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connections-optimizer

重新组织用户的社交网络，将对外连接从单向的潜在客户列表转变为双向的关系优化。

connections-optimizer 是什么？

Connections Optimizer 是一项 AI Agent 技能，旨在帮助用户智能地管理和优化其在 X（Twitter）和 LinkedIn 上的社交网络。与传统的单向开发不同，该技能专注于双向连接的价值，通过分析双向互动、活跃度、主题对齐以及网络桥梁价值来确定保留、修剪或新增连接。它支持三种模式（轻度、默认、激进），并始终遵循“先审查后操作”的安全原则，不会自动发送消息或执行删除操作。该技能优先使用 X API 和 LinkedIn API（如有），否则回退到浏览器自动化。它还能利用“品牌声音”技能生成符合用户真实语气的邮件或私信草稿，并通过一份结构化的审查报告提供完整的行动方案，让用户掌控每一步操作。

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

重新组织用户的网络，而不是将对外连接视为单向的潜在客户列表。

本技能负责：

  • X 关注列表的清理与扩展
  • LinkedIn 关注与连接分析
  • 基于审查的修剪队列
  • 添加和关注建议
  • 暖路径识别
  • 以用户真实口吻生成 Apple Mail、X DM 和 LinkedIn 草稿

何时激活

  • 用户想要修剪其 X 关注列表
  • 用户想要重新平衡关注或连接的对象
  • 用户说“清理我的网络”、“我应该取消关注谁”、“我应该关注谁”、“我应该重新与谁联系”
  • 外联质量取决于网络结构，而不仅仅是冷名单生成

所需输入

收集或推断：

  • 当前的优先事项和活跃工作
  • 目标角色、行业、地理区域或生态系统
  • 平台选择：X、LinkedIn 或两者
  • 不触碰名单
  • 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 图结构和近期活动分析
  • lead-intelligence 用于目标发现和暖路径排序
  • social-graph-ranker 当用户希望独立于更广泛的开发流程评估桥梁价值时
  • Exa / 深度研究用于人员与公司信息丰富
  • 在起草外联消息前使用 brand-voice

回退方案

  • 浏览器控制用于 LinkedIn 分析和起草
  • 浏览器控制用于 X（如果 API 覆盖受限）
  • 通过桌面自动化在 Apple Mail 或 Mail.app 中起草（当电子邮件是合适的渠道时）

安全默认设置

  • 默认是审查优先，绝不盲目自动修剪
  • X：仅修剪用户关注的账号，绝不修剪粉丝
  • LinkedIn：将一级连接的移除操作标记为手动审查优先
  • 不自动发送私信、邀请或邮件
  • 在任何应用步骤之前，输出一个排序后的行动计划和草稿

平台规则

X

  • 互相关注比单向关注更牢固
  • 可以更激进地修剪没有回关的账号
  • 长期不活跃或消失的账号应快速浮现
  • 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，优先使用 API
  • 当 API 访问不可用时，必须能使用浏览器工作流
  • 区分对外关注与已接受的一级连接
  • 对外关注可以更自由地修剪
  • 已接受的一级连接应默认为审查，而非自动移除

模式

light-pass

  • 仅修剪高置信度、低价值的单向关注
  • 其余信息浮现供审查
  • 生成一个小的添加/关注列表

default

  • 平衡的修剪队列
  • 平衡的保留列表
  • 排序后的添加/关注队列
  • 在需要时起草温暖的介绍或直接外联

aggressive

  • 更大的修剪队列
  • 对长期不活跃且未回关的账号容忍度更低
  • 仍然在应用前进行审查

评分模型

使用以下正面信号：

  • 互惠性
  • 近期活动
  • 与当前优先事项的对齐程度
  • 网络桥梁价值
  • 角色相关性
  • 真实的互动历史
  • 近期活跃度和响应性

使用以下负面信号：

  • 已消失或废弃的账号
  • 过时的单向关注
  • 与优先事项无关的话题集群
  • 低价值噪音
  • 反复无回应
  • 当存在许多更好的替代者时仍未回关

互相关注和真实的暖路径桥梁应比单向关注更少地被激进惩罚。

工作流程

  1. 捕获优先事项、不触碰约束和选定的平台。
  2. 拉取当前的关注/连接清单。
  3. 对修剪候选者进行评分，并附上明确理由。
  4. 对保留候选者进行评分，并附上明确理由。
  5. 使用 lead-intelligence 结合研究信息，对扩展候选者进行排序。
  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的联系
    • Apple Mail 草稿用于需要更多上下文的介绍或外联
  7. 在起草消息前运行 brand-voice。
  8. 在任何应用步骤之前返回一份审查包。

审查包格式

连接优化器报告
============================

模式：
平台：
优先级集合：

修剪队列
- 用户名 / 个人资料
  理由：
  置信度：
  操作：

审查队列
- 用户名 / 个人资料
  理由：
  风险：

保留 / 保护
- 用户名 / 个人资料
  桥梁价值：

添加 / 关注目标
- 个人
  为何现在：
  暖路径：
  首选渠道：

草稿
- X DM：
- LinkedIn：
- Apple Mail：

外联规则

  • 默认邮件路径是创建 Apple Mail / Mail.app 草稿。
  • 不自动发送。
  • 根据温度、相关性和上下文深度选择渠道。
  • 当电子邮件或不联系才是正确选择时，不要强行发送 DM。
  • 草稿应听起来像用户本人，而不是自动化的销售文案。

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暖路径计算
  • lead-intelligence 用于加权目标和暖路径发现
  • x-api 用于 X 图结构访问、起草和可选的应用流程
  • content-engine 当用户还希望围绕网络变动发布公开内容时

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/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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