warrenduffer: AI-driven intraday trading bot for Indian stocks

by arimanyus · Agent Tool · ★ 100

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

Security audit verdict: SAFE · quality 71/100

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.

ai-gatewayai-tradingalgorithmic-tradingbacktestingindian-stock-marketindravahanintraday-tradingjevkite-connectkotak-neo

Quick Facts

Stars100
Forks30
LanguageTypeScript
CategoryAgent Tool
LicenseMIT
Quality Score71/100
Last Updated2026-09-23
Created2026-09-22
Platformsnode
Est. Tokens~12k

About warrenduffer

Warren Duffer Single Node process: broker REST client (Kotak Neo or Zerodha Kite), Jev (TypeSafe AI via Vercel AI Gateway) as a two-stage ranker over Nifty-50 names, live MIS orders, SQLite audit log, terminal-style dashboard on . Jev classifies, ranks, and decides take-profit. Code sizes, places the stop, and enforces the loss halt. Risk warning.

Compatible Skills

These tools work well together with warrenduffer for enhanced workflows:

  • okx-agent-trade-kit — semantic(0.62)+complementary+rare_topics+same_lang+similar_pop+shared_platform (81%)
  • ccxt-mcp — semantic(0.46)+complementary+rare_topics+same_lang+similar_pop+shared_platform (75%)

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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:

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