Agent Skills
› gooseworks-ai/goose-skills
› comment-mining
comment-mining
GitHub从社交媒体评论中挖掘客户反馈、痛点及购买信号，为增长团队提供基于证据的客户声音研究。
触发场景
分析社交媒体评论以获取客户洞察
挖掘用户 objection 和购买意图
生成基于评论的创意测试建议
安装
npx skills add gooseworks-ai/goose-skills --skill comment-mining -g -y
SKILL.md
Frontmatter
{
"name": "comment-mining",
"description": "Mine comments and replies on social posts, videos, and ads for recurring customer language, questions, objections, desired outcomes, complaints, product requests, purchase signals, and creative opportunities. Use for voice-of-customer research grounded in linked source evidence."
}
Comment Mining
Turn public comment threads into evidence a growth team can use.
Inputs
- Brand/product and research question.
- Post, reel, video, or ad URLs; or creators/competitors to sample.
- Target market, platforms, time window, and desired sample size.
Workflow
- Use
scrapecreators-apito collect post context, top-level comments, and replies from the relevant platforms. Sample across multiple posts and creators instead of overfitting to one viral thread. - Preserve parent-and-reply context where it changes meaning. Remove obvious spam, duplicate comments, tag-only replies, and giveaways unless they are the subject of the study.
- Code each useful comment into one or more buckets: pain, desired outcome, objection, question, comparison, use case, purchase intent, product request, workaround, delight, complaint, churn risk, or exact product language.
- Mark buying-intent strength separately: curiosity, consideration, price or availability question, comparison, stated purchase, repeat use, and recommendation.
- Cluster semantically similar statements while retaining representative wording, the post context, and source links.
- Report prevalence as sample counts by platform and source type, not market-wide percentages.
- Convert the strongest clusters into testable messages, hooks, FAQ topics, product questions, or research follow-ups.
Output
- Coverage and sampling method.
- Ranked theme table with top-level and reply counts, representative language, source links, and confidence.
- Objection and question bank.
- Purchase, product-request, workaround, and churn signals.
- Recommended next tests for creative, landing pages, content, or product research.
- Limitations and gaps.
Never expose private information, infer sensitive traits, or claim the sample represents all customers.
版本历史
- 1add09b 当前 2026-11-02 21:53
