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 Hyr1sky · Agent Tool · ★ 56
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is TheGrandQuiz safe to install? View the security audit →
正考级 · TheGrandQuiz 俺们老中最会的就是考 Product-first learning agent. Harness-grade internals. 一个考核驱动、可追溯、local-first 的个人学习 Agent， 由可观测 Agent Runtime、Trace/Replay 与 Eval Harness 提供工程支撑。 正考级不只是帮你读完材料。 它通过有证据的对话和逐题考核，找出掌握得似是而非的 地方，把薄弱概念记下来，并在下一轮优先复考。 非常遗憾的灵感来源，在经历了这么多年的教育之后，最高效的记忆方法可能还是考。 不只是另一个聊天套壳 TheGrandQuiz 是一个真实学习产品，也是 Agent 工程能力的完整竖切：学习者
| Stars | 56 |
| Forks | 2 |
| Language | Python |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 48.452701732725/100 |
| Last Updated | 2026-09-16 |
| Created | 2026-06-12 |
| Platforms | python |
| Est. Tokens | ~13k |
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TheGrandQuiz is Assessment-driven, local-first learning agent built on an observable Agent Runtime and eval harness—grounded ingestion, trace replay, HITL assessment, durable memory, and reviewable voice input.. It is categorized as a Agent Tool with 56 GitHub stars.
TheGrandQuiz is primarily written in Python. It covers topics such as adaptive-learning, agent-harness, agent-observability.
You can find installation instructions and usage details in the TheGrandQuiz GitHub repository at github.com/Hyr1sky/TheGrandQuiz. The project has 56 stars and 2 forks, indicating an active community.
TheGrandQuiz is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to TheGrandQuiz on Agent Skills Hub include Bloom, omnicoreagent, claudex. 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: