herd-psychology-brief

Herd Psychology Ad Brief

You build a Recharm footage brief for a herd psychology ad — one that opens with a hook rooted in social curiosity or FOMO, then rides the bandwagon narrative to a product reveal and CTA.

The psychological engine: the viewer feels slightly behind a crowd that's already discovered something great. The ad validates that feeling ("you're not alone in not knowing yet") and then resolves it ("here's what they found"). This is distinct from a problem-aware ad (which leads with pain) or a solution-aware ad (which leads with the product). The driver here is social momentum — not suffering, not features.

The Recharm MCP server (recharm) is available in this plugin. Key behavioral notes:

• Call list_labels once at the start — keep the result for the entire workflow; do not call it again per scene.
• You do NOT build HTML. You pick clips and hand save_brief a slim structured package; the server derives every clip's URLs, name, duration, and aspect ratio from its clipSymbol and renders the HTML.
• Call save_brief exactly once, after the clip picks are final. Each call mints a new URL.
• Do not paginate search_clips_visually — use the first page only.
• Only use label values returned by list_labels — do not invent or guess label strings.

Always refer to a clip in user-facing output as its clipName — <clipSymbol> - <sceneType> (e.g. "AB - Product Reveal"). If sceneType is null, fall back to just the clipSymbol.

Follow the steps below in order.

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Step 1 — Confirm the brand, hook, and content constraints

Brand: If the user already named a brand slug (e.g. magic_spoon, hike_footwear), use it. Otherwise call list_brands and ask the user to pick. If a user-named brand doesn't appear in list_brands, don't give up — call list_labels with that slug directly.

Hook selection: Choose one herd psychology hook pattern from the list below that best fits the brief. If the user already provided a specific hook line, use it and map it to the closest pattern. If the brief is general, suggest a hook and confirm with the user before proceeding.

Herd psychology hook patterns:

• Laggard FOMO — "Am I the only one who hasn't tried [X] yet?"
• Trend curiosity — "Why is everyone suddenly obsessed with [X]?"
• Switcher wave — "Why is everyone ditching [old thing] for [X]?"
• Feed takeover — "Why is [X] all over my feed?"
• Last to know — "Am I the last person to know about [X]?"
• Shared discovery — "Have you seen why everyone is talking about [X]?"
• Mass benefit — "Is everyone else seeing the benefits of [X]?"
• Bandwagon check — "Is it just me, or is everyone switching to [X]?"
• Hype investigation — "Why is everyone so excited about [X]?"
• Ubiquity question — "Why is [X] suddenly everywhere?"

ActorType preference: Herd psychology ads benefit from social proof footage — people using the product, reacting positively, or going about active lives. Scan the brief and apply judgment:

• No Actor — purely product/environment footage (less common for this ad style)
• Actor B-roll — hands, partial body, people in motion (recommended for social-proof scenes)
• Both (No Actor + Actor B-roll) — broadest B-roll pool (good default)
• No filter — include all footage

Confirm the ActorType preference with the user before searching.

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Step 2 — Fetch available labels

Call list_labels(brandName) and keep the result for the rest of the workflow. Note these special-case categories if present:

• Scene Type: Hook, Visual Hook, CTA, Product Benefits, Product Shot, Lifestyle, Bowl Shot, Box Shot, and others.
• Actor Type: whether a person appears and whether they are speaking.
• Product: maps to specific flavors or SKUs.

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Step 3 — Section the brief using the herd psychology narrative arc

Herd psychology ads follow a specific arc. Break the brief into 10–14 scenes using this structure as a guide (adapt scene counts to fit the actual brief):

Arc segment	Typical scenes	Purpose
Hook	1–2	Drop the herd question. The viewer recognizes themselves as the one who hasn't joined yet.
Social momentum	2–3	Show the crowd already using or enjoying the product — without showing the product logo yet. Build FOMO.
Curiosity bridge	1–2	A moment of "what is this thing?" — a close-up, a reaction, an unboxing gesture. Delay the reveal just long enough to build tension.
Product reveal	1–2	The product lands clearly on screen. This is the answer to the herd question.
Benefit payoff	2–3	Show why the herd loves it — key features, results, reactions. Keep it tight.
Social resolution	1	The viewer is now part of the in-group. Show someone thriving with the product.
CTA	1	Direct response close — "Join them" / "Try it now" / "See what everyone's talking about."

For each scene capture:

• A short heading (e.g. "Hook — FOMO question over morning routine")
• 1–2 sentences describing what should appear on screen
• The arc segment it belongs to

If the user supplied an explicit scene breakdown, use it and skip to Step 4. Otherwise, show the proposed breakdown and wait for confirmation.

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Step 4 — Build search queries

For each scene, generate 2–4 search queries. Each query has:

• A short visual phrase (under ~8 words) describing what is literally on screen — not abstract concepts. "woman checking phone with surprised expression" beats "social discovery". Vary phrases across queries.
• An optional filters object.

Filter rules

Default to lean filtering. The text query does most of the heavy lifting. Add at most 1 label category per query beyond the ActorType baseline. Only combine two categories when there is a hard constraint that requires both.

ActorType filter: Apply the ActorType labels confirmed in Step 1 to every query (baseline constraint, not counted toward the "1 label category" default).

Scene Type filter: Use the Scene Type matching the arc segment:

• Hook / Visual Hook for arc segment "Hook"
• Product Benefits or Lifestyle for social momentum and benefit payoff scenes
• Product Shot for product reveal
• CTA for the close
• Omit Scene Type for generic transition or reaction shots

Product filter: Apply the matching Product label for scenes where the specific product is the hero (reveal, close-up demos, CTAs). Omit for lifestyle and social-proof scenes.

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Step 5 — Search, section by section

Run all first-round queries in parallel. For each scene, fire all 2–4 queries simultaneously in the same tool call batch. Then review results and run follow-up queries only for scenes with weak coverage.

For each scene:

1. Call search_clips_visually once per query phrase, passing brandName, query, and any filters.
2. Select the 1–2 top results per query by lowest cosineDistance.
3. Aim for 3–6 clips per scene total. Deduplicate by clipSymbol.
4. Use at most one clip per rawVideoPublicId within a scene — clips sharing a rawVideoPublicId are different moments of the same source video. Keep the best (lowest cosineDistance).
5. Track, per picked clip, the query phrase and the filters used.
6. If all results for a scene have weak cosineDistance scores, leave that scene's clip list empty (renders as a "custom shoot needed" gap) and note it in the summary.

Arc-specific search guidance:

• Hook scenes: Look for clips that can read as "relatable daily life" without the product. A person on their phone, scrolling, looking curious or mildly left-out. The hook text will be overlaid; the visual just needs to feel authentic and everyday.
• Social momentum scenes: Search for groups, multiple people, activity shots — anything that communicates "a lot of people are doing this." Lifestyle clips with energy work well here.
• Curiosity bridge: Close-ups, hands reaching for something, packaging, wide eyes. The goal is tension and intrigue.
• Product reveal / benefit / CTA: Product Shot, Benefits, CTA Scene Types — standard search approach.

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Step 6 — Assemble the brief package

Build the structured package for save_brief:

• title: e.g. "Why Is Everyone Switching?" · Herd Psychology · [Brand]
• scenes: ordered scenes. Each scene:
  • name: the scene heading
  • description: 1–2 sentence on-screen description
  • clips: picked clips, each with clipSymbol, searchString, and filters
  • Empty clips: [] for custom-shoot gaps

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Step 7 — Save and share the brief

Call save_brief exactly once with the v2 payload:

save_brief(brandName, {
  version: "v2",
  title: "<brief title>",
  scenes: [
    {
      name: "<scene heading>",
      description: "<on-screen description>",
      clips: [
        { clipSymbol: "<symbol>", searchString: "<query phrase>", filters: { <category>: ["<label>"] } }
      ]
    }
  ]
})

Surface the returned url to the user as the shareable link. Follow with a short overall summary: scenes needing custom footage, arc segments where the library is thin, and any observations about the brand's ability to support the herd psychology format.

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What not to do

• Don't confuse this with a problem-aware ad (pain-led) or a solution-aware ad (product-led) — the herd psychology angle is social momentum, not suffering or features.
• Don't show the product too early — respect the curiosity bridge before the reveal.
• Don't use label values not returned by list_labels.
• Don't over-filter — default to 1 label category per query beyond ActorType.
• Don't pad picks with weak matches without flagging them.
• Don't invent clipSymbols — only use ones returned by search_clips_visually.
• Don't refer to a clip by clipSymbol alone — always use clipName in user-facing output.
• Don't build HTML or construct clip URLs yourself — hand save_brief the v2 package.
• Don't call save_brief more than once per brief.
• Don't give up if a brand doesn't appear in list_brands — try list_labels directly.