SKILLEMALL.ai

AC game-design-attribution-audit

Audit a game, feature, combat scenario, progression step, failure state, onboarding beat, or reward outcome through the lens of attribution theory: how players explain success and failure. Use when evaluating whether players will blame themselves, the system, luck, or hidden rules; diagnosing perceived unfairness, learned helplessness, rage, or churn after losses; or identifying where clarity, control, and feedback are too weak for healthy learning.

ClawHub Agent Skills author: Stanislav Stankovic v1.0.0 MIT-0 4 files body ≈ 1 563 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process C 59/100 · Has gaps — weak spots: when it triggers, inputs and preconditions, failures and branches

AnalyzerInfrastructuretype 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
C
59/100
Has gaps
Inputs and preconditions w 11
0
Failures and branches w 10
0
Progress reporting w 2
0
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 59/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Failures and branches. Linear process with no failure handling
    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 1 mutating operations with no state check
    • 60Result and completion. Output format stated, no completion criterion
    • 100Tools and files. No external tools needed
    • 100Steps. 88 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1563 tokens

    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)
    • +4No input/output examples
    • +1No license
    • +2Single-language instructions
    • +3Description length 453: enough signal without eating the budget
    • +4Structure: 27 headings
    • +3Step-by-step instructions: 88 items
    • +3Output format is stated explicitly
    • +4Reference files are cited in the instructions (2 of 2)

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

    External checks

    ClawHub: clean
    This is a markdown-only game design review skill that does not ask for code execution, credentials, network access, persistence, or sensitive data handling.
    LLM: benign (high) · VirusTotal: · 29 May 2026