AC Generate Micro-Moment Copy with AI — Instant Marketing Content
Generate hyper-contextual microcopy (CTAs, subject lines, push notifications, form labels, error messages) optimized for awareness, consideration, decision, and retention micromoments. Use when the user needs conversion-focused copy variants, A/B testing sets, or behavioral psychology-driven messaging for any platform.
Generate hyper-contextual microcopy (CTAs, subject lines, push notifications, form labels, error messages) optimized for awareness, consideration, decision…
As a process C 57/100 · Has gaps — weak spots: result and completion, inputs and preconditions, consistency
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
- warning
name-formatname should be kebab-case (lowercase letters, digits, hyphens) - note
frontmatter-keyunknown frontmatter key "homepage"
Process rating: all ten parameters 57/100
- 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
- 30Running it twice. 8 mutating operations with no state check
- 40Consistency. Frontmatter name (Generate Micro-Moment Copy with AI — Instant Marketing Content) differs from the folder (micro-moment-copywriter)
- 50Failures and branches. 0 branches, has a failure section
- 70When it triggers. States when to use, but not when not to
- 100Tools and files. No external tools needed
- 100Steps. 57 steps
- 100Execution cost. Instruction body is 3668 tokens
- 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
- +1No license
- +2Single-language instructions
- +3Description length 320: enough signal without eating the budget
- +4Structure: 31 headings
- +3Step-by-step instructions: 57 items
- +4Has examples (12 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 78.