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Get Started Free →Use when the user asks to "optimize our landing page for influencer traffic", "fix our promo-code landing page", or "improve conversion from a creator campaign"; produces a message-match audit, page-structure and social-proof recommendations, a promo-code/CTA conversion plan, and an A/B test roadmap. Not for measuring campaign results after launch — use performance-analyzer.
.claude/skills/aiskillstore-landing-optimizer/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | 80% | 0% |
| case-04 | ✗→✓ | ▲ Improved | 121% | 0% |
| case-07 | ✗→✓ | ▲ Improved | 165% | 0% |
| case-08 | ✗→✓ | ▲ Improved | 157% | 0% |
| case-09 | ✗→✓ | ▲ Improved | 49% | 0% |
This skill helps you create and optimize landing pages specifically for influencer marketing traffic. When users click from an influencer's post, the landing experience should feel connected and optimized for conversion.
> Cross-discipline (paid ads): this is also the paid-ads post-click skill — the page half of the ROAS Offer message-match (it pairs with ad-creative-builder, which owns the ad half). The same diagnose-and-fix flow applies to paid landing pages; save paid runs under memory/ad/landing-optimizer/. On paid runs, message-match the page against the offer-claims-registry ledger when present: offer terms, promo codes, and dates against memory/claims/offers.md, and claim wording against the approved variants in memory/claims/claims-ledger.md.
Shortest invocation:
Optimize our landing page for traffic from [influencer campaign]Common scenario — diagnose and fix a low-converting creator page:
Our influencer landing page has [X%] conversion rate. How can we improve it?memory/influencer/landing-optimizer/YYYY-MM-DD-<topic>.md (message-match audit, structure and social-proof recommendations, conversion/CTA plan, A/B test roadmap).memory/hot-cache.md.> Emit the standard shape from skill-contract.md §Handoff Summary Format.
This family needs no live integrations (Tier 1). The skill works by asking the user for the page URL, current conversion rate, the influencer's message, and the promo code, then producing the audit and plan from those inputs.
Optional connectors that can deepen the analysis when available:
~~analytics — pull live conversion rate, bounce rate, scroll depth, and add-to-cart events instead of asking.~~A/B testing platform — read past test results and feed sample-size/duration estimates.~~CMS / landing page builder — inspect current page structure and copy directly.~~social platform analytics — confirm the creator's actual messaging and audience.See CONNECTORS.md for the verified free/keyless recipe per category. Every step degrades gracefully to user-supplied inputs.
When a user requests landing page help, work through these steps. Each step's fill-in template, ASCII layout, and HTML snippet live in references/templates.md — keyed by the same step numbers.
memory/claims/offers.md when the ledger exists — an ad's "50% off" promise is only true while the offer row is live./creator-name page is warranted and what to personalize.Save the finished plan to memory/influencer/landing-optimizer/YYYY-MM-DD-<topic>.md (paid runs to memory/ad/landing-optimizer/) and promote durable facts to memory/hot-cache.md.
User: "Our landing page for @fitnessanna's protein powder campaign has a 1.2% conversion rate. How can we improve it?"
Output (abridged — full version in references/templates.md):
ANNA20 not auto-applied; mobile CTA below the fold.Primary: performance-analyzer — measure whether the optimizations actually moved conversion, AOV, and attribution.
Alternates (same Measure family):
Termination note: Maintain a visited-set this session. If a recommended skill has already been invoked, stop and report the chain as complete rather than re-running it. Hard stop at chain depth 3 to avoid loops.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-02 | fail→fail | 25,911 | 24,419 | -6% | 1 | 1 | 0% | 4,373 | 5,984 | +37% | 0 | 0 | — |
case-01 | fail→pass | 19,413 | 23,033 | +19% | 1 | 1 | 0% | 3,165 | 5,682 | +80% | 0 | 0 | — |
case-03 | fail→fail | 20,595 | 16,436 | -20% | 1 | 1 | 0% | 3,243 | 4,573 | +41% | 0 | 0 | — |
case-04 | fail→pass | 13,354 | 16,580 | +24% | 1 | 1 | 0% | 2,071 | 4,581 | +121% | 0 | 0 | — |
case-05 | pass→pass | 14,005 | 12,331 | -12% | 1 | 1 | 0% | 2,047 | 3,725 | +82% | 0 | 0 | — |
case-06 | pass→pass | 12,978 | 14,552 | +12% | 1 | 1 | 0% | 2,026 | 4,118 | +103% | 0 | 0 | — |
case-07 | fail→pass | 9,652 | 13,370 | +39% | 1 | 1 | 0% | 1,542 | 4,088 | +165% | 0 | 0 | — |
case-08 | fail→pass | 5,878 | 2,989 | -49% | 1 | 1 | 0% | 890 | 2,289 | +157% | 0 | 0 | — |
case-09 | fail→pass | 8,827 | 3,051 | -65% | 1 | 1 | 0% | 1,612 | 2,397 | +49% | 0 | 0 | — |
case-10 | pass→pass | 13,650 | 11,972 | -12% | 1 | 1 | 0% | 2,125 | 3,525 | +66% | 0 | 0 | — |
case-11 | pass→pass | 11,351 | 14,819 | +31% | 1 | 1 | 0% | 1,720 | 4,148 | +141% | 0 | 0 | — |
case-12 | fail→pass | 11,516 | 18,309 | +59% | 1 | 1 | 0% | 1,724 | 4,849 | +181% | 0 | 0 | — |
case-13 | fail→pass | 17,296 | 15,647 | -10% | 1 | 1 | 0% | 2,651 | 4,424 | +67% | 0 | 0 | — |
case-14 | fail→pass | 10,110 | 3,654 | -64% | 1 | 1 | 0% | 1,570 | 2,475 | +58% | 0 | 0 | — |
case-15 | pass→pass | 15,039 | 8,420 | -44% | 1 | 1 | 0% | 2,247 | 3,036 | +35% | 0 | 0 | — |
case-16 | fail→pass | 7,937 | 3,589 | -55% | 1 | 1 | 0% | 1,197 | 2,357 | +97% | 0 | 0 | — |
case-17 | fail→fail | 17,627 | 14,750 | -16% | 1 | 1 | 0% | 2,729 | 3,921 | +44% | 0 | 0 | — |
case-18 | fail→fail | 19,543 | 19,222 | -2% | 1 | 1 | 0% | 3,051 | 4,838 | +59% | 0 | 0 | — |
case-19 | fail→fail | 13,042 | 17,801 | +36% | 1 | 1 | 0% | 2,477 | 5,117 | +107% | 0 | 0 | — |
case-20 | fail→pass | 15,274 | 8,676 | -43% | 1 | 1 | 0% | 2,427 | 3,193 | +32% | 0 | 0 | — |
case-21 | pass→pass | 15,615 | 14,344 | -8% | 1 | 1 | 0% | 2,447 | 3,902 | +59% | 0 | 0 | — |
case-22 | fail→pass | 7,672 | 2,214 | -71% | 1 | 1 | 0% | 1,238 | 2,153 | +74% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted. The headline lift of +50 percentage points is the difference between those two pass rates over the 22 comparable cases.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.