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social-graph-ranker
加权社交图谱排名,用于在X和LinkedIn上发现温暖介绍、桥梁评分和网络差距分析。当用户想要可重用的图谱排名引擎本身,而不是其上层更广泛的推广或网络维护工作流时使用。
加权社交图谱排名,用于在X和LinkedIn上发现温暖介绍、桥梁评分和网络差距分析。当用户想要可重用的图谱排名引擎本身,而不是其上层更广泛的推广或网络维护工作流时使用。
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 | social-graph-ranker |
| description | 加权社交图谱排名,用于在X和LinkedIn上发现温暖介绍、桥梁评分和网络差距分析。当用户想要可重用的图谱排名引擎本身,而不是其上层更广泛的推广或网络维护工作流时使用。 |
| origin | ECC |
面向网络感知外联的规范化加权图排名层。
当用户需要以下功能时使用此工具:
lead-intelligence 或 connections-optimizer 理解图谱数学原理当用户主要需要排名引擎时选择此技能:
当用户真正需要以下功能时,请勿单独使用此技能:
lead-intelligenceconnections-optimizer收集或推断:
给定:
T = 加权目标集M = 你当前的互关/直接联系人d(m, t) = 从互关 m 到目标 t 的最短跳数距离w(t) = 来自信号评分的目标权重基础桥梁分数:
B(m) = Σ_{t ∈ T} w(t) · λ^(d(m,t) - 1)
其中:
λ 是衰减因子,通常为 0.5二度扩展:
B_ext(m) = B(m) + α · Σ_{m' ∈ N(m) \\ M} Σ_{t ∈ T} w(t) · λ^(d(m',t))
其中:
N(m) \\ M 是互关认识但你认识的人集合α 对二度可达性进行折扣,通常为 0.3响应调整后的最终排名:
R(m) = B_ext(m) · (1 + β · engagement(m))
其中:
engagement(m) 是归一化的响应性或关系强度β 是参与度加成,通常为 0.2解读:
R(m) 和直接桥梁路径 -> 温暖引荐请求R(m) 和一跳桥梁路径 -> 条件性引荐请求R(m) 或无可行桥梁 -> 直接外联或关注缺口填补在图遍历前根据当前优先级集对目标进行加权:
在遍历后对互关进行加权:
R(m) 排名。社交图谱排名
====================
优先级集合:
平台:
衰减模型:
顶级桥梁
- 共同好友 / 连接
基础分数:
扩展分数:
最佳目标:
路径摘要:
推荐操作:
条件路径
- 共同好友 / 连接
原因:
额外跳数成本:
无温暖路径
- 目标
推荐:直接联系 / 填补图谱空白
lead-intelligence 在更广泛的目标发现和外联管道中使用此排名模型connections-optimizer 在决定保留、修剪或添加谁时使用相同的桥梁逻辑brand-voice 应在起草任何引荐请求或直接外联之前运行x-api 提供X图谱访问和可选执行路径