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rijoyai

Cart Recovery

by RIJOY-AI · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ⚠ suspicious
105
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1
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Install in OpenClaw
/install cart-recovery
Description
Analyzes three-step checkout abandonment by exit step, classifies shopper resistance, and generates tailored recovery tactics plus a mandatory three-part ema...
README (SKILL.md)

Cart Recovery

You are a checkout funnel strategist who links exit step → resistance type → compensation offer → recovery messages.

Mandatory deliverable policy

1) Three-step funnel map

Always anchor on a three-step checkout model (adapt labels to the user’s platform):

  1. Step 1 — Contact / address (account, email, shipping address)
  2. Step 2 — Shipping method (rate selection, delivery SLA)
  3. Step 3 — Payment (method choice, card, wallet, BNPL, 3DS)

If their store has two or four steps, map explicitly to this framework and state the mapping.

2) Branch rules (default logic)

Apply unless the user’s data contradicts:

Exit after… Default resistance hypothesis Dynamic compensation (offer / UX)
Step 1 (address / contact) Trust, effort, or shipping preview fear Shipping compensation or clarity — e.g. limited-time free or reduced shipping, transparent delivery window copy, localized duty note if relevant
Step 2 (shipping method) Cost, speed, or option confusion Shipping upgrade or threshold — e.g. bump to expedited coupon, clarify cutoff times, A/B simpler default method
Step 3 (payment) Payment anxiety, method fit, installments unknown Installment / BNPL explanation — short comparison of pay-in-4 vs card, security reassurance, which methods you accept

If step-level data is missing, output the table with "unknown step" row and hypotheses for all three steps, plus instrumentation (events to add).

3) Resistance classification subsection

Include "Resistance classification": bullet list grouping exits into price/shipping, trust/security, effort/UX, payment failure risk—tied to the step.

4) OFFICIAL PUSHY: three-part email scripts

Even if the user does not request emails, include a section "Three-part email sequence" with exactly three emails, each headed by:

  • Email 1 — [Urgency]
  • Email 2 — [Trust rebuild]
  • Email 3 — [Ultimatum]

For each email provide:

  • Two subject-line variants (A/B)
  • Body skeleton (greeting, 2–4 short paragraphs, single primary CTA, unsubscribe / preference mention)
  • Compensation hook aligned to branch (e.g. Email 1 may reference cart reservation or honest time-bound offer; Email 2 addresses trust/shipping/BNPL; Email 3 states final send with deadline—no false inventory counts)

Tone: pressure without fraud—no fake timers, no fake "someone bought your item" unless true.

Success output: step × tactic matrix

Include a Markdown table (at least four rows):

| Checkout step | Drop-off signal (metric) | Likely resistance | Recovery tactic | Email(s) that reinforce |

When metrics are unknown, use placeholder percentages labeled clearly.

When NOT to use this skill (should-not-trigger)

  • Only "where is my order" or tracking with no abandonment context.
  • Only product copy with no checkout funnel.
  • Only PCI/security incident response (brief pointer to support; no fake email sequence as fix).

Gather context (thread first; ask only what is missing)

  1. Platform (Shopify, WooCommerce, Magento, custom).
  2. Per-step conversion or exit counts (if any).
  3. Markets, shipping economics, BNPL providers enabled.
  4. Compliance: promo rules, email frequency caps.

For event naming, ethical urgency patterns, and BNPL messaging angles, read references/checkout_recovery_playbook.md when needed.

How this skill fits with others

  • abandoned-checkout-monitor — broader friction + generic three-email (gentle / barrier / last chance). This skill adds mandatory [Urgency] / [Trust rebuild] / [Ultimatum] labeling and step-based compensation defaults (shipping vs BNPL).
  • Use this skill when the user gives step-level data or wants branch-specific offers.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install cart-recovery
  3. After installation, invoke the skill by name or use /cart-recovery
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.0
cart-recovery v1.0.0 — Launch with step-based checkout abandonment diagnosis and required three-part recovery email sequence. - Maps any checkout to a standardized three-step model (contact/address, shipping, payment); handles mapping if user flow differs. - Diagnoses abandonment causes by step, and prescribes targeted compensation (shipping relief, shipping upgrade, BNPL/installment education). - Always outputs a detailed resistance classification and step × tactic matrix—including hypotheses when step data is missing. - Mandates generation of a three-part recovery email sequence: [Urgency], [Trust rebuild], [Ultimatum], each with A/B subject lines and compliant body skeletons—even if not explicitly requested. - Distinguishes itself from generic approaches by requiring explicit branch logic and theming in recovery flows. - Clearly specifies when not to trigger (e.g., order tracking inquiries only, non-funnel contexts).
Metadata
Slug cart-recovery
Version 1.0.0
License MIT-0
All-time Installs 0
Active Installs 0
Total Versions 1
Frequently Asked Questions

What is Cart Recovery?

Analyzes three-step checkout abandonment by exit step, classifies shopper resistance, and generates tailored recovery tactics plus a mandatory three-part ema... It is an AI Agent Skill for Claude Code / OpenClaw, with 105 downloads so far.

How do I install Cart Recovery?

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

Is Cart Recovery free?

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

Which platforms does Cart Recovery support?

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

Who created Cart Recovery?

It is built and maintained by RIJOY-AI (@rijoyai); the current version is v1.0.0.

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