AC Map Customer Micromoments Across Web Analytics & CRM Platforms
Analyze customer journey touchpoints to identify micro-moments of intent, frustration, and buying readiness. Use when the user needs audience behavior maps, emotional trigger extraction, or optimal intervention windows for targeted nurture sequences.
Analyze customer journey touchpoints to identify micro-moments of intent, frustration, and buying readiness.
As a process C 52/100 · Has gaps — weak spots: result and completion, inputs and preconditions, failures and branches
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 · 1
✓ No critical or high findings
Medium and low: 1
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low Exfiltration
exfil-webhook-urlSKILL.md:150Webhook / callback URL commonly used for exfiltration (verify the destination) (placeholder value)export SLACK_WEBHOOK_URL="https://hooks.slack.com/..."
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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 52/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Failures and branches. Linear process with no failure handling
- 0Progress reporting. Says nothing while it works
- 30Running it twice. 4 mutating operations with no state check
- 40Consistency. Frontmatter name (Map Customer Micromoments Across Web Analytics & CRM Platforms) differs from the folder (audience-micromoment-mapper)
- 70When it triggers. States when to use, but not when not to
- 100Tools and files. No external tools needed
- 100Steps. 49 steps
- 100Execution cost. Instruction body is 3678 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 250: enough signal without eating the budget
- +4Structure: 30 headings
- +3Step-by-step instructions: 49 items
- +4Has examples (12 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 78.