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 roman-rr · Claude Skill · ★ 113
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is trading-skills safe to install? View the security audit →
Trading Signals ⭐ Enjoying free signals? Push STAR to keep them free — it directly supports higher accuracy and continued development! Live crypto trading signals for Claude Code, OpenAI Codex, Cursor, Windsurf & 30+ AI agents. Every signal includes entry, SL, TP, leverage, position size, and a full transmission chain showing exactly which data points led to the trade. FREE during beta. More stars = more development time = better signals for everyone. Install Claude Code Plugin (recommended) npx (Claude Code, Codex, Cursor, Windsurf, etc.) MCP Server (Claude Desktop / Claude Code / Cursor) Or add to Claude Desktop config (): Exposes 5 tools: , , , , . OpenAI Codex CLI See agents/openai.yaml for agent configuration. Manual bash Claude Code git clone https://github.com/roma
| Stars | 113 |
| Forks | 13 |
| Language | Makefile |
| Category | Claude Skill |
| Quality Score | 75.5323478947771/100 |
| Last Updated | 2026-09-14 |
| Created | 2026-03-21 |
| Platforms | claude-code, codex |
| Est. Tokens | ~15k |
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trading-skills is 17 triggers × 44 algorithms × 3 AI experts — live crypto trading signals for Claude Code, Codex, Cursor & 30+ agents. Free during beta.. It is categorized as a Claude Skill with 113 GitHub stars.
trading-skills is primarily written in Makefile. It covers topics such as agent-skills, ai-trading, api.
You can find installation instructions and usage details in the trading-skills GitHub repository at github.com/roman-rr/trading-skills. The project has 113 stars and 13 forks, indicating an active community.
The top alternatives to trading-skills on Agent Skills Hub include ai-skills, tutor-skills, agent-designer. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
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: