AC vibesku
CLI for VibeSKU — an AI-powered creative automation platform that turns product SKU photos into professional e-commerce visuals and marketplace-ready copy at scale. Use when the user wants to: (1) generate hero banners, exploded-view infographics, detail page poster sets, or listing copy from product photos via the command line, (2) authenticate with VibeSKU (browser login or API key), (3) browse or inspect generation templates (ecom-hero, kv-image-set, exploded-view, listing), (4) refine AI-generated outputs with edit instructions, (5) export/download image and text results, (6) run batch generation across a product catalog, (7) manage credits (check balance, purchase, redeem), (8) configure CLI settings. Triggers on mentions of "vibesku", "product visuals", "SKU photos", "ecommerce images", "hero banner", "listing copy", "product image generation", "batch generation", "VisionKV", "exploded view", "product infographic", "component breakdown", "technical diagram", or any VibeSKU CLI workflow.
As a process C 62/100 · Has gaps — weak spots: result and completion, inputs and preconditions, progress reporting
How to improve
- 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: 12. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
✓ No remarks against the Agent Skills spec
Process rating: all ten parameters 62/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Progress reporting. Says nothing while it works
- 60Tools and files. Uses tools (node) that frontmatter does not declare
- 70When it triggers. States when to use, but not when not to
- 100Steps. 13 steps
- 100Failures and branches. 2 branches, has a failure section
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1831 tokens
- 100Running it twice. No mutating operations
- low The response is described with custom markup (8 tags): a typed call is more reliable
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
- +4Description does not say when NOT to use the skill (false activations)
- +3Description length 1007: 120–800 characters recommended
- +3Output format is not stated: the model decides each time
- +1No license
- +2Single-language instructions
- +5Description quotes 13 example trigger phrases
- +4Structure: 12 headings
- +3Step-by-step instructions: 13 items
- +4Has examples (4 code blocks)
- +4Reference files are cited in the instructions (6 of 6)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 90.