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Data Metrics Understanding

Community
vivy-yi
data-metrics-understanding

Use when analyzing Xiaohongshu account data, interpreting performance metrics, making data-driven content decisions, or explaining what each metric means for account growth

Overview

Publishervivy-yi
Repositoryxiaohongshu-skills
Skill namedata-metrics-understanding
Stars
502
Forks
76
Bundled files
Instructions only
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by vivy-yi on GitHub. Read the source before you install it.

Installation

Install the Data Metrics Understanding AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/vivy-yi/xiaohongshu-skills/tree/main/skills/03-数据分析/data-metrics-understanding
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Use it in TypingMind

Enable Data Metrics Understanding in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Data Metrics Understanding on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Data Metrics Understanding is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Data Metrics Understanding (数据指标理解)

Overview

Data metrics understanding is knowing what each Xiaohongshu performance metric means, how to calculate it, what benchmarks indicate good vs. bad performance, and how to use metrics to optimize content strategy.

When to Use

Use when:

  • Analyzing account performance
  • Interpreting content data
  • Setting performance goals
  • Diagnosing growth issues
  • Creating performance reports

Do NOT use when:

  • Needing data export tools (use qiangua-data)
  • Analyzing competitor data (use competitor-analysis)

Core Pattern

Before (metrics confusion):

❌ "What does engagement rate mean?"
❌ "Is 5% good or bad?"
❌ Don't know which metrics matter
❌ Overwhelmed by data dashboard

After (metrics clarity):

✅ Know what each metric measures
✅ Understand good vs. bad benchmarks
✅ Focus on metrics that matter
✅ Use data to drive decisions

7 Core Metrics:

  1. Views/Exposure - Content reach
  2. Engagement Rate - (Likes+Comments+Shares)/Views
  3. Save Rate - Saves/Views
  4. Follower Growth - New followers per post
  5. Completion Rate - Video/audio watched to end
  6. Click-Through Rate - Profile visits from content
  7. Share Rate - Shares/Views

Quick Reference

MetricFormulaGood BenchmarkWhat It Indicates
ViewsTotal impressions500+ for new accountsReach and discovery
Engagement Rate(L+C+S)/Views8-12%Content resonance
Save RateSaves/Views3-5%Content value
Follower GrowthNew followers/post5-10+ per 1K viewsConversion effectiveness
Completion RateWatched to end70%+ for videosContent engagement
Share RateShares/Views1-3%Shareability

Implementation

Step 1: Track Essential Metrics

Weekly tracking minimum:

  • Total views per post
  • Likes, comments, shares per post
  • New followers gained
  • Follower loss (unfollows)

Tools:

  • Xiaohongshu Creator Center (free)
  • Export to Excel for analysis

Step 2: Calculate Engagement Rate

Formula:

Engagement Rate = (Likes + Comments + Shares) / Views

Example:
Views: 1,000
Likes: 100
Comments: 20
Shares: 10
Engagement Rate = (100+20+10)/1000 = 13%

Benchmark:

  • Below 5%: Needs improvement
  • 5-10%: Average/good
  • Above 10%: Excellent

Step 3: Analyze Save Rate

Saves = content value indicator

Formula: Save Rate = Saves / Views

Benchmark:

  • 1-3%: Average
  • 3-5%: Good (content worth saving)
  • 5%+: Excellent (highly valuable content)

Use for: Tutorial, guide, educational content optimization.

Step 4: Monitor Follower Growth

Per-post growth:

Benchmark (per 1K views):

  • 0-2 followers: Poor conversion
  • 3-7 followers: Average
  • 8-15 followers: Good
  • 15+ followers: Excellent

Goal: Increase growth rate over time.

Step 5: Compare to Benchmarks

Industry averages (varies by niche):

  • Fashion/beauty: 10-15% engagement
  • Food/lifestyle: 8-12% engagement
  • Education/tips: 12-18% engagement

Adjust for: Account size, niche, content type.

Step 6: Identify Problem Metrics

Red flags:

  • Engagement rate dropping over time
  • Views high but saves low (not valuable)
  • Follower loss > gain (churning audience)
  • Completion rate <50% (content not engaging)

Take action: Use specific skills to address issues.

Common Mistakes

MistakeFix
Focusing only on viewsEngagement rate matters more
Ignoring save rateSaves = content value
Comparing to mega-accountsUse similar-sized accounts
Not tracking over timeTrends matter more than snapshots
Analysis paralysisFocus on 3-5 key metrics

Real-World Impact

Data-driven accounts: 3-5x faster growth Ignoring metrics: Stagnation, don't know what works


Related Skills:

  • REQUIRED: data-analytics (complete analysis methodology)
  • REQUIRED: qiangua-data (advanced metrics tools)
  • content-performance-analysis (individual post analysis)
  • traffic-analysis (where views come from)

Frequently asked questions

What does the Data Metrics Understanding AI skill do?

Use when analyzing Xiaohongshu account data, interpreting performance metrics, making data-driven content decisions, or explaining what each metric means for account growth

Why use Data Metrics Understanding on TypingMind?

Because you install it once and use it with any model. Data Metrics Understanding is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Data Metrics Understanding in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/vivy-yi/xiaohongshu-skills/tree/main/skills/03-数据分析/data-metrics-understanding. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Data Metrics Understanding?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Data Metrics Understanding?

As many as you like. As long as a model supports skills, you can use Data Metrics Understanding with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Data Metrics Understanding AI skill free?

It is published on GitHub by vivy-yi. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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