FriesTrader — security grade SAFE, quality 68/100

Security audit verdict: SAFE · quality 68/100

No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →

by YizhiSong · MCP Server · ★ 157

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

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About FriesTrader

FriesTrader An AI trading agent built to run cheap and fully on its own, trading real orders on Robinhood using its Agentic Trading MCP server. Once set up, it runs unattended on its own schedule every weekday, no manual triggering needed. Two short scheduled Claude Code sessions a day screen stocks, write out their reasoning, and (only under a narrow, explicit gate) place real trades, without a team of specialized sub-agents burning tokens on every decision. The actual safety mechanism is mechanical, auditable risk rules, not the model's judgment, and because it's just two lean sessions instead of a multi-agent pipeline, it runs comfortably on a Claude Pro subscription (as low as $200/year on the annual plan), no Claude Max or metered API spend required. This is a template/framework extracted from a real, live deployment. Adapt it, don't just run it blind — read "What this does and doesn't solve" below before pointing it at real money. Why this is safer than it sounds "Fully autonomous" and "trading real money" together should make you nervous.

agentic-aialgorithmic-tradingautomationclaudeclaude-codellm-agentsmcprisk-managementrobinhoodtrading-bot

Quick Facts

Stars157
Forks64
LanguagePython
CategoryMCP Server
LicenseMIT
Quality Score67.8426050475659/100
Last Updated2026-09-11
Created2026-07-17
Platformsclaude-code, mcp, python
Est. Tokens~13k

Compatible Skills

These tools work well together with FriesTrader for enhanced workflows:

  • anti-gambling-trader-tw — semantic(0.36)+complementary+rare_topics+similar_pop+shared_platform (62%)
  • crypto-trading-arena — semantic(0.40)+complementary+rare_topics+similar_pop+shared_platform (59%)
  • LLM_trader — semantic(0.37)+complementary+rare_topics+similar_pop (57%)

FriesTrader alternative? Top 6 similar tools

Looking for a FriesTrader alternative? If you're comparing FriesTrader with other mcp server tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.

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  • mcp-server-code-execution-mode by elusznik · ⭐ 337

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  • claude-emporium by Vvkmnn · ⭐ 82

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  • open-computer-use by coasty-ai · ⭐ 763

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Frequently Asked Questions

What is FriesTrader?

FriesTrader is Robinhood Agentic Trading agent — a fully automated AI trading bot placing real orders through Robinhood's Agentic Trading MCP, under mechanical, auditable risk rules the model cannot override. Able t. It is categorized as a MCP Server with 157 GitHub stars.

What programming language is FriesTrader written in?

FriesTrader is primarily written in Python. It covers topics such as agentic-ai, algorithmic-trading, automation.

How do I install or use FriesTrader?

You can find installation instructions and usage details in the FriesTrader GitHub repository at github.com/YizhiSong/FriesTrader. The project has 157 stars and 64 forks, indicating an active community.

What license does FriesTrader use?

FriesTrader is released under the MIT license, making it free to use and modify according to the license terms.

What are the best alternatives to FriesTrader?

The top alternatives to FriesTrader on Agent Skills Hub include agent-skills, intellegix-code-agent-toolkit, Overture. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.

How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. 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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