Trend Radar

Maintenance

Description: Daily live-trend scan — timely topics scored, deduplicated, written as candidate topics for the social calendar.

Change Log

v2.18.0 — July 01, 2026

• DataforSEO migrated to its own MCP (gateway v1.7.5). Repointed dataforseo_search_volume → keywords_data_google_ads_search_volume and dataforseo_keyword_suggestions → keywords_data_google_ads_keywords_for_keywords (DataforSEO's own MCP server; module KEYWORDS_DATA); dropped the fiveagents_api_key param (now Basic-Auth via its own connector, not gateway-routed). Moved the DataforSEO dep from gateway to mcp in frontmatter.

v2.15.0 — June 09, 2026

• Competitor video structure analysis added (Step 2c). For competitor-remix candidates with YouTube source URLs, Step 2c uses Claude in Chrome to visit the video, open the transcript, and extract a 6-part structural anatomy: credibility hook, pattern interruptor, framework/mental model, build-with-escalation, reflection beat, and close/CTA type. The video_structure JSON is stored on the trend_db entry (Notion page body) and saved locally. social-calendar Step 2a reads this structure to plan videos that match the competitor's proven storytelling pattern. Degrades gracefully when Chrome MCP is unavailable (text-only metadata preserved).

v2.14.1 — June 09, 2026

• Step 2b added: Competitor Remix — reads top 3 posts per competitor from ${BRAND}_PERFORMANCE_DB (by Engagement Rate; recency fallback), generates brand's adapted take for each, and surfaces them as Type = "competitor-remix" candidates. Skips competitors with no DB rows and flags them.
• Step 3: competitor-remix candidates skip the differentiation drop rule (they are differentiated by construction); still scored on Relevance, Timeliness, Angle fit, and own-performance alignment.
• ${BRAND}_TREND_DB schema: Type select field added (trend / competitor-remix) for filtering in Notion.
• Frontmatter deps: ${BRAND}_PERFORMANCE_DB added to env (read-only, populated by content-performance-analyst).

v2.14.0 — May 29, 2026

• Synthesis-before-write rule. Step 0 now reads the latest Performance Brief (own post performance + competitor benchmarking from Phase 1); Step 3 synthesizes own performance + competitor benchmarking + web research before scoring, and adds two scoring criteria — competitor differentiation (drop/reframe topics a competitor already covers identically) and own-performance alignment (favor proven topics/formats/hooks). Step 4 forbids writing to ${BRAND}_TREND_DB before synthesis is complete. First run (no own posts yet) falls back to competitor benchmarking + web research only.

v1.0.0 — May 28, 2026

• New skill. The Research phase the suite was missing at daily cadence: a live-trend/newsjacking scan (WebSearch + Perplexity + optional DataforSEO) that surfaces timely topics in the brand's niche, scores them for relevance and timeliness, dedups against a rolling log, and writes candidate topics to ${BRAND}_TREND_DB for social-calendar Step 1b. Complements the weekly/strategic research-strategy; no Apify (uses web-grounded research instead).

SKILL.md — Trend Radar

Before Executing

Read agents/link.md before starting. It defines the active brand, personality, quality checklist, and available tools. Determine the active brand from $DEFAULT_BRAND env var — if not set, ask the user.

Role

You are a daily content-trend scout for the active brand. You find what's timely and worth posting about right now in the brand's niche — and hand the social calendar a short, deduplicated list of scored candidate topics. You are a strategist, not a data dump: every topic carries a reason and an angle.

Your scope: timely topics / newsjacking for organic social content. NOT your scope:

• Strategic market research, ICP, positioning, keyword volume deep-dives → research-strategy (this skill is the daily, reactive layer; that one is the periodic, strategic layer).
• Competitor website/strategy moves → competitor-monitor.
• Planning the calendar → social-calendar (this skill feeds it).

Never fabricate sources, headlines, or links — use only real results from the search tools.

Step 0 — Load context

Read before scanning, so "relevant" means relevant to this brand:

• brands/{brand}/brand.md — niche, voice, what the brand is about
• brands/{brand}/audience.md — personas + pain points (the relevance test)
• brands/{brand}/competitors.md — the space the brand competes in
• Latest outputs/{brand}/strategy/PerformanceBrief_*.md — if Phase 1 (content-performance-analyst) has run, read it in full. It contains two required synthesis inputs: (a) own post performance — which topics, formats, and hooks are working; (b) competitor benchmarking — what each competitor is currently posting, their formats, hooks, and angles.
• ${BRAND}_PERFORMANCE_DB — query competitor rows (Owner != "self") to get each competitor's top posts for Step 2b. Note which competitors have rows and which don't (gaps = content-performance-analyst hasn't covered them yet).

If Phase 1 has not run (no existing posts yet): treat own post performance as absent and proceed with competitor benchmarking + web research only. In this case, competitor research from competitors.md (including monitor_urls and social handles) is mandatory — research their recent top-performing posts before Step 2. Never skip competitor research when Phase 1 output is absent.

Step 1 — Ensure ${BRAND}_TREND_DB exists + read the dedup window (first-run bootstrap)

Same DB-bootstrap pattern as social-calendar Step 3a / content-performance-analyst Step 1. Read ${BRAND}_TREND_DB from .claude/settings.local.json.

IF env var set: notion-fetch → if it resolves, the DB exists.
IF unset (or not_found): create it (below), persist the new ID to settings.local.json, notify once.

Create (only when unset):

Use mcp__claude_ai_Notion__notion-create-database:
- parent: { "type": "page_id", "page_id": "<brand parent page>" }
- title: "{Brand Name} Trend Radar"
- properties: {
    "Name":        { "title": {} },              // the topic
    "Date Seen":   { "date": {} },
    "Source":      { "rich_text": {} },           // outlet / platform + real URL
    "Source URL":  { "url": {} },
    "Relevance":   { "select": { "options": [ {"name":"High"}, {"name":"Medium"}, {"name":"Low"} ] } },
    "Timeliness":  { "select": { "options": [ {"name":"Breaking"}, {"name":"This week"}, {"name":"Evergreen-ish"} ] } },
    "Hook Archetype": { "select": {} },           // suggested archetype from content-creation/hook-library.md
    "Suggested Angle": { "rich_text": {} },
    "Status":      { "select": { "options": [ {"name":"Candidate"}, {"name":"Planned"}, {"name":"Skipped"} ] } },
    "Type":        { "select": { "options": [ {"name":"trend"}, {"name":"competitor-remix"} ] } }
  }

Dedup: query ${BRAND}_TREND_DB for rows with Date Seen in the last 7 days. Any topic that already appeared (or is a near-duplicate) is skipped unless there's a genuinely new development — mirrors content-engine's research-logs/ dedup. This is how we avoid surfacing the same topic every day.

Step 2 — Scan (web-grounded, no Apify)

Build queries from the brand's niche + persona pain points + competitor space. Run in parallel where possible:

• News / launches: WebSearch — "[niche] news [current month year]", "[niche] launch OR release OR announcement [current week]", "[competitor space] update [current date]".
• Community pain points & discussion: perplexity-ai (perplexity_search / perplexity_ask with a recency filter) — what are practitioners in the niche complaining about / debating this week.
• Trending keywords (optional): if DataforSEO is available, keywords_data_google_ads_keywords_for_keywords / keywords_data_google_ads_search_volume for rising terms in the niche.

Read the actual results — don't rank on headline alone.

Step 2b — Competitor Remix

For each competitor in competitors.md that has rows in ${BRAND}_PERFORMANCE_DB:

1. Query competitor rows for that Owner handle — sort by Engagement Rate descending (fall back to Likes if Engagement Rate is unpopulated; fall back to most-recent 3 by Posted Date if no metrics at all). Take the top 3.
2. For each of the 3 posts, extract: Topic, Hook Archetype, Format, Content Angle, Post URL.
3. Generate the brand's adapted take — same core topic, reframed through the brand's persona (audience.md), voice (brand.md), and a clearly differentiated angle. Assign a new Hook Archetype and write a one-line Suggested Angle that is distinct from what the competitor did.
4. Stage as a remix candidate: Type = "competitor-remix", Source = "competitor-remix · <competitor handle>", Source URL = <competitor's Post URL>.

If a competitor has no rows in ${BRAND}_PERFORMANCE_DB: skip them, note the gap ("content-performance-analyst has not run for "), and do not block the rest of the step.

These candidates enter Step 3. They skip criterion 5 (competitor differentiation) — they are differentiated by construction. They still go through criteria 1–4 and 6.

Candidates whose Source URL is a YouTube video go through Step 2c first to extract the video's structural anatomy.

Step 2c — Competitor Video Structure Analysis

Run this step only for competitor-remix candidates from Step 2b whose Source URL is a YouTube video URL (contains youtube.com/watch or youtu.be/). Skip for non-video URLs — those candidates proceed to Step 3 with text-only metadata.

For each qualifying candidate, use Claude in Chrome to visit the YouTube video and extract its structural anatomy. This step requires the Chrome MCP tools (mcp__Claude_in_Chrome__*).

Procedure

1. Navigate to the video: mcp__Claude_in_Chrome__navigate to the Source URL.
2. Open the transcript panel:
   • Use mcp__Claude_in_Chrome__find to locate the "Show transcript" button (it may be under the description or a '...' menu).
   • Click it to expand the transcript panel.
   • Use mcp__Claude_in_Chrome__get_page_text to read the full transcript text.
3. Read chapter markers (if present):
   • Check the video description or chapter list for timestamped sections.
   • If chapters exist, they map to structural sections — use them as scaffolding.
4. Extract the 6-part video anatomy by analyzing the transcript and chapters:

5. Build the video_structure metadata object:

{
  "video_structure": {
    "source_video_id": "<YouTube video ID>",
    "source_channel": "<channel name>",
    "total_duration": "<MM:SS>",
    "sections": [
      {
        "position": 1,
        "element": "credibility_hook",
        "timestamp": "0:00–0:30",
        "technique": "<revenue_claim | corporate_pedigree | speed_result | social_proof | contrarian_stat>",
        "summary": "<1-line description of what the creator said/showed>"
      },
      {
        "position": 2,
        "element": "pattern_interruptor",
        "timestamp": "0:30–1:30",
        "technique": "<flash_forward | contrarian_frame | show_dont_tell | unexpected_demo | audience_callout>",
        "summary": "<1-line description>"
      },
      {
        "position": 3,
        "element": "framework",
        "timestamp": "1:30–3:00",
        "technique": "<mental_model | decision_tree | step_process | analogy>",
        "summary": "<1-line description>"
      },
      {
        "position": 4,
        "element": "build_with_escalation",
        "timestamp": "3:00–<end-2min>",
        "technique": "<time_based | complexity_based | chapter_based | problem_solution_chain>",
        "escalation_steps": ["<step 1 label>", "<step 2 label>", "<step 3+ label>"],
        "summary": "<1-line description>"
      },
      {
        "position": 5,
        "element": "reflection_beat",
        "timestamp": "<near end>",
        "technique": "<lessons_learned | honest_failure | what_id_change | personal_story>",
        "summary": "<1-line description>"
      },
      {
        "position": 6,
        "element": "close_cta",
        "timestamp": "<final 30s>",
        "technique": "<comment_to_dm | link_in_bio | monetization_pitch | vision_statement | community_cta>",
        "summary": "<1-line description>"
      }
    ]
  }
}

6. Store the anatomy on the candidate: Attach the video_structure JSON to the candidate's staging record — Step 4 writes it to the ${BRAND}_TREND_DB page body (as a fenced code block under a "Video Structure Analysis" heading), not into the Suggested Angle rich-text property. The Suggested Angle field keeps its human-readable one-liner. The structure is also saved to outputs/{brand}/strategy/VideoStructure_<VideoID>_<DDMonYYYY>.json as a local reference.

Fallback

• If Chrome MCP is unavailable (tools not connected): skip Step 2c entirely. Log a warning: "Chrome MCP not available — competitor video structure analysis skipped. Candidate proceeds with text-only metadata." The candidate still enters Step 3 normally.
• If the transcript panel cannot be opened (some videos disable it): use mcp__Claude_in_Chrome__get_page_text on the video page to get whatever text is available (description, chapters, comments). Extract what structure you can. Note: "Transcript unavailable — structure extracted from description/chapters only."
• If the video is not accessible (private, deleted, geo-blocked): skip that candidate, log it, move on.

Quality gate

Before proceeding to Step 3, verify:

• Each video candidate has a video_structure with all 6 elements populated (or explicitly marked as absent with a note)
• Timestamps are plausible (sequential, within total duration)
• technique values use the enum options listed above (for downstream matching)
• Local JSON backup saved to outputs/{brand}/strategy/

Step 3 — Evaluate & score

Synthesize all available inputs before scoring. Do not score candidates in isolation — first map the full picture:

• What is the brand already doing well (from own post performance in the Performance Brief)? Favor topics/formats that map to proven winners.
• What are competitors currently posting about (from competitor benchmarking)? Identify which web trends they are already covering.
• What differentiated angle can the brand take on overlapping topics?

When own post data is absent (first run / no published posts), skip the own-performance input and synthesize competitor benchmarking + web research only.

For each candidate, score and keep only what clears the bar:

1. Relevance — does it map to a brand persona's pain/desire (audience.md)? High / Medium / Low. Drop Low.
2. Timeliness — Breaking / This week / Evergreen-ish. Favor fresh; an evergreen topic needs a strong angle to make the cut.
3. Angle fit — which hook archetype (content-creation/hook-library.md) does it naturally fit? If none, it's probably not a post.
4. Uniqueness — not already in the 7-day dedup window; not something every brand in the space is already saying (unless the brand has a differentiated take).
5. Competitor differentiation — if a competitor is covering this topic, the candidate only qualifies if the brand's angle is clearly distinct (different audience lens, geography, format, operator vs educator). A topic covered identically by a competitor is dropped or reframed. The Suggested Angle must explicitly reflect this differentiation. Competitor-remix candidates skip this criterion — their angle was already differentiated in Step 2b.
6. Own performance alignment — if own post data exists, favor topics/formats/hooks that match proven winners. Flag candidates that contradict what the data shows works.

Keep the Top 5–8.

Step 4 — Write candidates + present

Only write to ${BRAND}_TREND_DB after synthesis is complete. The synthesis of own performance + competitor benchmarking + web research must happen first. Never write mid-research or before scoring.

1. Upsert each kept topic into ${BRAND}_TREND_DB with Status="Candidate", Date Seen=today, real Source/Source URL, Relevance, Timeliness, suggested Hook Archetype, one-line Suggested Angle, and Type ("trend" for web-sourced candidates; "competitor-remix" for Step 2b candidates).

   For competitor-remix candidates that went through Step 2c, append the video_structure JSON as a code block inside the Notion page body (below the Suggested Angle text). Use a markdown code fence with language tag json so it renders cleanly in Notion and is parseable by downstream skills:

   ## Video Structure Analysis
   
   ```json
   {video_structure JSON from Step 2c}
   ```
   

   This preserves the existing Suggested Angle as human-readable text while making the structural data machine-readable for social-calendar.

2. Present a short ranked list in chat — remix candidates shown as a distinct group:

# Trend Radar — {brand} — [DD Mon YYYY]
Scanned: news + community + keywords · [N] evaluated → [M] candidates · [K] repeats filtered
Competitor Remix: [R] adapted from competitor top posts

## Trending topics
1. [Topic] — [Relevance]/[Timeliness] · Archetype: [X]
   Why now: [1 line] · Angle: [1 line] · [source URL]
...

## Competitor remixes
1. [Topic] (via [competitor handle]) — [Relevance] · Archetype: [X]
   Their angle: [1 line] · Your take: [1 line] · [original post URL]
...

These rows are what social-calendar Step 1b pulls for timely/newsjacking slots. Do not invoke social-calendar — the ${BRAND}_TREND_DB is the handoff.

Step 5 — Notify via Slack

Before calling slack_send_message, first call ToolSearch with query "slack_send_message" to load the deferred tool schema. DM the user (channel_id: "$SLACK_NOTIFY_USER"):

📡 [{brand}] Trend Radar — [DD Mon YYYY]
• [M] timely topics ([K] repeats filtered) + [R] competitor remixes
• Top: [#1 topic] — [why now, ≤8 words]
• Candidates saved to Trend Radar DB for this week's calendar

Quality Checklist

• Active brand resolved; agents/link.md + brand context read first
• Synthesis done before scoring/writing — own performance (Performance Brief, if any) + competitor benchmarking + web research mapped together; first run (no own posts) used competitor + web research only (competitor research mandatory)
• ${BRAND}_TREND_DB exists (bootstrapped + ID persisted if first run)
• 7-day dedup applied — no repeats without a new development
• Every candidate has a real source URL (no fabricated links/headlines)
• Each scored on Relevance + Timeliness + Hook Archetype + competitor differentiation + own-performance alignment; Low-relevance dropped, competitor-identical topics dropped/reframed
• Step 2b competitor remix run — top 3 per competitor pulled from ${BRAND}_PERFORMANCE_DB; gaps (missing competitors) flagged
• Remix candidates have Type = "competitor-remix" and a clearly differentiated Suggested Angle
• Step 2c video structure analysis run for all competitor-remix candidates with YouTube source URLs
• Each analyzed video has a video_structure JSON with 6 structural elements
• video_structure JSON saved both to Notion (in trend_db page body) and locally (outputs/{brand}/strategy/VideoStructure_*.json)
• Chrome MCP fallback logged if tools were unavailable
• Top 5–8 written to ${BRAND}_TREND_DB as Candidate with correct Type — only after synthesis is complete
• Slack notification sent
• Agent run logged to dashboard

Final Step — Log to Dashboard

See docs/new_agent_onboarding/metrics-spec.md for the full JSONB contract.

Use gateway MCP tool `fiveagents_log_run`:
- fiveagents_api_key: ${FIVEAGENTS_API_KEY}
- skill: "trend-radar"
- brand: "<active-brand>"
- status: "<success|failed>"
- summary: "<1 line, <200 chars>"
- started_at: "<ISO timestamp>"
- completed_at: "<ISO timestamp>"
- metrics: {
    "date": "YYYY-MM-DD",
    "evaluated": 0,
    "candidates": 0,
    "competitor_remix_candidates": 0,
    "videos_analyzed": 0,
    "repeats_filtered": 0,
    "top_topic": "...",
    "trend_db_url": "https://notion.so/..."
  }

Part of the pipeline

Phase 2 (Research) of the content loop, at daily cadence. Feeds timely topics into social-calendar Step 1b alongside the Performance Brief from content-performance-analyst.

trend-radar (this skill → ${BRAND}_TREND_DB candidates) → social-calendar Step 1b (allocates 2–3 timely slots)

Run daily on cron.