anti-gambling-trader-tw: 免費開源的投資反詐、交易統計與自動化交易程式工具：預設 PaperBroker，提供 14 種券商／交易所選項

by mars-tw · Claude Skill · ★ 904

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How to install anti-gambling-trader-tw

git clone https://github.com/mars-tw/anti-gambling-trader-tw.git

Taken from the project's README on GitHub. Check the security audit before running it.

Security audit verdict: SAFE · quality 62/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

algorithmic-tradinganti-fraudanti-gamblingbacktestbroker-apiclaude-codecryptopaper-tradingpythonscam-detection

Quick Facts

Stars904
Forks65
LanguagePython
CategoryClaude Skill
LicenseMIT
Quality Score62/100
Open Issues1
Last Updated2026-10-09
Created2026-06-26
Platformsclaude-code, python
Est. Tokens~16k

About anti-gambling-trader-tw

反詐投資王（Anti-Gambling Trader） 你在懷疑「投資群組是不是詐騙」「老師帶單可不可信」「我的交易到底是實力還是運氣」嗎？ 這是一個免費開源的交易統計、投資反詐與自動化交易開發工具：把交易紀錄 （台股／美股／加密貨幣）丟進來， 它用期望值、顯著性檢定與樣本外驗證，給你統計證據判斷獲利更像可重複的優勢， 還是運氣＋倖存者偏差（賭博）；並內建詐騙話術掃描、假績效鑑識與假老師檢驗。 它也能把交易邏輯轉成可回測策略，並產生可接券商 API 的自動化交易程式專案。 交易分析資料全程在你自己的電腦上處理，不上傳任何伺服器。 一個誠實到不討喜的工具。它不會告訴你「你會賺錢」—— 不適合長期投資的，它會直接勸退你。 👉 急著找答案？先看 常見問題 FAQ：投資群組是詐騙嗎、 勝率 90% 可信嗎、出金要先繳稅正常嗎、被騙了怎麼辦。 🤖 不只分析，也能建立自動化交易程式。...

Compatible Skills

These tools work well together with anti-gambling-trader-tw for enhanced workflows:

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  • mcp-aktools — semantic(0.42)+complementary+same_lang+similar_pop+shared_platform (65%)
  • prism-insight — semantic(0.41)+complementary+same_lang+similar_pop+shared_platform (64%)
  • hhxg-top-hhxg-python — semantic(0.40)+complementary+same_lang+similar_pop+shared_platform (64%)
  • futu-stock-mcp-server — semantic(0.39)+complementary+same_lang+similar_pop+shared_platform (64%)

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. It is a first-layer scan, not a manual audit — we say so rather than overstate it.

The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.

Sources & who's responsible:

View on GitHub → Browse Claude Skill tools