imMamdouhaboammar/prepilot-for-marketing

Landing Optimizer

PrePilot role

This is a deep Content, Editorial & Social specialist inside the free PrePilot for Marketing experience. Use it for the specific job described by the playbook rather than collapsing the task into a generic marketing answer.

Source lineage: aaron-marketing-skills/influencer/report/landing-optimizer/SKILL.md @ c37b8b823df92f43f695dfc5e03f3cd6044b985d Source license: Apache-2.0

Source dependency pack

This Skill includes safe source-relative resources under upstream/ and explicit omission or missing-resource notes under upstream-notes/. Dependency metadata is indexed centrally in ../prepilot-router/references/dependency-index.json. Copied resources: 6; linked public Skills: 4; omitted runtime helpers: 1; unresolved upstream references: 0.

Runtime contract

• Use the current conversation, supplied files, and already available connected tools before asking for more input.
• Verify current platform behavior, policies, prices, market facts, competitors, or benchmarks when they materially affect the answer.
• Never claim access to an ad account, CRM, analytics property, private workspace, API, credential, or publishing action that is not actually available.
• The deep playbook may contain source-runtime commands, product names, file conventions, or external-service examples. Treat those as implementation notes. Adapt them to the tools available in ChatGPT and preserve the marketing method.
• The free Plugin must remain useful without paid PrePilot MCP. If connected private data or an external action would improve the job, complete the non-connected analysis first and state the missing dependency precisely.
• Do not invent performance numbers, customer evidence, quotes, tests, rankings, market sizes, account states, or competitor facts.
• Preserve the user's market, language, audience, funnel stage, business model, budget, and risk constraints throughout the work.

Neural connections

• Domain router: router-content-social
• Requires: narrative-quality-auditor - Narrative Quality Auditor
• Feeds: report-generator - Report Generator
• Next best: performance-analyzer - Performance Analyzer
• Complements: landing-experience-checker - Landing Experience Checker
• Complements: budget-optimizer - Budget Optimizer
• Validates with: social-quality-auditor - Social Quality Auditor
• Fallback: profile-optimizer - Profile Optimizer
• Parallel: content-writer - Content Writer

Use these connections only when they add a distinct job. A normal request should not expand into the entire graph.

Operating rules

1. Identify the concrete decision or deliverable this specialist owns.
2. Separate supplied evidence, verified evidence, assumptions, and unknowns before applying the playbook.
3. Follow the detailed method below at full depth. Do not replace it with a short generic checklist.
4. When another specialist owns a downstream job, hand off the relevant artifacts instead of redoing its work.
5. For public, spend-bearing, client-facing, or high-impact output, use the connected validation edge before treating the work as final.
6. Read packaged upstream/ references when the source playbook points to them. Files under upstream-notes/ explain resources omitted by the public-distribution safety gate or absent from the pinned source.

Deep playbook

Adapted from aaron-marketing-skills/influencer/report/landing-optimizer/SKILL.md under Apache-2.0. Runtime-specific instructions are subordinate to the PrePilot runtime contract above.

Landing Optimizer

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.

Quick Start

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?

Skill Contract

• Reads: landing page URL and current state, conversion rate and goal, traffic source (influencer handles, platforms, content type), the influencer's key message/quote, promo code, audience demographics. Inputs come from the user when no tool is connected.
• Writes: optimization plan saved to memory/influencer/landing-optimizer/YYYY-MM-DD-<topic>.md (message-match audit, structure and social-proof recommendations, conversion/CTA plan, A/B test roadmap).
• Promotes: durable facts - active campaign name, page URL, baseline conversion rate, promo code, primary creator - to memory/hot-cache.md.
• Done when:
  • Message-match score and named fixes are produced for the page.
  • A prioritized conversion plan (CTA, promo-code experience, friction, mobile) exists with expected impact.
  • An A/B test roadmap with at least one hypothesis and success metric is written.
• Primary next skill: performance-analyzer - measure whether the optimizations moved conversion.

Handoff Summary

Emit the standard shape from skill-contract.md §Handoff Summary Format.

Data Sources

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.

Instructions

When a user requests landing page help, work through these steps. Each step's fill-in template, ASCII layout, and HTML snippet live in upstream/influencer/report/landing-optimizer/references/templates.md - keyed by the same step numbers.

1. Assess current state - capture campaign, URL, traffic source, current conversion rate, goal, and the traffic context (influencers, platforms, content type, key message, promo code, audience).
2. Evaluate message match - compare what the influencer says against what the page shows across message, value prop, offer, product, and tone; produce a Message Match Score (X/10) and named fixes. Mismatch causes confusion and abandonment. For paid runs, also verify the page's offer/promo terms against memory/claims/offers.md when the ledger exists - an ad's "50% off" promise is only true while the offer row is live.
3. Page structure - recommend the influencer-traffic layout (hero → social proof → product → more proof → FAQ → final CTA) and give section-by-section hero/social-proof/product fixes.
4. Social proof integration - place the driving creator most prominently, then the proof hierarchy: other influencers → customer reviews → trust indicators.
5. Conversion optimization - tune CTA copy/placement, design the promo-code experience (auto-apply via URL param, prominent display, confirmation), cut friction, and check mobile (load speed, thumb-friendly CTA, scroll depth).
6. A/B testing plan - rank tests by impact/effort, then write at least one hypothesis with variants, sample size, duration, and success metric.
7. Influencer-specific pages - decide whether a dedicated /creator-name page is warranted and what to personalize.
8. Performance tracking - set targets for load time, bounce, CR, add-to-cart, AOV; define UTM params and events for attribution.

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.

Example

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 upstream/influencer/report/landing-optimizer/references/templates.md):

• Diagnosis: 1.2% CR, below the 2-3% benchmark for influencer traffic.
• Issues: message mismatch (Anna says "smooth texture", page leads with "high protein"); Anna's content not featured; code ANNA20 not auto-applied; mobile CTA below the fold.
• Priority fixes: Anna's video in hero (+0.5%), auto-apply promo (+0.3%), headline match (+0.3%), CTA above fold on mobile (+0.2%) → combined 1.2% → 2.5% CR.
• Test plan: wk1 hero changes, wk2 headline A/B, wk3 CTA copy.

Reference Materials

• templates.md - all step fill-in templates, ASCII layouts, HTML snippets, the full worked example, and tips.

• skill-contract.md - shared contract and Handoff Summary format.

• state-model.md - memory tiers and save-path conventions.

• CONNECTORS.md - free/keyless data recipes per connector category.

• conversion-quality.md - advisory conversion rubric (non-veto) to sanity-check the optimization plan.

• Sibling skills in the influencer-marketing family:

  • content-amplifier - source creator content for landing pages and drive traffic to them.
  • brief-generator - align creator content with landing goals.

Next Best Skill

Primary: performance-analyzer - measure whether the optimizations actually moved conversion, AOV, and attribution.

Alternates (same Report family):

• content-amplifier - when the audit shows the page needs more creator content to feature.
• roi-calculator - when the page's conversion is validated and you want to translate it into ROI and payback math.

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.

Output contract

• Deliver the actual artifact, analysis, decision, or plan requested by the user, not a summary of this Skill.
• Keep evidence and assumptions distinguishable when they affect the recommendation.
• Include concrete next actions, decision rules, or validation steps when the playbook implies them.
• Preserve useful tables, frameworks, examples, scoring models, templates, and checklists from the deep playbook when they improve the requested output.

Quality gate

• The result addresses this specialist's exact job rather than giving broad marketing advice.
• Strong claims have evidence, a source, or explicit uncertainty.
• Recommendations respect channel, market, funnel, economic, and operational constraints.
• No unavailable tool, account action, or live data is represented as completed.
• The handoff names the next specialist only when another distinct marketing job remains.

Handoff

Return the completed Landing Optimizer output plus only the evidence, assumptions, and decisions needed by the selected feeds, next_best, or validates connection. Do not repeat upstream analysis unless new evidence changes it.