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Agent Skill · 研究检索

attribution-analysis-modeling(归因分析建模)

attribution-analysis-modeling 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

attribution-analysis-modeling 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为需要联网;许可证为 MIT,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 51,212。

总安装 588周安装 24GitHub Stars 183下载 190核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:attribution-analysis-modeling(归因分析建模)
来源仓库:https://github.com/liangdabiao/claude-data-analysis-ultra-main
仓库路径:skills/attribution-analysis-modeling
安装命令:
npx skills add https://github.com/liangdabiao/claude-data-analysis-ultra-main --skill attribution-analysis-modeling
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。不同来源提供的安装方式可能略有差异;本站展示可直接复制的安装命令,安装前请核对来源页面。

skills.shnpx skills
npx skills add https://github.com/liangdabiao/claude-data-analysis-ultra-main --skill attribution-analysis-modeling

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

attribution-analysis-modeling 提供多渠道营销归因建模与效果分析全套解决方案。适用于电商、SaaS 等多触点客户旅程追踪与预算分配优化场景。支持 CSV/JSON/Excel 格式输入,自动解析时间序列路径与转化节点关联关系。

内置线性、首次接触、末次接触及 Shapley 值等多种算法对比输出决策建议。适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

来源仓库内路径:skills/attribution-analysis-modeling

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/liangdabiao/claude-data-analysis-ultra-main --…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要需要联网,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

attribution-analysis-modeling 怎么安装?

在终端执行 npx skills add https://github.com/liangdabiao/claude-data-analysis-ultra-main --skill attr…。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

attribution-analysis-modeling 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

attribution-analysis-modeling 需要什么权限?

权限级别是需要联网。该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

attribution-analysis-modeling 是免费的吗?

来源标注的许可证是 MIT。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

attribution-analysis-modeling 用的人多吗?

公开数据是累计安装 588 次,近一周安装 24 次,下载 190 次,GitHub 183 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

attribution-analysis-modeling 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 3 个目录同时收录,说明这个 Skill 流传比较广。
  • 许可证明确为 MIT,商用和分发边界好判断。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。
  • 需要许可证清楚、并且有第三方扫描结果的团队项目。

不太适合

  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
attribution-analysis-modeling

归因分析建模 · 本页

588183需要联网MIT通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

Marketing Attribution Analysis & Modeling

A comprehensive attribution analysis skill that evaluates marketing channel effectiveness using advanced statistical models, helping optimize marketing spend and understand customer journey patterns.

Instructions

1. Data Loading and Preparation

When users provide marketing touchpoint data:

  • Load and validate channel interaction data
  • Parse customer journey paths and touchpoint sequences
  • Handle different data formats (CSV, JSON, Excel)
  • Support both user-level and session-level attribution analysis
  • Process timestamp data for chronological path analysis

2. Customer Journey Analysis

  • Reconstruct customer journey paths from touchpoint data
  • Calculate path lengths and conversion patterns
  • Identify common conversion paths and bottlenecks
  • Analyze channel sequencing and order effects
  • Support both online and offline channel attribution

3. Attribution Model Implementation

  • Markov Chain Attribution: Build transition probability matrices and calculate removal effects
  • Shapley Value Attribution: Calculate fair channel contributions using game theory
  • First-Touch Attribution: Assign full credit to the first channel in the path
  • Last-Touch Attribution: Assign full credit to the last channel before conversion
  • Linear Attribution: Distribute credit equally across all channels
  • Time-Decay Attribution: Weight channels based on recency
  • Position-Based Attribution: Weight first and last touches more heavily

4. Channel Performance Analysis

  • Calculate conversion rates by channel and channel combinations
  • Compute ROI and cost-per-acquisition (CPA) for each channel
  • Analyze channel synergy and interaction effects
  • Identify underperforming and overperforming channels
  • Generate channel contribution percentages

5. Visualization and Reporting

  • Create attribution weight distribution charts
  • Generate customer journey path visualizations
  • Build channel transition heatmaps and network graphs
  • Produce ROI analysis and budget allocation recommendations
  • Generate comprehensive attribution reports

Usage Examples

Marketing Channel Attribution

Analyze the effectiveness of our marketing channels:
[CSV with columns: user_id, timestamp, channel, conversion_status, conversion_value]

Digital Campaign Attribution

Calculate attribution for our digital marketing campaigns:
[Marketing touchpoint data with campaign, channel, timestamp, and conversion data]

E-commerce Conversion Attribution

Perform attribution analysis for e-commerce customer journeys:
[Customer path data showing touchpoints before purchase]

Budget Optimization

Help optimize our marketing budget based on attribution results:
[Channel performance data with spend and conversion metrics]

Key Features

Advanced Attribution Models

  • Markov Chain Analysis: Probabilistic model for channel transition analysis
  • Shapley Values: Game theory-based fair attribution calculation
  • Custom Models: Flexible framework for custom attribution logic
  • Model Comparison: Compare different attribution models side-by-side

Customer Journey Analysis

  • Path Reconstruction: Automatically build conversion paths from raw data
  • Touchpoint Sequencing: Analyze order and timing effects
  • Conversion Funnels: Identify drop-off points in customer journeys
  • Multi-path Analysis: Handle customers with multiple conversion paths

Channel Performance Metrics

  • Attribution Weights: Calculate each channel's contribution to conversions
  • ROI Analysis: Compute return on investment for each channel
  • Synergy Effects: Measure how channels work together
  • Incremental Impact: Estimate additional value from channel combinations

Business Intelligence

  • Budget Optimization: Recommend optimal budget allocation
  • Channel Recommendations: Suggest best channel combinations
  • Performance Benchmarks: Compare channel performance against baselines
  • Trend Analysis: Track attribution changes over time

File Requirements

Standard Touchpoint Data Format

user_id,timestamp,channel,conversion_status,conversion_value,cost
USER001,2024-01-15T10:30:00Z,paid_search,0,0,50
USER001,2024-01-16T14:20:00Z,social_media,0,0,30
USER001,2024-01-18T09:15:00Z,email,1,1000,10

Required Fields:

  • user_id: Unique customer identifier
  • timestamp: Touchpoint timestamp (ISO format preferred)
  • channel: Marketing channel or touchpoint
  • conversion_status: Binary indicator of conversion (0/1)
  • conversion_value: Monetary value of conversion (optional)
  • cost: Marketing cost for touchpoint (optional, for ROI analysis)

Supported Channel Types:

  • Digital: paid_search, organic_search, social_media, email, display, video
  • Traditional: tv, radio, print, outdoor, direct_mail
  • E-commerce: marketplace, affiliate, referral
  • Custom: Any channel name can be used

Output Files Generated

  • attribution_results.csv: Complete attribution analysis with channel weights
  • channel_performance.csv: Channel metrics including ROI and CPA
  • customer_paths.csv: Reconstructed customer journey paths
  • transition_matrix.csv: Markov chain transition probability matrix
  • attribution_dashboard.png: Comprehensive visualization dashboard
  • attribution_report.md: Detailed analysis report and recommendations

Dependencies

  • Core Analytics: pandas, numpy, scipy
  • Visualization: matplotlib, seaborn, networkx (for path graphs)
  • Statistical Models: scikit-learn (optional, for advanced models)
  • Data Processing: Standard Python libraries for file operations

Attribution Models Explained

Markov Chain Attribution

Uses probability transition matrices to model customer journey behavior:

  • Calculates removal effect of each channel
  • Considers channel transition probabilities
  • Handles complex multi-path customer journeys
  • Provides incremental value assessment

Shapley Value Attribution

Applies cooperative game theory for fair attribution:

  • Calculates marginal contribution of each channel
  • Considers all possible channel combinations
  • Provides theoretically optimal attribution
  • Handles channel interaction effects

Custom Attribution Models

Flexible framework for business-specific attribution:

  • Configurable weighting rules
  • Time-based decay functions
  • Position-based weighting
  • Custom business logic integration

Business Applications

Marketing Budget Optimization

  • Allocate budget based on true channel contribution
  • Identify underutilized high-performing channels
  • Reduce spend on low-impact channels
  • Test new channel opportunities

Campaign Performance Analysis

  • Evaluate multi-channel campaign effectiveness
  • Understand channel synergy effects
  • Optimize campaign sequencing and timing
  • Measure incremental lift from channel combinations

Customer Journey Optimization

  • Identify optimal channel sequences
  • Remove friction points in conversion paths
  • Enhance high-performing channel combinations
  • Personalize channel selection by customer segment

Advanced Features

Real-time Attribution

  • Process streaming touchpoint data
  • Update attribution weights dynamically
  • Provide real-time channel performance insights
  • Support live campaign optimization

Multi-Conversion Analysis

  • Handle multiple conversion types
  • Analyze different conversion values separately
  • Compare attribution across conversion types
  • Optimize for specific conversion goals

Segmentation Analysis

  • Perform attribution by customer segment
  • Compare channel effectiveness across segments
  • Optimize channel mix by segment
  • Personalize marketing strategies

Best Practices

Data Quality

  • Ensure consistent user identification across touchpoints
  • Maintain accurate timestamp data
  • Include cost data for ROI analysis
  • Handle data gaps and missing values appropriately

Model Selection

  • Choose attribution model based on business goals
  • Compare multiple models for validation
  • Consider customer journey complexity
  • Validate results with business stakeholders

Implementation

  • Start with simpler models before advancing to complex ones
  • Test attribution results against known business outcomes
  • Implement gradual changes based on attribution insights
  • Monitor attribution model performance over time

*This skill transforms complex attribution analysis into actionable marketing insights, helping businesses optimize their marketing spend and understand true channel effectiveness.*

平台分布

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3.82%
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安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

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权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。

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