AC commercial-image-prompt
Commercial and e-commerce image generation methodology — three-layer prompt engineering framework (subject-scene / style-texture / technical-constraints) covering both global cross-border platforms (Amazon, Shopify, SHEIN, TikTok, Instagram) and domestic Asian markets (Taobao, JD, Xiaohongshu, WeChat). Includes multi-platform templates and iterative optimization strategies. Use when: generating product photos, marketing posters, e-commerce covers, or commercial AI visuals. Keywords: e-commerce image, product photography, commercial prompts, SHEIN, Amazon, Shopify, 淘宝主图, 营销海报, 小红书封面, 生图提示词.
Commercial and e-commerce image generation methodology — three-layer prompt engineering framework (subject-scene / style-texture / technical-constraints)…
As a process C 58/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions
How to improve
- 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
✓ No remarks against the Agent Skills spec
Process rating: all ten parameters 58/100
- 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
- 20When it triggers. No condition that starts the skill
- 50Failures and branches. 0 branches, has a failure section
- 100Tools and files. No external tools needed
- 100Steps. 20 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2081 tokens
- 100Running it twice. No mutating operations
- medium 2 test cases, all positive: not one "should refuse" or "should ask first"
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)
- +3Output format is not stated: the model decides each time
- +2Single-language instructions
- +3Description length 596: enough signal without eating the budget
- +4Structure: 20 headings
- +3Step-by-step instructions: 20 items
- +4Has examples (7 code blocks)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 85.