SKILLEMALL.ai

AB new-visitor-cold-start

Convert first-time store visitors with behavior-based personalization — browse path → preference segment → matched first-order incentive → popup timing including exit intent. Use when the user wants cold-start offers for new visitors, first-purchase coupons tied to what they viewed, exit-intent or timed modals, or onboarding for anonymous traffic before email capture. Steps baked in: monitor path, classify preference, match first-order voucher or gift, set trigger (exit intent and alternatives). Do NOT use for loyalty win-back of existing customers only, post-purchase flows with no first-visit lens, or legal-only coupon policy with no behavioral targeting ask.

LeoYeAI/openclaw-master-skills Agent Skills author: LeoYeAI MIT 10 files body ≈ 583 tokens Open the sourcegithub.com analyzed 3 d ago

Convert first-time store visitors with behavior-based personalization — browse path → preference segment → matched first-order incentive → popup timing…

As a process B 66/100 · Nearly there — weak spots: result and completion, inputs and preconditions, running it twice

GeneratorCommercetype and topics are labelled automatically from the skill text
JSON
Technical rating
A
91/100
safety, quality, tests
Safety 60%
100
Quality 40%
78
Run on models
none yet
Process rating
B
66/100
Nearly there
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

How to improve

    For the model run — optional
    • 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: 7. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • warning frontmatter-yaml SKILL.md: the frontmatter is not valid YAML (YAML parse error: Nested mappings are not allowed in compact mappings at line 2, column 14: description: Convert first-time store visitors with behavior-based personalizat… ^ ); fields were read line by line. The usual cause is a colon inside an unquoted value

    Process rating: all ten parameters 66/100

    • 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. 2 mutating operations with no state check
    • 60Failures and branches. 2 branches
    • 100Tools and files. No external tools needed
    • 100Steps. 11 steps
    • 100When it triggers. States when to use and when not to
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 583 tokens
    • medium 3 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
    • +3Output format is not stated: the model decides each time
    • +4No input/output examples
    • +1No license
    • +2Single-language instructions
    • +4Description says when NOT to use the skill
    • +3Description length 668: enough signal without eating the budget
    • +4Structure: 10 headings
    • +3Step-by-step instructions: 11 items
    • +4Reference files are cited in the instructions (1 of 2)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 78.