以证据为先的ECC当前状态研究工作流程。当用户希望基于当前公开证据和提供的本地上下文获取最新事实、比较、丰富信息或建议时使用。
npx skills add https://github.com/affaan-m/ECC --skill research-ops复制此命令并粘贴到 Claude Code 中以安装该技能
React 18/19 patterns including hooks discipline, server/client component boundaries, Suspense + error boundaries, form actions, data fetching, state management decision trees, and accessibility-first composition. Use when writing or reviewing React components.
React and Next.js performance optimization patterns adapted from Vercel Engineering's React Best Practices (https://github.com/vercel-labs/agent-skills). Organizes 70+ rules across 8 priority categories — waterfalls, bundle size, server-side, client fetching, re-render, rendering, JS micro-perf, advanced. Use when writing, reviewing, or refactoring React/Next.js code for performance.
React component testing with React Testing Library, Vitest/Jest, MSW for network mocking, accessibility assertions with axe, and the decision boundary between component tests and Playwright/Cypress end-to-end runs. Use when writing or fixing tests for React components, hooks, or pages.
Agent-driven scheduling and publishing of social media posts across 13 platforms via SocialClaw. Use when the user wants to publish to X, LinkedIn, Instagram, Facebook Pages, TikTok, Discord, Telegram, YouTube, Reddit, WordPress, or Pinterest — or when managing campaigns, uploading media, or monitoring post delivery status.
End-to-end marketing campaign planning and execution. Covers audience research, positioning, campaign angle definition, landing page copy, email sequences, social posts, ad copy, short-form video scripts, and content calendars. Use as the orchestration layer for multi-channel product launches.
Accessibility patterns for React and Next.js — semantic HTML, ARIA attributes, form labeling, keyboard navigation, focus management, and screen reader support. Use when building any interactive UI component or form.
| name | research-ops |
| description | 以证据为先的ECC当前状态研究工作流程。当用户希望基于当前公开证据和提供的本地上下文获取最新事实、比较、丰富信息或建议时使用。 |
| origin | ECC |
当用户要求研究当前信息、比较选项、丰富人员或公司信息，或将重复查询转化为可监控的工作流时，使用此功能。
这是仓库研究栈的操作封装。它并非 deep-research、exa-search 或 market-research 的替代品；而是指示何时以及如何将它们结合使用。
在相关场景下，将这些 ECC 原生技能纳入工作流：
exa-search：用于快速发现当前网络信息deep-research：用于多源综合并附带引用market-research：当最终结果应为建议或排序决策时使用lead-intelligence：当任务针对人员/公司而非通用研究时使用knowledge-ops：当结果需持久存储于后续上下文时使用将任何提供的材料规范化为：
如果用户已构建部分模型，不要从零开始重新分析。
在搜索前选择正确的路径：
exa-search 进行快速发现deep-researchmarket-researchlead-intelligence对于重要声明，说明其属于：
对时效性敏感的答案应包含具体日期。
如果用户可能反复提出相同的研究问题，请明确说明，并建议采用监控或工作流层，而非永远重复相同的手动搜索。
问题类型
- 事实性 / 比较性 / 补充性 / 监控性
证据
- 有来源的事实
- 用户提供的上下文
推论
- 从证据中得出的结论
建议
- 答案或下一步行动
- 是否应将其设为监控项