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AC polymarket-quant-trader

Professional-grade Polymarket prediction market trading system. Includes Kelly Criterion position sizing, EV calculator, Bayesian probability updater, cross-platform arbitrage detector (Polymarket vs 1WIN), and autoresearch loop that self-improves strategy overnight via Brier score optimisation. Use when: user wants to trade prediction markets, find arbitrage opportunities, build a trading bot, or improve prediction accuracy. Triggers: polymarket, prediction markets, kelly criterion, EV trading, arb detector, brier score, prediction market bot, market making, quant trading, sports betting math, cross-platform arbitrage.

ClawHub Agent Skills author: Henry6262 v1.0.0 MIT-0 6 files body ≈ 4 553 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

As a process C 56/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

AnalyzerInfrastructuretype 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
91/100
safety, quality, tests
Safety 60%
99
Quality 40%
80
Run on models
none yet
Process rating
C
56/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
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 · 1

    ✓ No critical or high findings

    Medium and low: 1
    • low Exfiltration read-dotenv README.md:20
      Reads a .env file
      cp .env.example .env

    Files scanned: 6. 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 56/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 20When it triggers. No condition that starts the skill
    • 60Tools and files. Uses tools (bash, node) that frontmatter does not declare
    • 70Execution cost. Instruction body is 4553 tokens
    • 100Steps. 20 steps
    • 100Failures and branches. 2 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Running it twice. No mutating operations
    • 100Progress reporting. Reports progress

    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
    • -43 reference files, but SKILL.md never points to them: the model will not open them
    • +1No license
    • +2Single-language instructions
    • +3Description length 627: enough signal without eating the budget
    • +4Structure: 31 headings
    • +3Step-by-step instructions: 20 items
    • +4Has examples (26 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 80.

    External checks

    ClawHub: suspicious
    This real-money trading skill is broadly coherent, but it should be reviewed because it relies on unreviewed external bot code, wallet/API secrets, and under-scoped live trading controls.
    LLM: suspicious (high) · VirusTotal: · 29 May 2026