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HIPAA-合规

用于处理医疗隐私与安全合规工作的 HIPAA 专用入口点。当任务明确涉及 HIPAA、受保护健康信息(PHI)、覆盖实体、业务关联协议、违规态势或美国医疗合规要求时使用。

HIPAA-合规 是什么?

这是一个专为美国医疗合规场景设计的 AI 技能入口点,主要面向 HIPAA 相关任务。该技能保持精简和规范化,依赖 `healthcare-phi-compliance` 作为处理 PHI/PII 数据分类、审计日志、加密和防泄漏的主要实施技能;同时通过 `healthcare-reviewer` 为代码、架构或产品行为提供医疗合规性的二次审查。它建议将 HIPAA 视为医疗隐私技能的覆盖层,遵循最小必要访问原则,并要求对所有涉及 PHI 的读/写/导出事件进行审计。支持的平台包括 Claude Code、Cursor、Codex 等 AI 代理工具,通过激活该技能,开发者可以确保在处理患者数据、第三方 SaaS 工具或 LLM 提供商时,始终遵守 BAA 要求和 HIPAA 安全规则。

兼容平台~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/affaan-m/ECC/tree/main/skills/hipaa-compliance

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文档

HIPAA 合规

当任务明确涉及美国医疗合规时,将此作为 HIPAA 专用入口点使用。本技能刻意保持精简和规范化:

  • healthcare-phi-compliance 仍是处理 PHI/PII 数据分类、审计日志、加密和防泄漏的主要实施技能。
  • healthcare-reviewer 仍是专用审查者,用于代码、架构或产品行为需要医疗合规二次审核的场景。
  • security-review 仍适用于通用身份验证、输入处理、密钥、API 和部署加固。

使用时机

  • 请求明确提到 HIPAA、PHI、覆盖实体、业务关联方或 BAA
  • 构建或审查存储、处理、导出或传输 PHI 的美国医疗软件
  • 评估日志、分析、LLM 提示词、存储或支持工作流是否造成 HIPAA 暴露
  • 设计面向患者或临床医生的系统,其中最小必要访问和可审计性至关重要

工作原理

将 HIPAA 视为医疗隐私技能的覆盖层:

  1. 从 healthcare-phi-compliance 开始,获取具体实施规则。
  2. 应用 HIPAA 专用决策点:
    • 此数据是否为 PHI?
    • 此行为者是覆盖实体还是业务关联方?
    • 供应商或模型提供方是否需要 BAA 才能接触数据?
    • 访问权限是否限制在最小必要范围内?
    • 读/写/导出事件是否可审计?
  3. 如果任务影响患者安全、临床工作流或受监管的生产架构,升级到 healthcare-reviewer。

HIPAA 专用护栏

  • 切勿将 PHI 放置于日志、分析事件、崩溃报告、提示词或客户端可见的错误信息中。
  • 切勿在 URL、浏览器存储、屏幕截图或复制的示例负载中暴露 PHI。
  • 要求对 PHI 的读取和写入进行经过身份验证的访问、范围限定的授权和审计跟踪。
  • 默认阻塞第三方 SaaS、可观测性、支持工具和 LLM 提供商,直到其 BAA 状态和数据边界清晰。
  • 遵循最小必要访问原则:正确用户应仅看到完成任务所需的最小 PHI 片段。
  • 优先使用不透明的内部 ID,而非姓名、MRN、电话号码、地址或其他标识符。

示例

示例 1:产品请求涉及 HIPAA

用户请求:

在临床医生仪表板中添加 AI 生成的访问摘要。我们服务美国诊所,需要保持 HIPAA 合规。

响应模式:

  • 激活 hipaa-compliance
  • 使用 healthcare-phi-compliance 审查 PHI 移动、日志记录、存储和提示词边界
  • 在向摘要生成提供方发送任何 PHI 前,确认其是否受 BAA 覆盖
  • 如果摘要影响临床决策,升级到 healthcare-reviewer

示例 2:供应商/工具决策

用户请求:

我们能否将支持记录和患者消息发送到分析栈中?

响应模式:

  • 假设这些消息可能包含 PHI
  • 除非分析供应商已获批准用于 HIPAA 工作负载且数据路径已最小化,否则阻止该设计
  • 尽可能要求进行脱敏处理或使用非 PHI 事件模型

相关技能

  • healthcare-phi-compliance
  • healthcare-reviewer
  • healthcare-emr-patterns
  • healthcare-eval-harness
  • security-review

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

accessibility

Design, implement, and audit inclusive digital products using WCAG 2.2 Level AA. Use when building or auditing UI that must meet WCAG 2.2 Level AA, or when reviewing a change for keyboard, contrast, or screen-reader support.

affaan-m/content-engine

Create platform-native content systems for X, LinkedIn, TikTok, YouTube, newsletters, and repurposed multi-platform campaigns. Use when the user wants social posts, threads, scripts, content calendars, or one source asset adapted cleanly across platforms.

affaan-m/ECC

Multi-platform content distribution across X, LinkedIn, Threads, and Bluesky. Adapts content per platform using content-engine patterns. Never posts identical content cross-platform. Use when the user wants to distribute content across social platforms.

affaan-m/fal-ai-media

Unified media generation via fal.ai MCP — image, video, and audio. Covers text-to-image (Nano Banana), text/image-to-video (Seedance, Kling, Veo 3), text-to-speech (CSM-1B), and video-to-audio (ThinkSound). Use when the user wants to generate images, videos, or audio with AI.

affaan-m/manim-video

日本語翻訳:このファイルは manim-video 用の日本語翻訳が必要です

affaan-m/remotion-video-creation

Remotion のベストプラクティス - React で動画を作成する。3D、アニメーション、音声、字幕、チャート、トランジションなどをカバーするドメイン固有の29のルール。

affaan-m/video-editing

AI-assisted video editing workflows for cutting, structuring, and augmenting real footage. Covers the full pipeline from raw capture through FFmpeg, Remotion, ElevenLabs, fal.ai, and final polish in Descript or CapCut. Use when the user wants to edit video, cut footage, create vlogs, or build video content.

agent-architecture-audit

Full-stack diagnostic for agent and LLM applications. Audits the 12-layer agent stack for wrapper regression, memory pollution, tool discipline failures, hidden repair loops, and rendering corruption. Produces severity-ranked findings with code-first fixes. Essential for developers building agent applications, autonomous loops, or any LLM-powered feature. Use when an agent or LLM feature misbehaves and the failing layer is unknown, or before shipping an agent stack.

agent-eval

Head-to-head comparison of coding agents (Claude Code, Aider, Codex, etc.) on custom tasks with pass rate, cost, time, and consistency metrics. Use when choosing between coding agents, or when a change to an agent setup needs measured pass rate, cost, and time rather than an impression.

agent-harness-construction

Design and optimize AI agent action spaces, tool definitions, and observation formatting for higher completion rates. Use when defining or revising an agent

agentic-engineering

Operate as an agentic engineer using eval-first execution, decomposition, and cost-aware model routing. Use when planning or executing engineering work that agents will carry out end to end.

agentic-os

Build persistent multi-agent operating systems on Claude Code. Covers kernel architecture, specialist agents, slash commands, file-based memory, scheduled automation, and state management without external databases. Use when building a persistent multi-agent system on Claude Code with its own memory, commands, and scheduling.

agent-introspection-debugging

Structured self-debugging workflow for AI agent failures using capture, diagnosis, contained recovery, and introspection reports. Use when an agent run fails and you need a reproducible diagnosis instead of a retry.

agent-payment-x402

Add x402 payment execution to AI agents with per-task budgets, spending controls, and non-custodial wallets. Supports Base through agentwallet-sdk and X Layer through OKX Payments / OKX Agent Payments Protocol. Use when an agent must pay for something itself and needs per-task budgets, spending controls, and a non-custodial wallet.

agent-self-evaluation

Use after completing any non-trivial task. The agent self-rates its output on 5 axes — accuracy, completeness, clarity, actionability, conciseness — with concrete evidence per criterion. Produces a structured 1-5 scorecard with specific improvement suggestions.

agent-sort

Build an evidence-backed ECC install plan for a specific repo by sorting skills, commands, rules, hooks, and extras into DAILY vs LIBRARY buckets using parallel repo-aware review passes. Use when ECC should be trimmed to what a project actually needs instead of loading the full bundle.

ai-first-engineering

Engineering operating model for teams where AI agents generate a large share of implementation output. Use when setting team process, review gates, or ownership rules for a codebase largely written by agents.

ai-regression-testing

Regression testing strategies for AI-assisted development. Sandbox-mode API testing without database dependencies, automated bug-check workflows, and patterns to catch AI blind spots where the same model writes and reviews code. Use when adding regression coverage to AI-assisted code, or when the same model both wrote and reviewed a change.

android-clean-architecture

Clean Architecture patterns for Android and Kotlin Multiplatform projects — module structure, dependency rules, UseCases, Repositories, and data layer patterns. Use when structuring modules, layers, or data flow in an Android or KMP project.

angular-developer

Generates Angular code and provides architectural guidance. Trigger when creating projects, components, or services, or for best practices on reactivity (signals, linkedSignal, resource), forms, dependency injection, routing, SSR, accessibility (ARIA), animations, styling (component styles, Tailwind CSS), testing, or CLI tooling.

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