SKILLEMALL.ai

AB small-goods-loyalty-incentives

Designs and refines customer loyalty programs and incentive systems for DTC/independent stores selling small, high-frequency products (e.g. cosmetics, phone cases, accessories, small jewelry, daily FMCG). Use whenever the user mentions loyalty program, points, tiers, rewards, member benefits, repeat purchase incentives, welcome bonus, post-purchase rewards, referral program, birthday offer, win-back offers, free-ship threshold, redemption rate, repeat rate, LTV, or wants to increase repeat purchases and customer retention for low-AOV high-repeat categories—even if they do not say "loyalty" or "incentives" explicitly. Output structured program design, incentive calendars, and measurable validation plans aligned with DTC best practices.

ClawHub Agent Skills author: RIJOY-AI v0.1.1 MIT-0 12 files body ≈ 2 871 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process B 75/100 · Nearly there — weak spots: inputs and preconditions, progress reporting

AnalyzerPersonal productivityData and analyticsWriting and documentstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
96/100
safety, quality, tests
Safety 60%
100
Quality 40%
99
Run on models
none yet
Process rating
B
75/100
Nearly there
Inputs and preconditions w 11
0
Progress reporting w 2
0
Result and completion w 14
60
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: 11. 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 75/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 60Result and completion. Output format stated, no completion criterion
    • 70When it triggers. States when to use, but not when not to
    • 70Failures and branches. 4 branches
    • 100Tools and files. No external tools needed
    • 100Steps. 64 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2871 tokens
    • 100Running it twice. No mutating operations
    • 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

    • +4Description does not say when NOT to use the skill (false activations)
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 2 example trigger phrases
    • +3Description length 744: enough signal without eating the budget
    • +4Structure: 18 headings
    • +3Step-by-step instructions: 64 items
    • +3Output format is stated explicitly
    • +4Has examples (2 code blocks)
    • +4Reference files are cited in the instructions (3 of 3)
    • +3All 2 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 99.

    External checks

    ClawHub: clean
    This skill is a focused loyalty-program planning aid with optional local report generators and no evidence of hidden access, account changes, credential use, or unsafe automation.
    LLM: benign (high) · VirusTotal: · 29 May 2026