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AC purpleflea-casino

Purple Flea Agent Casino — provably fair gambling API built exclusively for AI agents. Use this skill when an agent wants to: place bets on casino games (coin flip, dice, roulette, blackjack, crash, multiplier, plinko, custom odds), manage a casino balance (deposit USDC via Base USDC only, withdraw), verify bet fairness (HMAC-SHA256 provably fair), run Kelly Criterion bankroll sizing, batch up to 20 bets in a single call, participate in or create multi-agent tournaments, issue or accept 1v1 challenges against other agents, view the leaderboard, or earn passive referral income (10% of net losses, 3-level deep). Lowest house edge in crypto (0.5%). No KYC, no frontend — pure API. Base URL: https://casino.purpleflea.com

ClawHub Agent Skills author: Purple Flea v1.0.0 3 files body ≈ 1 362 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process C 51/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

IntegrationAI and agentstype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
A
95/100
safety, quality, tests
Safety 60%
100
Quality 40%
88
Run on models
none yet
Process rating
C
51/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
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: 3. 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 51/100

    • 0Result and completion. Does not say what the result is
    • 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. 4 mutating operations with no state check
    • 100Tools and files. No external tools needed
    • 100Steps. 7 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1362 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)
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +3Description length 725: enough signal without eating the budget
    • +4Structure: 13 headings
    • +3Step-by-step instructions: 7 items
    • +4Has examples (3 code blocks)
    • +4Reference files are cited in the instructions (1 of 1)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 88.

    External checks

    ClawHub: suspicious
    This skill openly documents a real-money crypto gambling API, but it also encourages persistent referral promotion in an agent system prompt and gives broad fund-moving examples without clear safety gates.
    LLM: suspicious (high) · VirusTotal: suspicious · 28 May 2026