analytics-expert

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Chinese

Content Analytics Expert

内容分析专家

Overview

概述

This skill enables Claude to analyze content analytics data, generate comprehensive reports, identify performance trends, calculate ROI and revenue attribution, and provide actionable insights for content optimization.

此技能使Claude能够分析内容分析数据、生成全面报告、识别绩效趋势、计算ROI和收入归因，并为内容优化提供可操作的见解。

When to Use This Skill

何时使用此技能

This skill activates automatically when users:

Ask analytics questions or request performance reports
Need help analyzing content performance data
Want ROI calculations or revenue attribution analysis
Request trend identification from analytics data
Need content optimization recommendations based on data
Want to understand which content performs best and why

当用户有以下需求时，该技能会自动激活：

提出分析相关问题或请求生成绩效报告
需要帮助分析内容绩效数据
想要进行ROI计算或收入归因分析
请求从分析数据中识别趋势
需要基于数据的内容优化建议
想要了解哪些内容表现最佳及其原因

Core Capabilities

核心能力

1. Generate Analytics Reports

1. 生成分析报告

To generate comprehensive analytics reports:

Collect Analytics Data
- Access analytics platform data (discover from project)
- Aggregate performance metrics across platforms
- Gather engagement data (views, likes, comments, shares)
- Collect conversion and revenue data (if available)
Create Report Structure
- Weekly/Monthly performance reports
- Platform-specific performance analysis
- Content type performance comparison
- Audience engagement reports
- ROI and revenue attribution reports
Generate Report Content
- Summarize key metrics and insights
- Create data visualizations (charts, graphs)
- Identify top-performing content
- Highlight trends and patterns
- Provide actionable recommendations

Example User Request: "Generate a monthly performance report for my content"

Integration (discover from project):

Analytics Platform: Access performance data
Content Management Platform: Store and share reports
Publishing Platform: Use insights for scheduling optimization

要生成全面的分析报告：

收集分析数据
- 访问分析平台数据（从项目中获取）
- 汇总跨平台的绩效指标
- 收集互动数据（浏览量、点赞、评论、分享）
- 收集转化和收入数据（如有可用）
创建报告结构
- 周/月度绩效报告
- 特定平台的绩效分析
- 内容类型绩效对比
- 受众互动报告
- ROI和收入归因报告
生成报告内容
- 总结关键指标和见解
- 创建数据可视化（图表、图形）
- 识别表现最佳的内容
- 突出趋势和模式
- 提供可操作的建议

用户请求示例： "为我的内容生成月度绩效报告"

集成方式（从项目中获取）：

分析平台：访问绩效数据
内容管理平台：存储和分享报告
发布平台：利用见解优化排期

2. Identify Top-Performing Content Patterns

2. 识别高表现内容模式

To identify patterns in top-performing content:

Analyze Performance Data
- Review content performance metrics
- Identify top-performing content pieces
- Analyze common characteristics of successful content
Extract Patterns
- Content topics and themes
- Content formats and types
- Posting times and frequencies
- Platform-specific patterns
- Engagement drivers (hooks, CTAs, visuals)
Generate Insights
- Document successful content patterns
- Recommend content strategies based on patterns
- Suggest content replication opportunities

Example User Request: "What patterns do you see in my top-performing content?"

Integration (discover from project):

Analytics Platform: Analyze performance data
Content Creation Tools: Apply patterns to new content generation
Content Management Platform: Store pattern insights

要识别高表现内容的模式：

分析绩效数据
- 查看内容绩效指标
- 识别表现最佳的内容条目
- 分析成功内容的共同特征
提取模式
- 内容主题和主题方向
- 内容格式和类型
- 发布时间和频率
- 特定平台的模式
- 互动驱动因素（钩子、CTA、视觉元素）
生成见解
- 记录成功的内容模式
- 基于模式推荐内容策略
- 建议内容复制的机会

用户请求示例： "在我的高表现内容中你能看到哪些模式？"

集成方式（从项目中获取）：

分析平台：分析绩效数据
内容创作工具：将模式应用于新内容生成
内容管理平台：存储模式见解

3. Predict Content Performance

3. 预测内容表现

To predict content performance before publishing:

Analyze Historical Data
- Review similar content performance
- Identify factors that correlate with success
- Build performance prediction models
Evaluate New Content
- Compare new content to historical patterns
- Assess content against success factors
- Calculate predicted performance scores
Provide Recommendations
- Suggest content improvements
- Recommend optimal posting times
- Identify best platforms for content
- Predict viral potential

Example User Request: "Predict how well this content will perform before I publish it"

Integration (discover from project):

Analytics Platform: Use historical data for predictions
Content Creation Tools: Optimize content before generation
Publishing Platform: Optimize scheduling based on predictions

要在发布前预测内容表现：

分析历史数据
- 查看类似内容的表现
- 识别与成功相关的因素
- 构建绩效预测模型
评估新内容
- 将新内容与历史模式进行对比
- 根据成功因素评估内容
- 计算预测绩效分数
提供建议
- 建议内容改进方向
- 推荐最佳发布时间
- 识别适合内容的最佳平台
- 预测传播潜力

用户请求示例： "在我发布前预测一下这个内容的表现"

集成方式（从项目中获取）：

分析平台：使用历史数据进行预测
内容创作工具：在生成前优化内容
发布平台：基于预测优化排期

4. ROI Analysis and Attribution

4. ROI分析与归因

To calculate ROI and revenue attribution:

Track Revenue Metrics
- Link content to conversions and revenue
- Track attribution through project's tracking links (discover format from project docs)
- Calculate cost per content piece (API costs, time)
Calculate ROI
- Revenue per content piece
- Cost to create content
- ROI percentage calculation
- Revenue per platform/channel
Generate ROI Reports
- Content-level ROI analysis
- Platform ROI comparison
- Campaign ROI tracking
- Revenue optimization recommendations

Example User Request: "Calculate the ROI for my content and show me which pieces drive the most revenue"

Integration (discover from project):

Analytics Platform: Track conversions and revenue
Content Management Platform: Store ROI data and reports
Publishing Platform: Optimize distribution based on ROI

要计算ROI和收入归因：

跟踪收入指标
- 将内容与转化和收入关联
- 通过项目的跟踪链接跟踪归因（从项目文档中获取格式）
- 计算每条内容的成本（API成本、时间成本）
计算ROI
- 每条内容的收入
- 内容创作成本
- ROI百分比计算
- 各平台/渠道的收入
生成ROI报告
- 内容层面的ROI分析
- 平台ROI对比
- 营销活动ROI跟踪
- 收入优化建议

用户请求示例： "计算我的内容的ROI，并告诉我哪些内容带来最多收入"

集成方式（从项目中获取）：

分析平台：跟踪转化和收入
内容管理平台：存储ROI数据和报告
发布平台：基于ROI优化分发

5. Trend Identification

5. 趋势识别

To identify trends from analytics data:

Analyze Time-Series Data
- Review performance trends over time
- Identify growth or decline patterns
- Detect seasonal trends
Identify Emerging Trends
- Content topics gaining traction
- Platform trends and shifts
- Audience behavior changes
- Engagement pattern shifts
Provide Trend Insights
- Document identified trends
- Recommend actions based on trends
- Predict future trend directions

Example User Request: "What trends do you see in my content performance over the last 3 months?"

Integration (discover from project):

Analytics Platform: Analyze time-series data
Content Management Platform: Store trend insights
Content Creation Tools: Apply trends to content generation

要从分析数据中识别趋势：

分析时间序列数据
- 查看一段时间内的绩效趋势
- 识别增长或下降模式
- 检测季节性趋势
识别新兴趋势
- 受到关注的内容主题
- 平台趋势和变化
- 受众行为变化
- 互动模式转变
提供趋势见解
- 记录已识别的趋势
- 基于趋势推荐行动方案
- 预测未来趋势方向

用户请求示例： "在过去3个月里，我的内容表现有哪些趋势？"

集成方式（从项目中获取）：

分析平台：分析时间序列数据
内容管理平台：存储趋势见解
内容创作工具：将趋势应用于内容生成

Project Context Discovery

项目上下文获取

Before analyzing analytics, discover the project's context:

Scan Project Documentation:
- Check
```
.agents/SYSTEM/ARCHITECTURE.md
```
  for analytics platform details
- Review
```
.agents/SYSTEM/SUMMARY.md
```
  for analytics capabilities
- Look for analytics-related documentation
Identify Analytics Platform:
- Check for analytics service integrations in codebase
- Look for analytics API endpoints or SDKs
- Review environment variables for analytics services
Discover Available Metrics:
- Review analytics API documentation if available
- Check for analytics data models or schemas
- Identify what metrics the project tracks

Common Analytics Data Types (adapt based on discovery):

Post-level metrics: Views, Likes, Comments, Shares, Engagement Rate
Platform-specific metrics: Performance by platform
Time-based metrics: Performance over time (7d, 30d, 90d)
Conversion metrics: Clicks, signups, revenue (via tracking links)
Content type metrics: Performance by content type

Key Metrics:

Engagement Rate: (Likes + Comments + Shares) / Views
ROI: (Revenue - Cost) / Cost × 100
Conversion Rate: Conversions / Clicks
Average Performance: Aggregate metrics across content

在进行分析之前，先获取项目的上下文：

扫描项目文档：
- 查看
```
.agents/SYSTEM/ARCHITECTURE.md
```
  获取分析平台详情
- 查看
```
.agents/SYSTEM/SUMMARY.md
```
  获取分析能力相关信息
- 查找与分析相关的文档
识别分析平台：
- 检查代码库中的分析服务集成
- 查找分析API端点或SDK
- 查看环境变量中的分析服务信息
发现可用指标：
- 如有可用，查看分析API文档
- 查找分析数据模型或模式
- 确定项目跟踪的指标

常见分析数据类型（根据获取的信息调整）：

帖子层面指标：浏览量、点赞、评论、分享、互动率
特定平台指标：各平台的表现
时间维度指标：不同时间段的表现（7天、30天、90天）
转化指标：点击、注册、收入（通过跟踪链接）
内容类型指标：不同内容类型的表现

关键指标：

互动率：(点赞 + 评论 + 分享) / 浏览量
ROI：(收入 - 成本) / 成本 × 100
转化率：转化数 / 点击数
平均表现：跨内容的汇总指标

Best Practices

最佳实践

Data-Driven Insights: Base all recommendations on actual analytics data
Context Matters: Consider platform, timing, and audience when analyzing data
Actionable Recommendations: Provide specific, actionable insights, not just data
Comparative Analysis: Compare performance against benchmarks and historical data
Continuous Monitoring: Recommend regular analytics review and optimization

数据驱动的见解：所有建议均基于实际分析数据
上下文至关重要：分析数据时考虑平台、时间和受众因素
可操作的建议：提供具体、可执行的见解，而非仅数据
对比分析：将表现与基准和历史数据进行对比
持续监控：建议定期审查分析数据并进行优化

Resources

资源

references/

```
analytics-api-reference.md
```
: Project analytics API endpoints and data structures (discover from project docs)
```
roi-calculation-guide.md
```
: ROI calculation methods and formulas
```
performance-benchmarks.md
```
: Industry benchmarks for content performance

```
analytics-api-reference.md
```
：项目分析API端点和数据结构（从项目文档中获取）
```
roi-calculation-guide.md
```
：ROI计算方法和公式
```
performance-benchmarks.md
```
：内容表现的行业基准

assets/

```
analytics-report-template.md
```
: Template for analytics reports
```
roi-report-template.md
```
: Template for ROI analysis reports
```
trend-analysis-template.md
```
: Template for trend identification reports

```
analytics-report-template.md
```
：分析报告模板
```
roi-report-template.md
```
：ROI分析报告模板
```
trend-analysis-template.md
```
：趋势识别报告模板

Complementary Skills (External)

互补技能（外部）

For A/B testing and analytics tracking, pair with coreyhaines31/marketingskills:

/plugin marketplace add coreyhaines31/marketingskills

Skill	Why
`analytics-tracking`	Tracking setup and event configuration
`ab-test-setup`	A/B test design and implementation

如需进行A/B测试和分析跟踪，可搭配coreyhaines31/marketingskills使用：

/plugin marketplace add coreyhaines31/marketingskills

技能	原因
`analytics-tracking`	跟踪设置和事件配置
`ab-test-setup`	A/B测试设计与实施