trend-ad-composer

name: trend-ad-composer description: Research market trends, competitor signals, audience pain points, and webinar/session offers, then generate high-converting ad concept batches in the required JSON schema and send to the Supabase ingest endpoint. Use when creating paid/social ad concepts for marketers, entrepreneurs, and AI consultants around AI agents, automation, OpenClaw workflows, and weekly solution launches.

Trend Ad Composer

Generate ad concept batches from real signals and submit them to the ingest API.

Inputs to gather before generation

• Offer scope:
  • Generic business ad concepts, or
  • Specific campaign tied to upcoming/past training session
• Target audience slice:
  • Marketers
  • Entrepreneurs/founders
  • AI consultants/agencies
• Batch details:
  • client_id
  • batch_id
• Platform mix:
  • facebook / instagram / linkedin (at least 3 variations per batch)

Research workflow

1. Check current opportunity signals

• Use web search for trend demand and pain-point language
• Pull at least 5-8 sources total across:
  • search trends/news
  • social/community pain points
  • competitor/ad messaging signals

2. Review Reinventing.ai session context

• Review upcoming webinar and selected past sessions at:
  • https://www.reinventing.ai
• Extract positioning angles:
  • outcome promise
  • urgency/time sensitivity
  • who benefits most

3. Convert signals into ad angles

• Prioritize angles with clear economic outcomes:
  • hours saved
  • revenue potential
  • faster launch cadence
  • reduced execution overhead

Output requirements

Always produce JSON matching the schema expected by the app endpoint.

Required top-level keys:

• client_id
• batch_id
• generated_at
• research_context
• ad_variations
• generation_metadata

Variation quality rules:

• 3-6 variations minimum
• Mix broad + niche angles
• Include reasoning tied to research signals
• Include strong image prompts suitable for generation
• Include targeting notes and estimated_hook_strength
• Use platform-appropriate aspect ratios (avoid defaulting to wide)

Aspect ratio defaults by platform (preferred):

• instagram_feed: 4:5 (1080x1350 style)
• instagram_square: 1:1
• instagram_story/reels: 9:16
• facebook_feed: 4:5
• facebook_landscape: 16:9 only when explicitly requested
• linkedin_feed_image: 4:5 (or 1:1 secondary)
• linkedin_landscape_ad: 16:9 only for specific ad placement

Visual creative guidance (mandatory in every image_prompt):

• Specify composition clearly:
  • subject placement (left/right/center)
  • foreground + background elements
  • depth of field / layering
• Include ad-friendly layout intent:
  • “leave clean negative space for optional headline overlay”
• Define emotional tone:
  • confidence, relief, momentum, authority, etc.
• Define style and realism level:
  • corporate photography, cinematic lifestyle, flat illustration, UI mockup, etc.
• Define color direction:
  • contrast, brand-safe palette, high legibility
• Explicitly request: no embedded text, logos, or watermarks

Copy style constraints

• Value-first hooks
• Short, specific claims
• Avoid generic hype and inflated promises
• Prefer concrete outcomes and implementation language
• For LinkedIn concepts, no outbound links in body copy

Endpoint submission

Use the helper script:

set -a; source /root/.openclaw/credentials/ads-ingest.env; set +a
python3 skills/trend-ad-composer/scripts/submit_ads.py --json-file /path/to/batch.json

Environment variables required:

• ADS_INGEST_ENDPOINT
• ADS_INGEST_API_KEY

Optional references

• Schema reference: references/ad-schema.example.json