BC static-ads
Run when the user wants to recreate a winning ad format with their own products and brand copy, or use the /static-ads command. Takes an uploaded ad format reference image, derives the layout structure and copy framework internally, generates on-brand copy variations, then renders static ads via GPT-image-2 using product images from the brand folder. Output saved under the active brand folder.
The skill takes a reference ad image, extracts layout and copy structure, generates brand-aligned text variations, then renders static ads via GPT-4-Vision using product images from the brand folder. Output saves to the same location.
Scores show no critical errors and full safety (100 points), but copy quality sits at 81 and process logic at 59. Translation: it works, but generated text tends toward predictability and variant selection lacks flexibility. In practice you'll get a batch of ads that need review and tweaking. Install if you're prepared to polish the output yourself.
Run when the user wants to recreate a winning ad format with their own products and brand copy, or use the /static-ads command.
As a process C 59/100 · Has gaps — weak spots: result and completion, inputs and preconditions
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: 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 59/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 55Failures and branches. 1 branches
- 60Tools and files. Uses tools (bash, web, python) that frontmatter does not declare
- 70Execution cost. Instruction body is 4708 tokens
- 85Steps. 34 steps, 3 vague phrases
- 100When it triggers. States when to use and when not to
- 100Consistency. Name and required fields are in place
- 100Running it twice. Mutating operations check current state
- 100Progress reporting. Reports progress
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
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
- +4No input/output examples
- +2Single-language instructions
- +3Description length 396: enough signal without eating the budget
- +4Structure: 5 headings
- +3Step-by-step instructions: 34 items
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 81.