SKILLEMALL.ai

AB game-design-systematizing-empathizing-audit

Evaluate a game, feature, system, UX flow, progression loop, live-ops mechanic, monetization surface, or design concept on two axes: systematizing and empathizing. Use when you want to understand whether a design is structurally logic-driven or emotionally player-attuned, what kind of player personas it is likely to attract or repel, and what practical consequences follow from that positioning without assuming one quadrant is universally best.

ClawHub Agent Skills author: Stanislav Stankovic v1.0.0 MIT-0 5 files body ≈ 1 430 tokens Open the sourceclawhub.ai analyzed 2 d ago

As a process B 67/100 · Nearly there — weak spots: when it triggers, inputs and preconditions, progress reporting

AnalyzerInfrastructureMarketingDesigntype and topics are labelled automatically from the skill text
JSON
Technical rating
A
96/100
safety, quality, tests
Safety 60%
100
Quality 40%
91
Run on models
none yet
Process rating
B
67/100
Nearly there
Inputs and preconditions w 11
0
Progress reporting w 2
0
When it triggers w 12
20
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: 5. 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 67/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 55Failures and branches. 1 branches
    • 60Result and completion. Output format stated, no completion criterion
    • 100Tools and files. No external tools needed
    • 100Steps. 90 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1430 tokens
    • 100Running it twice. No mutating operations

    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 447: enough signal without eating the budget
    • +4Structure: 23 headings
    • +3Step-by-step instructions: 90 items
    • +3Output format is stated explicitly
    • +4Has examples (0 code blocks)
    • +4Reference files are cited in the instructions (3 of 3)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 91.

    External checks

    ClawHub: clean
    This is a text-only game design audit skill that reads its own reference notes and produces structured analysis, with no code, credentials, persistence, or external data handling.
    LLM: benign (high) · VirusTotal: · 29 May 2026