SkillHub

creator-search-intent-radar

v1.0.3

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...

Sourced from ClawHub, Authored by LeroyCreates

Installation

Please help me install the skill `creator-search-intent-radar` from SkillHub official store. npx skills add Leooooooow/creator-search-intent-radar

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.