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 yuc16 · Codex Skill · ★ 57
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is PatentRadar safe to install? View the security audit →
PatentRadar 专利侵权竞品分析 · 全自动 · 可复核 输入一个专利公开号，自动产出一份律师 / 工程师可直接复核的 claim chart 报告——含可点击的证据 URL、逐特征对比推理，以及"还缺什么 / 下一步去哪查"的可执行建议。 https://github.com/user-attachments/assets/94f8f758-6a43-49a8-b16b-2e04e1d8e110 快速开始 · 架构 · 4 模块工作流 · Skill 模式 · Examples · 评估 为什么需要 PatentRadar 传统侵权排查靠人工：律师团队读专利权 → 拆特征 → 翻竞品手册 → 搜证据 → 写 claim chart，一份高质量报告动辄 2-5 天。 PatentRadar 把这条链路压到 1 小时：4 个模块串行流水线 + 多源搜索 + 多模态证据抓取 + 严苛评分规则，让 LLM 做"信息整理"和"逐特征对比"，把 可追溯、可复核 的 claim chart 报告直接交到律师手上。 设计原则 涉及 D/V、S/E、L/S 等比例约束的特征，必须从证据里抽数值现
| Stars | 57 |
| Forks | 11 |
| Language | Python |
| Category | Codex Skill |
| Quality Score | 56.0866864052792/100 |
| Open Issues | 1 |
| Last Updated | 2026-08-29 |
| Created | 2026-05-05 |
| Platforms | claude-code, codex, python |
| Est. Tokens | ~19k |
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PatentRadar is 专利侵权分析系统 —— 输入专利公开号，产出竞品侵权分析报告；同时打包成 skill，可被任意 agent（dsh, codex, claudecode 等） 调用。. It is categorized as a Codex Skill with 57 GitHub stars.
PatentRadar is primarily written in Python. It covers topics such as agentic-ai, claim-chart, claude-code.
You can find installation instructions and usage details in the PatentRadar GitHub repository at github.com/yuc16/PatentRadar. The project has 57 stars and 11 forks, indicating an active community.
The top alternatives to PatentRadar on Agent Skills Hub include ccproxy-api, legal-ai-agent, Claude-Patent-Creator. 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: