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 1Panel-dev · Codex Skill · ★ 64
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is CordysCRM-skills safe to install? View the security audit →
Cordys CRM Skill 你的专属 AI 助手,告别在传统页面上点来点去 角色感知 · 管道原生 · 默认零信任 同一个 CRM,不同的人看到不同的世界 同一个系统。同一份数据。同一个问题:"看看线索" 销售听到的是待办优先级。经理看到的是团队健康仪表盘。财务得到的是资金回笼全景。 Cordys CRM Skill 不是给 CRM 加一层界面,而是加一层智能——它理解谁在问、卡在 L2C 链路的哪个环节。它说人话,跨模块推理,在你开口问之前就告诉你哪里不对劲。 一行配置。 填好 API 密钥,系统自动识别你的身份、匹配角色、激活对应的认知视角。不用搭仪表盘,不用存筛选器。 五个角色,五种视角 不是偏好设置。是系统在"展示什么、先展示什么、以什么紧迫度展示"这三个维度上的本质切换。 销售 经理 高管 商务 财务 架构 不是一个巨型提示词。是九个目标明确的引擎晶格,按需加载,通过共享上下文总线协同工作。 mermaid flowchart LR U(["💬 输入"]) -- GATE{"意图路由"} GATE -- QUERY["🔍 查询引擎"] GATE -- LINK["🔗 链路引擎"] GATE -- FUNNEL["📊 漏斗引擎"] GATE -- INTENT["🧭 意图引擎"] GATE -- APPR["✅ 审批管道"] GATE -- WRITE["✏️ 写入引擎"] QUERY -- ROLE{"🧠 角色透镜"} LINK ROLE FUNNEL ROLE INTENT --
| Stars | 64 |
| Forks | 24 |
| Language | Python |
| Category | Codex Skill |
| Quality Score | 59.9215855437475/100 |
| Last Updated | 2026-08-10 |
| Created | 2026-02-03 |
| Platforms | claude-code, codex, python |
| Est. Tokens | ~13k |
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CordysCRM-skills is 你的专属 AI CRM 销售助理,可集成至 OpenClaw、WorkBuddy 等任何 Agent,覆盖 L2C 整个链路。. It is categorized as a Codex Skill with 64 GitHub stars.
CordysCRM-skills is primarily written in Python. It covers topics such as claude, codex, openclaw.
You can find installation instructions and usage details in the CordysCRM-skills GitHub repository at github.com/1Panel-dev/CordysCRM-skills. The project has 64 stars and 24 forks, indicating an active community.
The top alternatives to CordysCRM-skills on Agent Skills Hub include deepcontext-mcp, agent-skills, ask-user-questions-mcp. 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: