trend-spotter Checked

The intake and query plan work with no live integration. A report about what is current does not: it requires dated user-supplied evidence, a public fetch, or a live connector result for the requested platform, geography, and horizon. Brand inputs alone support search terms and evaluation criteria, not trend names, counts, growth, rising/peak/declining calls, or go/skip recommendations. Without current evidence, return NEEDS_INPUT and the exact queries/fields to collect. Where a tool supplies the read, use a ~~ connector placeholder:

• ~~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.

No connector is required to produce a useful **query plan**. A named current-trend report requires the source records above. See CONNECTORS.md (../../../CONNECTORS.md) for the free/keyless recipe per category.

For a keyless way to discover topics worth measuring, run the multi-source candidate scout — Google Trends RSS + Hacker News + Reddit + YouTube upload titles via the bundled stdlib rss_monitor.py (no new dependency): references/trend-scout-recipe.md (references/trend-scout-recipe.md). RSS/title overlap is only a Proxy candidate and stays NOT_SCORED; it does not establish a platform trend, view-count outlier, lifecycle state, or act-now recommendation. This is the Tier-1 candidate recipe behind ~~trend database (Google Trends RSS).

**Keyless news pulse (Tavily)**: python3 "${CLAUDE_PLUGIN_ROOT}/scripts/connectors/tavily.py" search "<vertical or candidate trend>" --topic news --time-range w --limit 10 adds recency-filtered discovery refs. Agreement with an RSS title may raise query priority, but it remains a Proxy candidate/NOT_SCORED; news overlap does not prove momentum on TikTok, Reels, YouTube, or another requested platform.

**Keyless source-specific sharpeners**: python3 "${CLAUDE_PLUGIN_ROOT}/scripts/connectors/pageviews.py" "<Topic_Article>" --granularity daily --days 30 measures Wikipedia page attention, and the Hacker News Algolia API (https://hn.algolia.com/api/v1/search?query=<topic>, keyless) measures HN points/comments. Those values are Measured only for their named source and window. Treat them as Proxy for a different requested platform; require dated momentum from the exact platform/geography/window before any rising / peak / declining or act-now call.

Instructions

When a user requests trend analysis, run these steps. Each step has a fill-in template in references/templates.md (references/templates.md) — copy the matching block and populate it.

1. **Define trend parameters** — capture brand/industry, platforms, audience, geographic focus, time horizon, and content categories. (Template: Step 1.)

2. **Qualify current evidence** — for every candidate topic, hashtag, audio, challenge, format, cultural moment, and competitor observation, retain source ref, observed/retrieved date, measurement window, platform/geography, metric definition, current value, and comparable prior value. RSS/title overlap alone stays Proxy candidate/NOT_SCORED. Missing current evidence stops factual output and yields the query plan. (Template: Step 2.)

3. **Analyze content format trends** — list rising/peak/declining formats only from a dated momentum series for the exact requested platform/geography/window; label explanations as observed association or hypothesis, never unsupported causation. (Template: Step 3.)

4. **Track cultural moments** — source dates and current conversation/sentiment claims; otherwise return calendar/search fields as TBD. (Template: Step 4.)

5. **Assess trend relevance** — score only evidence-backed candidates on audience alignment, brand value fit, content adaptability, risk, and timing (X/25). Unsupported candidates remain NOT_SCORED with no go/caution/skip call. (Template: Step 5.)

6. **Monitor competitor trend adoption** — require dated post/campaign evidence; do not infer adoption, performance, gaps, or overuse from general brand knowledge. (Template: Step 6.)

7. **Generate the trend report** — fill top-3-act-now, watch, avoid, timed action, format, and hashtag blocks only for candidates that pass the complete current-evidence gate for the exact platform/geography/window; otherwise leave those blocks TBD and return NEEDS_INPUT with the collection plan. Return it inline; offer the exact WARM save path, then ask separately before any HOT promotion. (Template: Step 7.)

For repeatable monitoring, return any proposed ledger write as an inline plan first. Do not run ledger.py record until the user gives a separate exact authorization naming the normalized ledger path, the record operation, and the exact source/topic/platform/geography/window scope. Report-save or HOT-promotion authorization never covers that write.

Example

**User**: "What TikTok trends should a fitness brand run right now?"

Output is NEEDS_INPUT because the prompt supplies no dated TikTok evidence. It returns platform/geography/window-specific queries for topic, hashtag, sound, format, safety, and competitor-adoption records, plus the fields source_ref, observed_at, and measurement_window. It names no trend, count, status, hashtag, format winner, or this-week action until those records arrive. Full version: references/templates.md (references/templates.md#extended-example--tiktok-fitness-trends).

Reference Materials

• references/templates.md (references/templates.md) — fill-in templates for every step, the extended worked example, and execution tips.

• skill-contract.md (../../../references/skill-contract.md) — shared contract and Handoff Summary format.
• state-model.md (../../../references/state-model.md) — HOT/WARM/COLD memory tiers and save paths.
• [CONNECTOR