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Topic Analysis

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vivy-yi
topic-analysis

Use when analyzing content topics on Xiaohongshu, identifying trending themes, researching audience interests, evaluating topic performance, or planning content around high-engagement topics

Overview

Publishervivy-yi
Repositoryxiaohongshu-skills
Skill nametopic-analysis
Stars
502
Forks
76
Bundled files
Instructions only
Links
Skill sourceRepository
  • 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 Topic Analysis 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/topic-analysis
  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 Topic Analysis 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 Topic Analysis 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 Topic Analysis 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.

Topic Analysis (话题分析)

Overview

Topic analysis is the systematic examination of content themes, subject matters, and conversation trends on Xiaohongshu to understand what resonates with audiences, identify emerging opportunities, and make data-driven content planning decisions.

When to Use

  • Identifying trending topics and themes
  • Analyzing which topics drive engagement
  • Researching audience interests and preferences
  • Evaluating content topic performance over time
  • Planning content around proven themes
  • Discovering content gaps and opportunities
  • Measuring brand topic alignment

Core Pattern

Before: Random content, guessing topics, inconsistent themes After: Data-driven topics, proven themes, strategic content

5 Topic Dimensions:

  1. Performance Topics (high engagement, proven winners)
  2. Trending Topics (rising popularity, time-sensitive)
  3. Evergreen Topics (consistent performance, reliable)
  4. Niche Topics (underserved, opportunity-rich)
  5. Brand Topics (core to brand identity, strategic)

Quick Reference

Topic TypeEngagementCompetitionLongevityBest For
PerformanceHighVariesVariesCapitalize on success
TrendingSpikeLow-MediumShortTimely content
EvergreenConsistentHighLongSustainable growth
NicheMediumLowMediumDifferentiation
BrandBuildingVariesLongBrand positioning

Implementation

Step 1: Collect Topic Data

Data Sources:

  • Your content performance by topic
  • Competitor topic analysis
  • Platform trending topics
  • Audience questions and requests
  • Search trends and suggestions
  • Seasonal topic patterns

Build Topic Database: Track: Topic name, post count, avg engagement, trend direction, last covered, priority

Step 2: Analyze Topic Performance

Topic Metrics:

  • Engagement rate per topic
  • Reach and impressions
  • Follower growth by topic
  • Save and share rates
  • Comment sentiment by topic
  • Conversion rate by topic

Performance Categories:

  • Star topics (top 10% performers)
  • Strong topics (top 25%)
  • Average topics (middle 50%)
  • Weak topics (bottom 25%)
  • Avoid topics (consistently underperform)

Step 3: Identify Topic Trends

Trend Analysis:

  • Rising topics (momentum increasing)
  • Stable topics (consistent performance)
  • Declining topics (losing interest)
  • Seasonal patterns (predictable cycles)
  • Viral spikes (sudden popularity)

Trend Detection:

  • Week-over-week change
  • Month-over-month change
  • Seasonal comparison (same period last year)
  • Platform trend alignment

Step 4: Map Topics to Content Strategy

Topic Allocation:

  • 40% Star topics (proven winners)
  • 30% Trending topics (timely relevance)
  • 20% Evergreen topics (consistency)
  • 10% Experimental topics (innovation)

Content Calendar Integration:

  • Schedule star topics during peak times
  • Plan trending topics while hot
  • Evergreen topics for consistency
  • Test topics in low-risk time slots

Step 5: Monitor Topic Saturation

Saturation Indicators:

  • Declining engagement on topic
  • Increased competition
  • Audience fatigue (comments like "again?")
  • Diminishing returns over time

Refresh Strategy:

  • New angle on same topic
  • Different format (video vs post)
  • Update with new information
  • Pause over-saturated topics

Real-World Impact

Topic Analysis Results:

  • Content engagement +40% from topic optimization
  • Follower growth +60% from trending topics
  • Saved 50% time on content planning
  • Identified 15 underserved niche topics

Related Skills

REQUIRED: Use data-analytics (quantitative analysis) REQUIRED: Use content-performance-analysis (topic-specific metrics)

Recommended:

  • trend-analysis, audience-research, content-strategy

Frequently asked questions

What does the Topic Analysis AI skill do?

Use when analyzing content topics on Xiaohongshu, identifying trending themes, researching audience interests, evaluating topic performance, or planning content around high-engagement topics

Why use Topic Analysis on TypingMind?

Because you install it once and use it with any model. Topic Analysis 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 Topic Analysis 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/topic-analysis. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Topic Analysis?

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 Topic Analysis?

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

Is the Topic Analysis 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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