• Reads: brand/industry, target platforms, audience, geographic focus, time horizon, content categories; prior audience and niche findings from

  memory/influencer/

  if present.
• Writes: a trend report (ranked trends, brand-fit scores, format calls, cultural calendar, go/skip recommendations) to

  memory/influencer/trend-spotter/YYYY-MM-DD-<topic>.md

  .
• Promotes: durable facts (top trends to act on now, trends to avoid, next review date) to

  memory/hot-cache.md

  .
• Done when:
  1. Each candidate trend has a brand-fit score and a go / caution / skip call.
  2. The report names the top 3 trends to act on now plus a watch list and an avoid list.
  3. Action items carry a timing window and a content-format recommendation.
• Primary next skill: influencer-discovery — find the creators who can execute the chosen trends.

Emit the standard shape from skill-contract.md §Handoff Summary Format.

~~

• ~~social platform analytics

  — trending hashtags, sounds, and view counts per platform.

• ~~trend database

  — emerging topics, challenge participation, and growth rates.

• ~~social listening

  — cultural conversations and sentiment around a topic.

• ~~competitor tracking

  — which trends rival brands have adopted and how they performed.

rss_monitor.py

~~trend database

python3 "${CLAUDE_PLUGIN_ROOT}/scripts/connectors/tavily.py" search "<vertical or candidate trend>" --topic news --time-range w --limit 10

python3 "${CLAUDE_PLUGIN_ROOT}/scripts/connectors/pageviews.py" "<Topic_Article>" --granularity daily --days 30

https://hn.algolia.com/api/v1/search?query=<topic>

1. Define trend parameters — capture brand/industry, platforms, audience, geographic focus, time horizon, and content categories. (Template: Step 1.)
2. Identify current trends — log trending topics, hashtags, audio/sounds, and challenges with volume, growth, lifespan, and brand-safety flags. (Template: Step 2.)
3. Analyze content format trends — list hot, emerging, and declining formats per platform with how-to-adapt notes. (Template: Step 3.)
4. Track cultural moments — build the cultural calendar (events + lead times), conversations to join vs avoid, and seasonal opportunities. (Template: Step 4.)
5. Assess trend relevance — for each candidate trend, score audience alignment, brand value fit, content adaptability, risk, and timing (X/25) and land a ✅ participate / ⚠️ caution / ❌ skip call. (Template: Step 5.)
6. Monitor competitor trend adoption — record which trends rivals adopted, gaps they missed, and what they overused. (Template: Step 6.)
7. Generate the trend report — assemble top-3-act-now, watch list, avoid list, timed action items, format and hashtag strategy, and a next-review date. Save to

   memory/influencer/trend-spotter/YYYY-MM-DD-<topic>.md

   and promote durable facts to

   memory/hot-cache.md

   . (Template: Step 7.)

• references/templates.md — fill-in templates for every step, the extended worked example, and execution tips.
• skill-contract.md — shared contract and Handoff Summary format.
• state-model.md — HOT/WARM/COLD memory tiers and save paths.
• CONNECTORS.md — free/keyless data recipe per connector category.
• STAR benchmark scoring at references/star-benchmark.md — for grading trend-driven creative output downstream.
• Siblings in the scout phase: audience-mapper, influencer-discovery, fit-scorer.

• Primary: influencer-discovery — turn the chosen trends into a shortlist of creators who can execute them.
• Alternate: audience-mapper — confirm which trends actually resonate with your audience before committing.
• Alternate: fit-scorer — score which creators fit the chosen trends and the brand before committing.