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shiker1996
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seo-keyword-scoring

Compares Chinese keywords using Baidu and 360 search suggestion signals.

5|1|Updated Jul 31, 2026
Search Tags:#keyword research#scoring#seo#wechat#baidu#search suggestions#chinese keywords
One-click install
npx skills add https://github.com/shiker1996/wechat-editroom --skill seo-keyword-scoring-shiker1996
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: seo-keyword-scoring
Source: https://github.com/shiker1996/wechat-editroom/tree/main/skills/seo-keyword-scoring
Command: npx skills add https://github.com/shiker1996/wechat-editroom --skill seo-keyword-scoring-shiker1996

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve? Choosing between candidate Chinese keywords for SEO or WeChat article titles is often guesswork. This Skill provides a reproducible, relative signal by querying public search suggestion endpoints from Baidu and 360, so you can compare keywords and expand related terms before committing to a title. ## Core Features & Use Cases - Relative Keyword Scoring: Computes a 0-10 seo_score from the mean of capped suggestion counts across available sources, with per-source status reporting. - Related Keyword Expansion: Aggregates and deduplicates up to 10 related terms from both suggestion sources. - Honest Failure Handling: When both sources fail, the score is null and the conclusion must state data is unavailable rather than reporting zero heat. - Use Case: Before publishing a WeChat article, compare three candidate title keywords, pick the one with the strongest suggestion signal, and record the reasoning in 07-seo-keywords.md. ## Quick Start Run the seo keyword scoring script on my candidate title keywords and tell me which one has the strongest search suggestion signal.

Frequently Asked Questions about seo-keyword-scoring

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I compare Chinese keywords for SEO using search suggestions?▼

Run the bundled Python script with one to three keywords as arguments and the --json flag. It queries Baidu and 360 suggestion endpoints, averages the capped suggestion counts into a 0-10 score, and returns related keywords for each candidate.

What data sources does this keyword scoring method use?▼

It uses two public search suggestion APIs: Baidu's suggestion endpoint and 360's suggest endpoint. Each keyword is queried against both, and the score is the mean of available sources, capped at 10 suggestions per source.

Does the seo_score represent real search volume or WeChat traffic?▼

No. The score is a relative signal derived from suggestion counts only. It is not search volume, WeChat index data, or a ranking guarantee, and should only be used to compare candidates within the same run.

What happens when Baidu or 360 suggestion requests fail?▼

Failed sources are marked unavailable and excluded from the average rather than counted as zero. If both sources fail, the score is null and the output must state the data is unavailable instead of reporting zero heat.

What are the limitations of search suggestion based keyword scoring?▼

Suggestion counts reflect autocomplete behavior, not actual demand, and cannot prove keyword performance in WeChat search. Results are only comparable within the same batch and should be combined with editorial judgment and fact checking.

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