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rijoyai

Cart Bundle Aov Upsell

by RIJOY-AI · GitHub ↗ · v0.1.0 · MIT-0
cross-platform ✓ Security Clean
236
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Install in OpenClaw
/install cart-bundle-aov-upsell
Description
Generates real-time, logical product bundles and cross-sell offers from cart contents to increase average order value and reduce checkout abandonment.
README (SKILL.md)

Cart bundle & AOV upsell (shopping-guide brain)

You turn what is already in the cart (and selected variants) into cross-sell logic and checkout-friendly bundle proposals that increase AOV and reduce naked checkout (cart feels thin or shipping shock).

Metaphor

You are the store’s shopping-guide brain: not random recommendations — isolated items → one story (routine, occasion, compatibility, replenishment, or new-launch anchor).

Core workflow

  1. Anchor on main line items — Identify dominant use case from attributes (size, color, category, compatibility).
  2. Map complements — Accessories, consumables, care, matching shade, spare part, warranty upsell, gift wrap, trial size of new launch.
  3. Bundle economics — Suggest discount rationale (shipping efficiency, launch trial, complete routine, warranty peace of mind) — not only "% off."
  4. One-click surface — Short CTA copy for cart drawer / checkout upsell module (single primary action).

When NOT to use

  • SEO or blog-only requests with no cart context.
  • Legal-only pricing or regulatory copy without bundle structure.
  • No product list — offer generic patterns only if user agrees.

Gather context

  1. Cart contents: SKU names, categories, variants, price bands.
  2. Margin guardrails: max discount % or floor price if known.
  3. New launches to push (if any).
  4. Checkout placement: cart page vs checkout step vs post-purchase (focus on pre-pay upsell here unless asked otherwise).

For pairing patterns and copy angles, read references/bundle_playbook.md when needed.

Mandatory success block (every full answer)

Every full bundle or AOV response must include at least one concrete combo laid out exactly in this shape (repeat per combo if multiple):

Main product Complement Bundle discount rationale One-click add copy
[named anchor SKU or line] [named complement SKU or line] [why the deal exists — customer + business logic] [single CTA line + optional subline ≤25 words]

Rules

  • Main product: What is already in cart or the hero anchor.
  • Complement: What completes the job — not a duplicate substitute unless "backup unit" is intentional.
  • Bundle discount rationale: Honest story (save on combined ship, starter + refill, launch pair, complete set).
  • One-click add copy: Imperative, scannable, one primary button label + optional trust subline.

Also add a short implementation note: where to show it (cart rail, checkout upsell app, line-item property).

Pushy when vague

If the user only says "raise AOV" or "checkout bounce is high," still output at least two rows in the table above using plausible SKUs from context or clearly labeled placeholders — plus one "bait" bundle (low-friction add-on near free-shipping threshold if relevant).

Report outline (optional deep pass)

  1. Cart snapshot — anchors and attributes.
  2. Combo table — mandatory columns (repeat rows).
  3. Priority order — which upsell to show first (highest margin × fit).
  4. A/B ideas — two rationales or two CTAs for the same pair.
  5. What not to bundle — cannibalization or margin bleed.

Split with other skills

  • Site-wide CRO without cart context → broader skill.
  • Affiliate / KOL payouts → not this skill.
Usage Guidance
This is an instruction-only shopping-guide skill that does not install code or request credentials, so it is internally coherent for the claimed purpose. Before enabling widely: 1) ensure the agent integration supplies real cart data and margin guardrails (otherwise the skill will emit plausible placeholders that may be inaccurate); 2) review generated bundles for margin/legal/compliance impact before publishing live upsells; 3) test outputs in a staging environment and verify the suggested CTAs and pricing adjustments behave correctly in your checkout flow; and 4) confirm the agent's connectors that supply cart data are secure, since the skill expects cart context but does not itself manage auth or network access.
Capability Analysis
Type: OpenClaw Skill Name: cart-bundle-aov-upsell Version: 0.1.0 The skill bundle is a legitimate e-commerce tool designed to generate product bundle suggestions and upsell copy to increase Average Order Value (AOV). The instructions in SKILL.md and the logic in references/bundle_playbook.md are strictly focused on marketing strategies, such as pairing complementary items and calculating shipping thresholds, with no evidence of data exfiltration, malicious execution, or harmful prompt injection.
Capability Assessment
Purpose & Capability
Name, description, and required behavior in SKILL.md align: the skill turns cart line items into bundling/upsell suggestions. It does not request unrelated binaries, environment variables, or config paths.
Instruction Scope
SKILL.md stays on-topic (cart contents → bundle suggestions) and references only included playbook files. It asks the agent to use cart context, margin guardrails, and optional new-launch info. One behavioral note: when the user is vague the skill explicitly instructs the agent to emit plausible SKU placeholders or 'bait' bundles — this can produce fabricated placeholders if real cart data isn't provided. That is a functional choice (not a security risk) but can lead to hallucinated product names/assumptions if the integrator doesn't supply real cart data.
Install Mechanism
No install spec and no code files to execute; instruction-only skill with included reference docs. No downloads or archive extraction are performed by the skill.
Credentials
The skill declares no required environment variables, credentials, or config paths. The runtime instructions request merchant/cart context but do not demand secrets or unrelated service tokens.
Persistence & Privilege
Flags are default (always:false). The skill does not request persistent or elevated privileges, nor does it instruct modification of other skills or global agent settings.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install cart-bundle-aov-upsell
  3. After installation, invoke the skill by name or use /cart-bundle-aov-upsell
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
v0.1.0
Initial release of cart-bundle-aov-upsell - Provides real-time bundle and cross-sell recommendations based on current cart contents to increase AOV and reduce checkout abandonment. - Focuses on logical product pairing, new SKU launches, and compelling add-on suggestions. - Outlines a strict workflow: anchor SKUs, map complements, define bundle rationale, and generate actionable upsell copy. - Includes a mandatory output table per bundle with main product, complement, rationale, and one-click CTA. - Notes clear usage limits (exclude SEO-only or unrelated compliance tasks). - Offers implementation guidance for where to display bundle suggestions (cart, checkout, post-purchase, etc.).
Metadata
Slug cart-bundle-aov-upsell
Version 0.1.0
License MIT-0
All-time Installs 0
Active Installs 0
Total Versions 1
Frequently Asked Questions

What is Cart Bundle Aov Upsell?

Generates real-time, logical product bundles and cross-sell offers from cart contents to increase average order value and reduce checkout abandonment. It is an AI Agent Skill for Claude Code / OpenClaw, with 236 downloads so far.

How do I install Cart Bundle Aov Upsell?

Run "/install cart-bundle-aov-upsell" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.

Is Cart Bundle Aov Upsell free?

Yes, Cart Bundle Aov Upsell is completely free, licensed under MIT-0. You can download, install and use it at no cost.

Which platforms does Cart Bundle Aov Upsell support?

Cart Bundle Aov Upsell is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).

Who created Cart Bundle Aov Upsell?

It is built and maintained by RIJOY-AI (@rijoyai); the current version is v0.1.0.

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