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Agent skill

Social Graph Ranker

by affaan-m in affaan-m/ECC

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。

MITAuto-check passed

Install Social Graph Ranker

skills CLI
$ npx skills add affaan-m/ECC --skill social-graph-ranker -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install affaan-m/ECC social-graph-ranker --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/affaan-m/ECC.git skills-src && mkdir -p .claude/skills && cp -r skills-src/docs/zh-CN/skills/social-graph-ranker .claude/skills/social-graph-ranker && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
social-graph-ranker
GitHub stars
276k
Token cost
~484 tokens
SKILL.md length
122 words
Files
1
Skills in repo
683
Repo updated
First seen
Licence
MIT

At a glance

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。

  • Works in 6 steps: 构建加权目标集。 → 从X、LinkedIn或两者拉取用户的图谱。 → 计算直接桥梁分数。 → …
  • SKILL.md covers 何时独立使用, 输入, 核心模型 and 评分信号, plus 3 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Social Graph Ranker is an agent skill from affaan-m/ECC. 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。

Its SKILL.md is about 480 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It works with LinkedIn. The repository describes itself as: The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond. The licence is MIT.

Example prompts

  • “/social-graph-ranker”

Workflow steps

6 steps, taken from the first numbered list in SKILL.md.

  1. 构建加权目标集。
  2. 从X、LinkedIn或两者拉取用户的图谱。
  3. 计算直接桥梁分数。
  4. 为最高价值的互关扩展二度候选者。
  5. 按 R(m) 排名。
  6. 返回

What it can do on your machine

Read from SKILL.md and the folder at commit 4eb71d9. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Social Graph Ranker loads about 484 tokens when it runs. Until then it costs about 26 tokens; SKILL.md has 122 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~26
When it runs · the whole SKILL.md, loaded when a task matches
~484

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from affaan-m/ECC at commit 4eb71d9, republished under its MIT licence (© affaan-m). 122 words, ~484 tokens.

Download SKILL.mdSave it as .claude/skills/social-graph-ranker/SKILL.md (or your agent's skills folder).
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

社交图谱排名器

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：

  • 根据内在价值对现有互关或联系人进行排名
  • 为目标列表绘制温暖路径
  • 衡量跨一度和二度连接的桥梁价值
  • 决定哪些目标适合温暖引荐而非直接冷启动外联
  • 独立于 lead-intelligence 或 connections-optimizer 理解图谱数学原理

何时独立使用

当用户主要需要排名引擎时选择此技能：

  • "我的网络中谁最适合引荐我？"
  • "对我的互关进行排名，看谁能帮我联系到这些人"
  • "针对此ICP映射我的图谱"
  • "展示桥梁数学计算"

当用户真正需要以下功能时，请勿单独使用此技能：

  • 完整的潜在客户生成和外联序列 -> 使用 lead-intelligence
  • 修剪、重新平衡和扩展网络 -> 使用 connections-optimizer

输入

收集或推断：

  • 目标人物、公司或ICP定义
  • 用户在X、LinkedIn或两者上的当前图谱
  • 权重优先级，如角色、行业、地理位置和响应性
  • 遍历深度和衰减容忍度

核心模型

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：

text
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
  • 直接路径贡献全部价值
  • 每增加一跳，贡献减半

二度扩展：

text
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：

text
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：

  • 第一梯队：高 R(m) 和直接桥梁路径 -> 温暖引荐请求
  • 第二梯队：中等 R(m) 和一跳桥梁路径 -> 条件性引荐请求
  • 第三梯队：低 R(m) 或无可行桥梁 -> 直接外联或关注缺口填补

评分信号

在图遍历前根据当前优先级集对目标进行加权：

  • 角色或职位匹配度
  • 公司或行业契合度
  • 当前活跃度和时效性
  • 地理相关性
  • 影响力或覆盖范围
  • 响应可能性

在遍历后对互关进行加权：

  • 进入目标集的加权路径数量
  • 这些路径的直接性
  • 响应性或过往互动历史
  • 进行引荐的上下文契合度

工作流程

  1. 构建加权目标集。
  2. 从X、LinkedIn或两者拉取用户的图谱。
  3. 计算直接桥梁分数。
  4. 为最高价值的互关扩展二度候选者。
  5. 按 R(m) 排名。
  6. 返回：
    • 最佳温暖引荐请求
    • 条件性桥梁路径
    • 不存在温暖路径的图谱缺口

输出格式

text
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

相关技能

  • 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

© affaan-m, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in docs/zh-CN/skills/social-graph-ranker of affaan-m/ECC.

Open the folder on GitHubat commit 4eb71d9

Compare with similar skills

Social Graph Ranker next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Social Graph Ranker compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Social Graph Ranker this skillaffaan-m/ECC276k—~484Automated safety check: PassMIT
Socialcoreyhaines31/marketingskills54k4 repos~4.5kAutomated safety check: PassMIT
Banner Design Systemnextlevelbuilder/ui-ux-pro-max-skill135k1 repos~1.8kAutomated safety check: PassMIT
Agent ReachPanniantong/Agent-Reach95k—~1.4kAutomated safety check: PassMIT
Ad CreativeLeoYeAI/openclaw-marketing-skills1k8 repos~3.4kAutomated safety check: PassCustom licence
Social Contentfreekmurze/dotfiles1k23 repos~2.1kAutomated safety check: PassNone

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Works with

Questions about Social Graph Ranker

What does Social Graph Ranker do?

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。. Social Graph Ranker is an agent skill from affaan-m/ECC.

How do I install Social Graph Ranker in Claude Code?

Run `npx skills add affaan-m/ECC --skill social-graph-ranker -a claude-code`. Or copy the skill folder (docs/zh-CN/skills/social-graph-ranker in affaan-m/ECC) into .claude/skills/social-graph-ranker in your project. Claude Code loads it when a task matches its description.

How do I install Social Graph Ranker in Codex?

Run `npx skills add affaan-m/ECC --skill social-graph-ranker -a codex`. Or copy the skill folder (docs/zh-CN/skills/social-graph-ranker in affaan-m/ECC) into .agents/skills/social-graph-ranker in your project. Codex loads it when a task matches its description.

Can I use Social Graph Ranker in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add affaan-m/ECC --skill social-graph-ranker -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/social-graph-ranker, .gemini/skills/social-graph-ranker, .github/skills/social-graph-ranker and .opencode/skills/social-graph-ranker in your project.

What does Social Graph Ranker need to run?

SKILL.md names no scripts, command-line tools or credentials: Social Graph Ranker is instructions for the agent only.

Does Social Graph Ranker access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Social Graph Ranker safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Social Graph Ranker use?

Social Graph Ranker is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Social Graph Ranker use?

About 484 tokens (SKILL.md is roughly 1.9k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Social Graph Ranker?

Skills that share tags, products or a category with Social Graph Ranker: Social (coreyhaines31/marketingskills, 54k stars), Banner Design System (nextlevelbuilder/ui-ux-pro-max-skill, 135k stars), Agent Reach (Panniantong/Agent-Reach, 95k stars) and Ad Creative (LeoYeAI/openclaw-marketing-skills, 1k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Social Graph Ranker?

affaan-m (a GitHub user) maintains it in affaan-m/ECC, which has 276,111 GitHub stars. The repository holds 683 skills in this directory. The repository was last updated on October 10, 2026.

Source: affaan-m/ECC on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.