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 usewhale · MCP Server · ★ 659
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is DeepSeek-Code-Whale safe to install? View the security audit →
Whale 简体中文 · English Blazingly fast · 98% prompt cache hit · Zero bloat Whale — AI coding agent for DeepSeek, in any environment. Long context, tools, and programmable workflows — start in the terminal, scale to desktop and beyond. 🚀 Quick
| Stars | 659 |
| Forks | 44 |
| Language | Go |
| Category | MCP Server |
| License | MIT |
| Quality Score | 67.6245390981011/100 |
| Open Issues | 10 |
| Last Updated | 2026-06-20 |
| Created | 2026-05-06 |
| Platforms | cli, go, mcp |
| Est. Tokens | ~17k |
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DeepSeek-Code-Whale is Whale — blazingly fast, terminal-first AI coding agent for DeepSeek. ~98% prompt cache hit rate, 1M context, MCP tools, dynamic workflows.. It is categorized as a MCP Server with 659 GitHub stars.
DeepSeek-Code-Whale is primarily written in Go. It covers topics such as coding-agent, deepseek, deepseek-agent.
You can find installation instructions and usage details in the DeepSeek-Code-Whale GitHub repository at github.com/usewhale/DeepSeek-Code-Whale. The project has 659 stars and 44 forks, indicating an active community.
DeepSeek-Code-Whale is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to DeepSeek-Code-Whale on Agent Skills Hub include blade-code, claude-code-plugins-plus-skills, open-cowork. 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: