What does this skill do, and when should you use it?
Trend Spotter is the influencer-scouting trend analysis skill in Aaron Marketing Skills. Given a brand or industry, platforms, audience, geography, time horizon, and content categories, it maps topics, hashtags, sounds, challenges, formats, and cultural moments. It scores candidate trends for brand fit, classifies their momentum as rising, peak, or declining, and recommends participate, caution, or skip. It is designed for campaign timing and themes, not creator discovery or social-calendar construction.
Reads brand and campaign parameters plus any available findings in memory/influencer/; analyzes trending topics, hashtags, sounds, challenges, platform formats, cultural events, seasonal opportunities, and competitor adoption; builds a cultural calendar with lead times; scores audience alignment, brand-value fit, content adaptability, risk, and timing on a 25-point scale; produces a ranked trend report with rising/peak/declining calls, top-three act-now recommendations, watch and avoid lists, timing windows, format and hashtag guidance, and a next-review date; saves the report to memory/influencer/trend-spotter/YYYY-MM-DD-<topic>.md and promotes durable facts to memory/hot-cache.md.
- A brand team planning its next TikTok campaign needs to decide which current trends are worth joining.
- A marketer is evaluating a hashtag, sound, or challenge before committing campaign budget and wants a quantified fit score.
- An influencer lead needs to map cultural moments to content-production lead times and campaign timing.
- A brand wants to review which trends competitors adopted, missed, or overused.
How do you install this skill?
- Do not treat the template examples, trend names, view counts, or “best performing” claims as verified facts; require dated, platform-specific sources and distinguish Estimated from Measured.
- Before writing to memory or promoting findings to hot-cache, confirm the path, content scope, and absence of sensitive brand or audience data, with a manual rollback plan.
- Before using the RSS, Tavily, Wikipedia, Reddit, or YouTube recipes, verify network reachability, regional representativeness, platform terms, and freshness; mainland-China users may need reachable local substitutes.
- Resolve the CLAUDE_PLUGIN_ROOT versus repository-root execution convention and add diagnosable handling for network errors, empty results, malformed inputs, and write failures.
- Shell / CLI
- Network access
- Local filesystem
Python 3
For Claude Code, run /plugin marketplace add aaron-he-zhu/aaron-marketing-skills, then /plugin install aaron-marketing@aaron. On an Agent Skills-compatible host, run npx skills add aaron-he-zhu/aaron-marketing-skills to install the collection. The skill is located at influencer/scout/trend-spotter/SKILL.md.
tmp="$(mktemp -d)"
git clone --depth 1 https://github.com/aaron-he-zhu/aaron-marketing-skills.git "$tmp"
mkdir -p ~/.claude/skills
cp -R "$tmp/influencer/scout/trend-spotter" ~/.claude/skills/
rm -rf "$tmp"Generated from the source repository and skill path; it copies only this skill's folder. If the author's install steps above differ, follow those first. To scope it to one project, replace ~/.claude/skills with that project's .claude/skills.
How do you use this skill?
Ask a supported prompt such as What trends are relevant for [brand/industry] right now? or Should [brand] participate in [trend/challenge]? Score the brand fit and give a go/skip call. Include the platform and time horizon when relevant. The skill asks for the brand, platforms, audience, geographic focus, and time horizon if they are missing.
What are this skill's strengths and limitations?
- Produces a concrete ranked report with brand-fit scores, momentum calls, and go/caution/skip recommendations.
- Combines trend signals, content formats, cultural-calendar planning, and competitor adoption analysis.
- Works at Tier 1 without a required live integration when the user supplies the inputs.
- Can be sharpened with Google Trends RSS, Hacker News, Reddit, YouTube outlier data, Wikipedia pageviews, and Tavily.
- The core analysis depends on user-provided brand, platform, audience, and time-horizon inputs; real-time data is not guaranteed by the skill itself.
- Optional external recipes require shell access, Python 3, and network access; single-source signals must remain labeled Estimated.
- It does not find creators to execute selected trends or build a social posting calendar from a go verdict.
- The source does not provide independent platform-by-platform test coverage details.
How does this skill compare with similar options?
Side by side with related skills; every score comes from the same FSRS standard.
| Skill | FS score | Stars | Last updated | License |
|---|---|---|---|---|
| Trend Spotter this page | 49 · Use with care | ★ 3k | 13d ago | Apache-2.0 |
| Influencer Fit Scorer | 52 · Use with care | ★ 3k | 13d ago | Apache-2.0 |
| Evergreen Social Calendar | 59 · Recommended | ★ 3k | 13d ago | Apache-2.0 |
| Share of Voice Tracker | 59 · Recommended | ★ 3k | 13d ago | Apache-2.0 |
| Social Pulse Monitor | 58 · Recommended | ★ 3k | 13d ago | Apache-2.0 |
How did FollowSkills review this skill?
The skill discloses its expected brand inputs, optional memory reads, report paths, hot-cache promotion, and connector categories. 11 points are deducted because it does not clearly require user confirmation before writes, define sensitive-data handling, provide rollback, or expose an approval-level data-flow boundary; repository-wide security controls do not fully substitute for skill-level safeguards.
The workflow, templates, inputs, outputs, and done conditions are mostly coherent, and a no-dependency RSS recipe is supplied. 12 points are deducted because static files do not reproduce the key paths, define diagnostic behavior for malformed inputs, network failures, empty signals, or write failures, and contain an environment assumption: the main skill uses CLAUDE_PLUGIN_ROOT while the recipe says to run from the repository root.
Triggers, argument hint, audience, platforms, geography, time horizon, outputs, and non-fit cases are reasonably clear. 6 points are deducted because the material is predominantly English with limited Chinese-language guidance; geo-relevance is explicitly low, and core signal quality may depend on Google Trends, Reddit, YouTube, and other services without mainland-China reachability or substitute paths being documented.
The candidate has structured frontmatter, versioning, an Apache-2.0 license, quick starts, a contract, templates, examples, references, and handoff guidance. 5 points are deducted for no skill-specific changelog, explicit maintenance owner or update path, complete single-skill installation/dependency notes, or systematic troubleshooting; example metrics lack source and freshness disclosure.
It specifies a directly usable trend report containing rankings, brand-fit scores, format calls, a cultural calendar, timing, and go/skip recommendations. 10 points are deducted because no execution evidence verifies output quality, live trend data is optional despite examples implying current measurements, and metrics and “best performing” claims require substantial human verification and editorial review.
The recipe names multiple public feeds and defines a reproducible relevance formula, with single-source signals labeled Estimated. 7 points are deducted because the skill has no key-path tests or third-party execution evidence, example trend, view-count, and performance claims lack item-level attribution and corroboration, and the supplied repository tests do not exercise this skill.
Open a dimension to read why it scored that way
Evidence confidence：Low — Mostly static review, author material or a limited demo; useful for discovery, not high-risk decisions.
See the full review method →