# Trend Radar > Daily live-trend scan for any active brand — surfaces timely topics, launches, news, and community pain points in the brand's niche, scored for relevance and timeliness, deduplicated against a rolling log, and written as candidate topics the social calendar can pull. - Skill: `fivebucksventures/trend-radar` (Agent Skill) - Install (CLI): `npx skillmds@latest add fivebucksventures/trend-radar` - Raw SKILL.md: https://api.skillmd.com/api/skills/fivebucksventures/trend-radar/raw - Safety review: PASS (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Productivity - Author: fivebucksventures (https://skillmd.com/u/fivebucksventures) - Updated: 2026-08-19 - Page: https://skillmd.com/skills/fivebucksventures/trend-radar --- ## Maintenance | Agent | Version | Last Changed | |---|---|---| | Link | v2.18.0 | July 01, 2026 | **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 `${BRAND}_TREND_DB` from `.claude/settings.local.json`. ``` IF env var set: notion-fetch → if it resolves, the DB exists. IF unset (or not_found): create it (below), persist the new ID to settings.local.json, notify once. ``` **Create (only when unset):** ``` Use mcp__claude_ai_Notion__notion-create-database: - parent: { "type": "page_id", "page_id": "" } - title: "{Brand Name} Trend Radar" - properties: { "Name": { "title": {} }, // the topic "Date Seen": { "date": {} }, "Source": { "rich_text": {} }, // outlet / platform + real URL "Source URL": { "url": {} }, "Relevance": { "select": { "options": [ {"name":"High"}, {"name":"Medium"}, {"name":"Low"} ] } }, "Timeliness": { "select": { "options": [ {"name":"Breaking"}, {"name":"This week"}, {"name":"Evergreen-ish"} ] } }, "Hook Archetype": { "select": {} }, // suggested archetype from content-creation/hook-library.md "Suggested Angle": { "rich_text": {} }, "Status": { "select": { "options": [ {"name":"Candidate"}, {"name":"Planned"}, {"name":"Skipped"} ] } }, "Type": { "select": { "options": [ {"name":"trend"}, {"name":"competitor-remix"} ] } } } ``` **Dedup:** query `${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 `${BRAND}_PERFORMANCE_DB`: 1. Query competitor rows for that `Owner` handle — sort by `Engagement Rate` descending (fall back to `Likes` if Engagement Rate is unpopulated; fall back to most-recent 3 by `Posted Date` if no metrics at all). Take the **top 3**. 2. For each of the 3 posts, extract: `Topic`, `Hook Archetype`, `Format`, `Content Angle`, `Post URL`. 3. Generate the brand's adapted take — same core topic, reframed through the brand's persona (`audience.md`), voice (`brand.md`), and a clearly differentiated angle. Assign a new `Hook Archetype` and write a one-line `Suggested Angle` that is distinct from what the competitor did. 4. Stage as a remix candidate: `Type = "competitor-remix"`, `Source = "competitor-remix · "`, `Source URL = `. **If a competitor has no rows in `${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: | # | Structural Element | What to extract | Example | |---|---|---|---| | 1 | **Credibility Hook** (0:00–0:30) | The opening claim or credential that earns attention. Note: what metric/credential is used? Is it revenue, experience, speed, result? | "I made $150k last month" (revenue) or "After 20 years at Apple..." (corporate pedigree) | | 2 | **Pattern Interruptor** (0:30–1:30) | The moment the creator breaks expected flow — showing the finished product first, a surprising demo, or a contrarian statement. Note: what technique? (show-don't-tell, flash-forward, contrarian frame) | "Let me show you the finished app first" (flash-forward) | | 3 | **Framework / Mental Model** (1:30–3:00) | The conceptual framework the creator teaches before diving into execution. Usually a 2–4 step mental model or decision tree. | "There are really only 3 types of apps you can build..." | | 4 | **Build / Execution with Escalation** (3:00–end-2min) | The main body — how does the creator structure the build? Look for: time-based escalation (Day 1 → Day 2 → Hour 72), complexity escalation (simple → advanced), or chapter-based progression. Note the escalation pattern. | Time-based: "Day 1: Setting up" → "Day 2: Building features" → "Hour 72: Launch" | | 5 | **Reflection Beat** (near end, 1–2 min) | A personal/honest moment — lessons learned, what went wrong, what they'd do differently. This builds authenticity. | "Here's what I'd actually change if I did this again..." | | 6 | **Close / CTA Type** (final 30s) | How the video ends — monetization pitch, vision statement, community CTA, or resource offer. Note the CTA mechanic (comment-to-DM, link-in-bio, subscribe). | "Comment 'BLUEPRINT' and I'll DM you the template" (comment-to-DM) | 5. **Build the `video_structure` metadata object:** ```json { "video_structure": { "source_video_id": "", "source_channel": "", "total_duration": "", "sections": [ { "position": 1, "element": "credibility_hook", "timestamp": "0:00–0:30", "technique": "", "summary": "<1-line description of what the creator said/showed>" }, { "position": 2, "element": "pattern_interruptor", "timestamp": "0:30–1:30", "technique": "", "summary": "<1-line description>" }, { "position": 3, "element": "framework", "timestamp": "1:30–3:00", "technique": "", "summary": "<1-line description>" }, { "position": 4, "element": "build_with_escalation", "timestamp": "3:00–", "technique": "", "escalation_steps": ["", "", ""], "summary": "<1-line description>" }, { "position": 5, "element": "reflection_beat", "timestamp": "", "technique": "", "summary": "<1-line description>" }, { "position": 6, "element": "close_cta", "timestamp": "", "technique": "", "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__.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: "" - status: "" - summary: "<1 line, <200 chars>" - started_at: "" - completed_at: "" - 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.