AB afrexai-voc-engine
Complete Voice of Customer system — collect, analyze, and operationalize customer feedback at scale. Covers NPS/CSAT/CES measurement, customer interview methodology, feedback taxonomy, feature request prioritization, sentiment analysis, closed-loop workflows, and VoC-driven product decisions. Use when building feedback systems, running customer interviews, measuring satisfaction, analyzing feature requests, reducing churn, or closing the feedback loop. Trigger on "customer feedback", "voice of customer", "NPS", "CSAT", "CES", "feature requests", "feedback system", "customer interviews", "satisfaction survey", "churn analysis".
As a process B 69/100 · Nearly there — weak spots: inputs and preconditions, running it twice, progress reporting
How to improve
- The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
- 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
body-longSKILL.md body ≈ 6736 tokens (recommended < 5000); move details to references/
Process rating: all ten parameters 69/100
- 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
- 55Failures and branches. 1 branches
- 60Result and completion. Output format stated, no completion criterion
- 70When it triggers. States when to use, but not when not to
- 70Execution cost. Instruction body is 6736 tokens
- 100Tools and files. No external tools needed
- 100Steps. 104 steps
- 100Consistency. Name and required fields are in place
- low 13 top-level sections: this looks like several domains in one skill
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)
- +1No license
- +2Single-language instructions
- +5Description quotes 7 example trigger phrases
- +3Description length 634: enough signal without eating the budget
- +4Structure: 53 headings
- +3Step-by-step instructions: 104 items
- +3Output format is stated explicitly
- +4Has examples (13 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 82.