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Kai retarget

Skill cgallic/kai-cmo-harness/harness/skills/kai-retarget

Design retargeting and remarketing campaign architecture across platforms — audience segmentation, creative strategy, frequency caps, and platform-specific setup with ad policy compliance. Use when "retargeting", "remarketing", "retarget", "re-engage visitors", "abandoned cart", "pixel setup", or any request to bring back visitors who didn't convert.From its SKILL.md

Install
npx -y skills add cgallic/kai-cmo-harness --skill kai-retarget

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

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SKILL.md

4.8 KB, ~1.1k tokens by cl100k_base, as published. Nobody here has run it

Kai Retarget Skill

Design retargeting/remarketing campaign architecture across platforms with audience segmentation, creative strategy, and policy compliance.

Phase 0: Load Product Context

Check if MARKETING.md exists in the project root (same directory as CLAUDE.md, README.md, package.json).

If it exists: Read it — skip product discovery questions. It has the product name, ICP, value prop, monetization, brand voice, current channels, and competitive landscape.

If it does NOT exist: Auto-explore the codebase to create it in the project root (next to CLAUDE.md). Do NOT ask the user what the product is. Read CLAUDE.md, README.md, PROJECT.md, package.json, landing pages, and any project files. Search for email/ad/analytics config. Then create MARKETING.md using the template from /kai-email-system. Present draft to user for confirmation.

Phase 1: Discovery

Read from MARKETING.md. Only ask about things not covered there:

  1. Traffic sources — Where do visitors come from? (organic, paid, social, email)
  2. Conversion points — What actions matter? (purchase, signup, demo, download)
  3. Drop-off data — Where do people leave? (homepage, pricing, checkout, form)
  4. Pixel/tag status — Which platforms have tracking installed?
  5. Budget — Monthly retargeting spend available
  6. Platforms — Which ad platforms to retarget on? (Meta, Google, LinkedIn, TikTok, etc.)
  7. Product type — B2B or B2C? High-ticket or impulse? Long or short sales cycle?

Phase 2: Plan

Build the retargeting architecture:

  1. Load retargeting playbook: knowledge/playbooks/retargeting-remarketing.md
  2. Load platform policy references (for each active platform):
    • Meta: harness/references/meta-ads-rules.md
    • Google: harness/references/google-ads-policy-reference.md
    • LinkedIn: harness/references/linkedin-ads-rules.md
    • TikTok: harness/references/tiktok-ads-policy-reference.md
    • Microsoft: harness/references/microsoft-ads-rules.md
    • Pinterest: harness/references/pinterest-ads-rules.md
    • Snapchat: harness/references/snapchat-ads-policy-reference.md
    • Amazon: harness/references/amazon-ads-policy-reference.md
    • X/Twitter: harness/references/x-ads-policy-reference.md
  3. Load compliance framework: harness/references/advertising-compliance.md
  4. Define audience segments:
    • Segment by intent level (visited homepage vs. visited pricing vs. started checkout)
    • Set recency windows (1-3 days, 3-7 days, 7-30 days, 30-90 days)
    • Exclude converters from retargeting pools
  5. Map creative to segment — Different message for each intent level
  6. Set frequency caps — Prevent ad fatigue (typically 3-5 impressions/day max)
  7. Define exclusion rules — Suppress ads for existing customers, employees, competitors

Phase 3: Produce

Build the campaign assets:

  1. Audience definitions — Platform-ready segment specs (pixel events, URL rules, time windows)
  2. Creative briefs per segment:
    • Low intent (homepage visitors): Brand awareness, social proof
    • Medium intent (product/pricing viewers): Value props, comparison, objection handling
    • High intent (cart/form abandoners): Urgency, incentive, friction removal
  3. Ad copy per platform — Respect character limits and format rules per platform
  4. Sequence timing — When each segment sees each creative
  5. Budget allocation — Higher spend on higher-intent segments

Phase 4: Quality Gates

Validate before launch:

  1. Four U's Score (on ad copy): python scripts/quality_gates/four_us_score.py <file>
    • Minimum: 10/16 (ad threshold)
  2. Banned Word Check: python scripts/quality_gates/banned_word_check.py <file>
  3. Platform policy compliance — Check each ad against its platform's TOS
  4. Frequency cap validation — Confirm caps are set per segment
  5. Exclusion list verification — Confirm converters are excluded

Max 2 auto-retry cycles on gate failures.

Phase 5: Output

Deliver the retargeting package:

  • Campaign architecture diagram (segments, creative, timing)
  • Audience segment definitions (platform-ready specs)
  • Ad copy per segment per platform
  • Budget allocation table
  • Frequency cap settings
  • Exclusion rules
  • Policy compliance checklist (per platform)
  • Gate pass/fail summary

Write output to workspace/ with filename pattern: retarget-campaign-YYYY-MM-DD.md

What ships with it

Read from the repository

Just SKILL.md. No reference files, no scripts.

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