SKILLEMALL.ai

AB menu-engineering-analysis

Use this skill when a restaurant chef, GM, multi-unit operator, or culinary director needs a Kasavana-Smith menu-engineering analysis of one menu over a defined sales period. Classifies every item as Star, Plowhorse, Puzzle, or Dog by contribution margin and popularity. Produces a DRAFT report with per-class action playbook, Top-3 quick wins, and data-quality flags for operator review before any price change or menu reprint.

ClawHub Agent Skills author: devasher v0.1.2 MIT-0 4 files body ≈ 3 359 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process B 79/100 · Nearly there — weak spots: when it triggers, running it twice

AnalyzerInfrastructureData and analyticstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
95/100
safety, quality, tests
Safety 60%
100
Quality 40%
87
Run on models
none yet
Process rating
B
79/100
Nearly there
When it triggers w 12
20
Running it twice w 4
30
Result and completion w 14
60
the three weakest of ten parameters · all ten

How to improve

    For the model run — optional
    • 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: 4. 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 79/100

    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 8 mutating operations with no state check
    • 60Result and completion. Output format stated, no completion criterion
    • 70Failures and branches. 9 branches
    • 100Tools and files. No external tools needed
    • 100Steps. 52 steps
    • 100Inputs and preconditions. Inputs and preconditions are listed
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3359 tokens
    • 100Progress reporting. Reports progress
    • 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)
    • +1No license
    • +2Single-language instructions
    • +3Description length 428: enough signal without eating the budget
    • +4Structure: 19 headings
    • +3Step-by-step instructions: 52 items
    • +3Output format is stated explicitly
    • +4Has examples (1 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 87.

    External checks

    ClawHub: clean
    This is a markdown-only advisory skill for restaurant menu analysis, with no executable code, network access, persistence, or hidden data handling.
    LLM: benign (high) · VirusTotal: · 28 May 2026