise for the model
- +1No license
- +2Single-language instructions
- +3Description length 789: enough signal without eating the budget
- +4Structure: 32 headings
- +3Step-by-step instructions: 139 items
- +3Output format is stated explicitly
- +4Has examples (12 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 75.