Agent Skills › gooseworks-ai/goose-skills › improve-search-visibility-question-set

improve-search-visibility-question-set

GitHub

该技能用于起草和评分买家问题集，将其分组为页面簇。它结合搜索意图与可赢性对问题进行打分筛选，利用搜索控制台数据计算权重，并生成结构化的问题清单文件，以优化搜索可见性策略中的内容映射。

skills/marketing/improve-search-visibility-question-set/SKILL.md gooseworks-ai/goose-skills

触发场景

需要优化网站搜索可见性构建基于用户问题的页面地图整理买家常见问题集

安装

npx skills add gooseworks-ai/goose-skills --skill improve-search-visibility-question-set -g -y

更多选项

非标准路径

npx skills add https://github.com/gooseworks-ai/goose-skills/tree/main/skills/marketing/improve-search-visibility-question-set -g -y

不安装直接使用

npx skills use gooseworks-ai/goose-skills@improve-search-visibility-question-set

指定 Agent (Claude Code)

npx skills add gooseworks-ai/goose-skills --skill improve-search-visibility-question-set -a claude-code -g -y

安装 repo 全部 skill

npx skills add gooseworks-ai/goose-skills --all -g -y

预览 repo 内 skill

npx skills add gooseworks-ai/goose-skills --list

SKILL.md

Frontmatter

{
    "name": "improve-search-visibility-question-set",
    "version": "0.1.1",
    "description": "Step of the improve-search-visibility playbook: drafts and scores the buyer-question set in AI and Google forms and groups it into page clusters. Run improve-search-visibility instead; not for direct use."
}

question-set

Reads: ../improve-search-visibility/references/question-list.md, ../improve-search-visibility/references/battlegrounds.md; brand summary, products, audience, competitors (gooseworks:brand_read, gooseworks:competitor_read); research-audience's research/audience/voc-bank.md and research-market's research/market/demand.md if present; customer language via gooseworks:knowledge_search with source_types: ["document", "evidence"] (query "Voice-of-customer bank — customer questions, objections, words customers use"); Search Console export if shared; the previous search/questions.md and search/page-map.md on re-runs.

Does: 0. Cover every audience the site sells to. If the site sells to more than one buyer or carries more than one positioning, include questions for each, so the decisions message can ask which buyer to win.

Re-run: reuse the saved set, then top it up to the mix — at least 3 brand-accuracy questions and every audience the site sells to — before anything runs; propose at most 3 other additions or swaps with reasons; keep ids stable — the same id always means the same words: a reworded question gets a new id and the old one is marked retired (reworded → Q<n>, v<N>), so the next run never compares different wordings.
First run: draft 18–20 candidates across the stage mix in both forms, never with the brand name in non-brand questions; 3–6 brand-accuracy questions always; local variants per city for physical businesses.
Score value (buying intent 1–5, ../improve-search-visibility/references/question-list.md) × winnability (1–3) provisionally with a one-line reason each; drop to the best 12–15. A brand name that is a common word always carries the category in brand questions. Pre-launch: category questions plus one name-collision question only.
Cluster and weight. Group the questions into clusters (one cluster = one page that should answer them); with a Search Console export, sum each cluster's impressions as its demand weight and add any high-impression query the list missed. These clusters are the page map's rows.
Pick the key questions yourself (the 3–5 highest value × winnability) and run the set — no approval before the free pass. The first message shows the set grouped by cluster for edits; edits apply to the paid top-up and the next runs.

Writes: search/questions.md in the saved-set format (version, date, country, language, engines, runs·route line, the table with a cluster column). Never: invents search volumes or "how many people ask this". Demand comes from Search Console, Google Trends or autocomplete (free), Keyword Planner when the brand shares it, or a vendor prompt-volume estimate the brand already has — always labelled with its source, estimates as estimates; otherwise "no demand data, ranked by judgment".