Timing Analysis

Community

vivy-yi

timing-analysis

Use when analyzing optimal posting times on Xiaohongshu, studying audience activity patterns, determining when followers are most active, scheduling content for maximum reach, or measuring time-based performance

Overview

Publisher	vivy-yi
Repository	`xiaohongshu-skills`
Skill name	`timing-analysis`
Stars	502
Forks	76
Bundled files	Instructions only
Links	Skill source Repository

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 Timing Analysis AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/vivy-yi/xiaohongshu-skills/tree/main/skills/03-数据分析/timing-analysis
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/vivy-yi/xiaohongshu-skills/tree/main/skills/03-数据分析/timing-analysis

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Use it in TypingMind

Enable Timing 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 Timing 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 Timing 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.

Timing Analysis (发布时机分析)

Overview

Timing analysis is the data-driven study of when Xiaohongshu audiences are most active and receptive to content, enabling strategic scheduling that maximizes reach, engagement, and conversion.

When to Use

Determining best times to post
Analyzing audience activity patterns
Scheduling content for optimal reach
Measuring time-based engagement
Testing different posting times
Optimizing content calendar timing
Understanding audience behavior

Core Pattern

Before: Post when convenient, inconsistent timing, missed opportunities After: Data-driven timing, peak engagement, strategic scheduling

3 Timing Dimensions:

Time of Day (morning, afternoon, evening)
Day of Week (weekdays vs weekends)
Seasonality (monthly, quarterly patterns)

Quick Reference

Time Slot	Engagement	Reach	Competition	Best Content Type
Morning (7-9 AM)	Medium	Medium	Low	Educational, tips
Lunch (12-1 PM)	High	High	Medium	Entertainment, light
Evening (7-9 PM)	Very High	Very High	High	All content types
Late Night (9-11 PM)	Medium	Medium	Low	Community, engagement

Implementation

Step 1: Analyze Audience Activity Patterns

Activity Tracking:

When followers are online
Peak engagement hours
Comment activity timing
Save and share timing
Live stream attendance

Tools:

Xiaohongshu analytics (when followers online)
Content performance by post time
Engagement rate by hour/day
Historical performance data

Step 2: Test Posting Times

A/B Testing Framework:

Test morning vs evening
Test weekday vs weekend
Test different days of week
Test same content at different times

Testing Variables:

Post time (primary variable)
Content type (keep consistent)
Day of week (test systematically)
Duration (run tests 2-4 weeks)

Step 3: Measure Time-Based Performance

Metrics by Time Slot:

Reach (impressions)
Engagement rate
Follower growth
Save rate
Share rate
Comment quality

Statistical Significance:

Test each time slot 5+ times
Calculate average performance
Identify outliers
Determine statistical winner

Step 4: Develop Optimal Timing Strategy

Optimal Schedule:

Primary posting times (best performance)
Secondary times (good performance)
Avoid times (consistently low performance)

Content Type Timing:

Educational: Morning/commute hours
Entertainment: Lunch/evening
Community building: Evening
Promotional: Evening/weekends
Live streams: Evenings/weekends

Step 5: Adapt to Seasonality

Seasonal Patterns:

Holiday behavior shifts
Season changes affect activity
Events and trends create timing opportunities
Back-to-school periods
Holiday shopping seasons

Real-Time Adaptation:

Monitor trending topics
Adjust for breaking news
Leverage cultural moments
Respond to audience activity shifts

Real-World Impact

Timing Optimization Results:

Engagement +35% from optimal timing
Reach +50% from strategic scheduling
Follower growth +25% from consistent timing
Saved time from efficient scheduling

Related Skills

REQUIRED: Use data-analytics (measure timing performance) REQUIRED: Use content-calendar (schedule optimized times)

Recommended:

audience-analysis, content-optimization, social-listening

Frequently asked questions

What does the Timing Analysis AI skill do?

Why use Timing Analysis on TypingMind?

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

Which AI models can use Timing 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 Timing Analysis?

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

Is the Timing 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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Overview

Installation

Install in TypingMind

Use it in TypingMind

Works with any AI model

Skill instructions

Timing Analysis (发布时机分析)

Overview

When to Use

Core Pattern

Quick Reference

Implementation

Step 1: Analyze Audience Activity Patterns

Step 2: Test Posting Times

Step 3: Measure Time-Based Performance

Step 4: Develop Optimal Timing Strategy

Step 5: Adapt to Seasonality

Real-World Impact

Related Skills

Frequently asked questions

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