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 SherwinQ · Agent Tool · ★ 112
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Karpathy Wiki — LLM 驱动的知识管理系统 基于 Andrej Karpathy 提出的 LLM Wiki 模式 构建的 Agent Skill，通过四阶段流水线将碎片化信息转化为结构化、可检索、持续增长的个人知识库。 核心理念 Wiki 是一个持久化、可复利增长的知识制品，不是一次性问答工具。 You never write the wiki. The LLM writes everything. You just steer, and every answer compounds. 四阶段流水线 每个阶段结束后追加 ，每个阶段增强下一个阶段，循环持续运行。 特性 核心特性 四阶段分离 — Ingest（采集）与 Compile（编译）解耦，可批量采集后统一编译 先分类再提取 — Ingest 时 Classify 来源类型和实体类型，Compile 时按类型选择章节结构 7 种实体类型 — concept · person · tool · event · comparison · pattern · overview，每种有不同的必需章节 Token 预算 / 渐进加载 — L0-L3 四级上下文管理，先索引后全文 Counter-Arguments — 综合 3+ 来源的文章自动添加反论段落，防止偏见积累 YAML Frontmatter 规范 — entitytype、confidence、跨领域 domains，支持 Obsidian Dataview 动态查询 Obsidian CLI 优先 — 创建/移动/重命名通过 CLI 自动维护双链完整性 三级索引 — Dashboard（总览）、Concept Index（文章目录）、Source Index（资料映射） 查询回填 + Promote
| Stars | 112 |
| Forks | 17 |
| Language | Python |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 60.4461797612633/100 |
| Last Updated | 2026-04-28 |
| Created | 2026-04-05 |
| Platforms | python |
| Est. Tokens | ~5k |
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karpathy-wiki is 基于 [Andrej Karpathy]提出的 [LLM Wiki 模式]构建的 Agent Skill，通过四阶段流水线将碎片化信息转化为结构化、可检索、持续增长的个人知识库。. It is categorized as a Agent Tool with 112 GitHub stars.
karpathy-wiki is primarily written in Python.
You can find installation instructions and usage details in the karpathy-wiki GitHub repository at github.com/SherwinQ/karpathy-wiki. The project has 112 stars and 17 forks, indicating an active community.
karpathy-wiki is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to karpathy-wiki on Agent Skills Hub include tutor-skills. 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.
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