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Creator Search Intent Radar

核心功能:将TikTok/YouTube/Instagram等平台的趋势、搜索、用户反馈等杂乱信号,按需求质量、受众适配等维度排序,生成可直接发布的内容决策。典型场景:创作者纠结更帖选题、做周度内容规划、需基于搜索意图选题时使用。输入为平台趋势片段、搜索提示、评论FAQ等;输出为排序的内容待办、平台适配、钩子方向、CTA,可对接脚本生成工作流。

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热度 84.47
最近更新 2026-05-11 17:30:20
分类:营销依赖:纯提示词应用场景:社交媒体 / 文案创作 / 市场分析 / 报告撰写 ...

安装技能

复制下面的话术发给你的 Agent,它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill: npx secure-skills-cli add creator-search-intent-radar
Metadata
name
creator-search-intent-radar
description
Convert TikTok/YouTube/Instagram search and trend signals into a prioritized weekly content backlog with script angles and hook directions. Use when the user asks what to post next, wants trend-based topic discovery, needs search-intent analysis, or wants a platform-by-platform content idea pipeline.

Creator Search Intent Radar

Skill Card

  • Category: Market Intelligence
  • Core problem: What should we post next with real demand signals?
  • Best for: Weekly planning and topic prioritization
  • Expected input: TikTok/YouTube/Instagram trend snippets, search hints, comments/DM FAQs
  • Expected output: Ranked topic backlog + platform fit + hook directions + CTA
  • Creatop handoff: Send top 3 topics into Creatop script workflow

Overview

Turn noisy trend inputs into ranked, publishable decisions.

Priority order:

  1. demand signal quality
  2. audience fit
  3. monetization fit
  4. execution speed

Workflow

1) Collect demand signals

Gather 10–30 candidate signals from:

  • TikTok search/trend surfaces
  • YouTube search/autosuggest
  • Instagram/Reels momentum
  • comments/DM FAQs/community threads

Record provenance for each signal:

  • source_type (official/community/internal)
  • source_link (if available)
  • captured_at
  • confidence (high/medium/low)

If live endpoints are unavailable, run fallback mode using recent internal patterns and clearly label output as mode: fallback.

2) Normalize and dedupe backlog

For each topic, standardize:

  • topic
  • platform_fit (TikTok / YouTube / Instagram)
  • intent_type (learn / compare / buy / troubleshoot / inspiration)
  • freshness (hot / warm / evergreen)
  • audience_fit (1–5)
  • monetization_fit (1–5)
  • difficulty (1–5)

Merge near-duplicate topics before scoring.

3) Score and rank

Use:

priority_score = (audience_fit * 0.35) + (freshness_score * 0.25) + (monetization_fit * 0.25) + (execution_speed * 0.15)

Mapping:

  • freshness_score: hot=5, warm=3, evergreen=2
  • execution_speed = 6 - difficulty

4) Generate decision output

Return:

  1. Top 10 ranked topics
  2. Per topic: 1 content angle + 3 hook directions + CTA
  3. 7-day lightweight schedule

Include data_confidence for each topic (high/medium/low).

Output format

  • Topic:
  • Why now:
  • Platform:
  • Intent:
  • Angle:
  • Hook directions (3):
  • CTA:
  • Confidence:

Quality and safety rules

  • Do not present synthetic/internal signals as live external trends.
  • Avoid generic topics without clear buyer intent.
  • Keep recommendations executable by small creator teams.

License

Copyright (c) 2026 Razestar.

This skill is provided under CC BY-NC-SA 4.0 for non-commercial use. You may reuse and adapt it with attribution to Razestar, and share derivatives under the same license.

Commercial use requires a separate paid commercial license from Razestar. No trademark rights are granted.