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 0xsline · MCP Server · ★ 1.0k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is awesome-deepseek-harness safe to install? View the security audit →
English | 简体中文 Awesome DeepSeek Harness Install Contribution guide DeepSeek Docs Public plugin topic Issues 完整目录 Curated DeepSeek Harness (DSH) ecosystem: plugins, tools & infrastructure. Sources: dsh-external/hub catalog and the public GitHub dsh-plugin topic. Note: the GitHub topic is public; some repository links may still require org access. Contents Install Core Context & Search Input & Editing UI & Experience IDE & Clients Browser & Remote Models & Inference [Git & Engineering](#g
| Stars | 1,021 |
| Forks | 373 |
| Language | Python |
| Category | MCP Server |
| License | CC0-1.0 |
| Quality Score | 65.2820081518693/100 |
| Open Issues | 3 |
| Last Updated | 2026-09-08 |
| Created | 2026-08-11 |
| Platforms | mcp, python |
| Est. Tokens | ~22k |
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awesome-deepseek-harness is DeepSeek Harness (DSH) ecosystem: curated plugins, tools, and infrastructure from dsh-external/hub and the public dsh-plugin topic.. It is categorized as a MCP Server with 1.0k GitHub stars.
awesome-deepseek-harness is primarily written in Python. It covers topics such as agent, ai, ai-agents.
You can find installation instructions and usage details in the awesome-deepseek-harness GitHub repository at github.com/0xsline/awesome-deepseek-harness. The project has 1.0k stars and 373 forks, indicating an active community.
awesome-deepseek-harness is released under the CC0-1.0 license, making it free to use and modify according to the license terms.
The top alternatives to awesome-deepseek-harness on Agent Skills Hub include awesome-deepseek-harness, AITreasureBox, memsearch. 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: