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connections-optimizer

重新组织用户的社交网络，而非将对外联系视为单向的潜在客户列表。

connections-optimizer 是什么？

Connections Optimizer 是一款智能社交网络优化技能，旨在帮助用户系统性地管理和优化其在 X（Twitter）和 LinkedIn 上的社交图谱。与传统的单向“潜在客户列表”思维不同，该技能通过识别高价值连接、清理低效关注、以及发现温暖路径来提升网络质量。它支持三种操作模式（轻量、默认、积极），并提供基于互惠性、近期活跃度、优先级对齐度等正反向信号的评分模型。该技能可生成详细的审查报告（包括清除队列、审查队列、保护列表和添加目标），并通过 Apple Mail、X DM 或 LinkedIn 消息以用户真实语气草拟外联消息，但始终默认手动审查，绝不自动执行。

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Connections Optimizer

重新组织用户的社交网络，而非将对外联系视为单向的潜在客户列表。

此技能处理：

  • X 的关注清理与扩展
  • LinkedIn 的关注与连接分析
  • 先审查再清除的队列
  • 添加与关注建议
  • 温暖路径识别
  • 以用户真实语气生成 Apple Mail、X DM 和 LinkedIn 草稿

何时激活

  • 用户想要清理他们的 X 关注列表
  • 用户想要重新平衡他们关注或保持联系的人
  • 用户说“清理我的网络”、“我应该取关谁”、“我应该关注谁”、“我应该与谁重新建立联系”
  • 外联质量取决于网络结构，而不仅仅是冷名单生成

必需输入

收集或推断：

  • 当前优先级和活跃工作
  • 目标角色、行业、地理位置或生态系统
  • 平台选择：X、LinkedIn 或两者
  • 免动名单
  • 模式：light-pass、default 或 aggressive

如果用户未指定模式，则使用 default。

工具要求

首选

  • x-api 用于 X 图谱检查和近期活动
  • lead-intelligence 用于目标发现和温暖路径排名
  • social-graph-ranker 当用户希望在更广泛的线索工作流程之外独立评估桥梁价值时
  • Exa / 深度研究用于个人和公司信息丰富
  • brand-voice 在草拟外联消息之前

备用

  • 浏览器控制用于 LinkedIn 分析和草拟
  • 浏览器控制用于 X（当 API 覆盖受限时）
  • 通过桌面自动化使用 Apple Mail 或 Mail.app 草拟（当电子邮件是合适渠道时）

安全默认设置

  • 默认先审查，绝不盲目自动清理
  • X：仅清理用户关注的账户，绝不清理粉丝
  • LinkedIn：将解除 1 度连接视为手动审查优先
  • 不要自动发送私信、邀请或电子邮件
  • 在执行任何“应用”步骤前，生成一个带排名的行动计划及草稿

平台规则

X

  • 互关比单向关注更稳固
  • 未回关的账户可以更积极地清理
  • 严重不活跃或已消失的账户应快速呈现
  • 互动、信号质量和桥梁价值比原始粉丝数更重要

LinkedIn

  • 如果用户确实拥有 LinkedIn API 访问权限，则优先使用 API
  • 当缺少 API 访问权限时，必须使用浏览器工作流程
  • 区分对外关注与已接受的 1 度连接
  • 对外关注可以更自由地清理
  • 已接受的 1 度连接应默认审查，而非自动移除

模式

light-pass

  • 仅清理高确信度的低价值单向关注
  • 其余部分呈现供审查
  • 生成少量添加/关注列表

default

  • 平衡的清除队列
  • 平衡的保留列表
  • 带排名的添加/关注队列
  • 在有用时草拟温暖介绍或直接外联

aggressive

  • 更大的清除队列
  • 对僵化未回关的容忍度更低
  • 仍以审查为前提，之后才应用

评分模型

使用这些正面信号：

  • 互惠性
  • 近期活跃度
  • 与当前优先级对齐
  • 网络桥梁价值
  • 角色相关性
  • 真实的互动历史
  • 近期存在及响应性

使用这些负面信号：

  • 已消失或废弃的账户
  • 僵化的单向关注
  • 偏离优先级的主题聚类
  • 低价值噪音
  • 多次无响应
  • 未回关，且有许多更好的替换人选

互关和真实的温暖路径桥梁应比单向关注更少受到积极惩罚。

工作流程

  1. 获取优先级、免动约束和所选平台。
  2. 拉取当前关注/连接清单。
  3. 对清除候选者评分，并附上明确理由。
  4. 对保留候选者评分，并附上明确理由。
  5. 使用 lead-intelligence 加研究资源来对扩展候选者进行排名。
  6. 匹配合适的渠道：
    • X DM 用于温暖、快速的社交接触点
    • LinkedIn 消息用于专业图谱邻近性
    • Apple Mail 草稿用于更高上下文的介绍或外联
  7. 在草拟消息前运行 brand-voice。
  8. 在任何“应用”步骤前返回一个审查包。

审查包格式

CONNECTIONS OPTIMIZER 报告
============================

模式：
平台：
优先级集：

清除队列
- 用户名 / 个人资料链接
  理由：
  置信度：
  操作：

审查队列
- 用户名 / 个人资料链接
  理由：
  风险：

保留 / 保护
- 用户名 / 个人资料链接
  桥梁价值：

添加 / 关注目标
- 人员
  为什么现在：
  温暖路径：
  首选渠道：

草稿
- X DM：
- LinkedIn：
- Apple Mail：

外联规则

  • 默认电子邮件路径是创建 Apple Mail / Mail.app 草稿。
  • 不要自动发送。
  • 根据温暖度、相关性和上下文深度选择渠道。
  • 当电子邮件或不进行外联是正确选择时，不要强行使用私信。
  • 草稿应听起来像用户本人，而非自动化的销售文案。

相关技能

  • brand-voice 用于可复用的声音档案
  • social-graph-ranker 用于独立的桥梁评分和温暖路径计算
  • lead-intelligence 用于加权目标和温暖路径发现
  • x-api 用于 X 图谱访问、草拟和可选的应用流程
  • content-engine 当用户还希望围绕网络行动发布公开内容时

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/claude-api

Anthropic Claude API patterns for Python and TypeScript. Covers Messages API, streaming, tool use, vision, extended thinking, batches, prompt caching, and Claude Agent SDK. Use when building applications with the Claude API or Anthropic SDKs.

affaan-m/everything-claude-code

Development conventions and patterns for everything-claude-code. JavaScript project with conventional commits.

affaan-m/everything-claude-code

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. Use when planning or executing a multi-channel product launch, or producing landing page, email, social, or ad copy.

affaan-m/everything-claude-code-conventions

Development conventions and patterns for everything-claude-code. JavaScript project with conventional commits.

affaan-m/frontend-design

Create distinctive, production-grade frontend interfaces with high design quality. Use when the user asks to build web components, pages, or applications and the visual direction matters as much as the code quality.

affaan-m/gget

gget CLI and Python workflow for quick genomic database queries, sequence lookup, BLAST-style searches, enrichment checks, and reproducible bioinformatics evidence logs.

affaan-m/literature-review

Systematic literature-review workflow for academic, biomedical, technical, and scientific topics, including search planning, source screening, synthesis, citation checks, and evidence logging.

affaan-m/motion-ui

Production-ready UI motion system for React/Next.js. Use when implementing animations, transitions, or motion patterns.

affaan-m/project-guidelines-example

Example project-specific skill template based on a real production application.

affaan-m/pubmed-database

Direct PubMed and NCBI E-utilities search workflows for biomedical literature, MeSH queries, PMID lookup, citation retrieval, and API-backed literature monitoring.

affaan-m/scholar-evaluation

Structured scholarly-work evaluation for papers, proposals, literature reviews, methods sections, evidence quality, citation support, and research-writing feedback.

affaan-m/uspto-database

USPTO patent and trademark data workflow for official record lookup, PatentSearch queries, TSDR checks, assignment data, and reproducible IP research logs.

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.

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