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jaygptpro/amazon-pro-skills109 installs

amz-return-reduction

Diagnose why an Amazon product gets returned and build a plan to cut the return rate. Clusters returns into five named root causes, quantifies the dollar bleed per return including the Returns Processing Fee where it applies, and fixes each cause at the listing, the product, or the packaging. Use when a user asks about reducing returns, a high return rate, why customers return a product, return reasons, the Returns Processing Fee, or refunds eating profit. Trigger phrases: "reduce returns", "return rate", "why are customers returning", "return reasons", "too many refunds", "cut returns", "Returns Processing Fee", "returns eating margin". Works with zero tools. the user pastes return reasons and reviews.

How do I install this agent skill?

npx skills add https://github.com/jaygptpro/amazon-pro-skills --skill amz-return-reduction
view source ↗

Is this agent skill safe to install?

  • Gen Agent Trust Hubpass

    This skill is a pure natural language analysis tool for Amazon sellers. It contains no executable code, network operations, or sensitive file access. It is safe to use for its intended purpose of diagnosing product return causes.

  • Socketpass

    No alerts

  • Snykfail

    Risk: HIGH · 2 issues

What does this agent skill do?

Return Reduction

Every return costs more than the refund. You eat the refund administration fee Amazon keeps, the return shipping, often a unit that cannot be resold, and on a high-return ASIN the Returns Processing Fee on top. A high return rate also drags organic rank and can throttle ad eligibility, so the bleed compounds. Most returns trace to a small number of root causes. This skill clusters them, prices each one, and fixes the worst at the source.

When to use this

  • A product's return rate is above the category norm.
  • Returns Processing Fee charges are showing up on the settlement report.
  • Refunds are visibly eating into profit.
  • A seller wants to know why a specific product gets sent back.
  • A return rate is climbing and the cause is unclear.
  • Pre-Q4 audit. you cannot afford a high return rate on peak volume.
  • Returns spiked after a supplier change or a listing edit.

The framework. The Returns Fault Tree

Returns are not random. Almost every one falls into one of five named buckets, and each bucket has a different owner and a different fix. The five are FIT, FAULT, MIRAGE, MISMATCH, and MANGLED. Reading the return reason codes plus the buyer comments plus the 1 to 3 star reviews tells you which bucket a return belongs to.

BucketWhat it meansCommon signalsThe fix lives in
FITSizing or fit miss"too small", "too large", "did not fit my wrist"A size chart graphic, real dimensions in the images, precise sizing language in title and bullets
FAULTQuality or durability defect"broke after two weeks", "stopped working", "fell apart"The product or the supplier. tighten QC, upgrade the material, add a warranty
MIRAGEPhoto and copy oversold the reality"color is different", "looks cheap", "smaller than the photo"The images and copy. reshoot with honest scale and true color, dial the claims down to what arrives
MISMATCHRight product, wrong buyer"not what I needed", "wrong type", bought for an application it was never meant forTargeting and clarity. add negative keywords, sharpen the title, state the intended use up front
MANGLEDShipping damage"arrived broken", "box was crushed", "packaging was poor"The packaging. protective redesign, master carton change, FBA prep. see amz-fba-prep

The diagnosis rule: MIRAGE and MISMATCH are listing problems and the cheapest to fix. FIT is usually a listing fix too, sometimes a product fix. FAULT is a product problem and the most expensive to ignore. MANGLED is a packaging problem. Never treat all returns as one thing, because the fixes point in opposite directions.

There is no sixth "changed mind" bucket on purpose. A genuine "no longer needed" or "bought by mistake" return is mostly unavoidable noise. Park it, do not chase it, and do not let it dilute the four buckets you can actually move.

Price the bleed. what one return actually costs

You cannot prioritize a fix without a dollar figure. Build a fully loaded cost per return so the biggest bucket gets the attention. Add up only the lines that actually apply to this product:

  • Refunded item cost. The unit landed cost you do not get back when the unit comes back unsellable, plus the portion of the sale price you refund.
  • Return shipping and handling. What it costs to get the unit back and inspected.
  • Refund administration fee. When you refund an order, Amazon returns the referral fee it charged you minus a refund administration fee. As of 2026 that fee is the lesser of $5.00 or 20 percent of the referral fee, and it applies on both FBA and FBM. Verify the current figure against your settlement report.
  • Returns Processing Fee (RPF). A separate FBA fee, and it does not apply to every return. See the rule below before you add it.
  • Inventory writedown. If the unit cannot be resold as new, the remaining inventory value is a loss on top of everything else.

Do not double count. The refund administration fee and the Returns Processing Fee are two different fees with two different triggers. The refund administration fee fires whenever you refund a customer. The RPF only fires in the cases below. Many returns carry the refund administration fee but no RPF. Only stack both when the RPF rule below is actually met.

When the Returns Processing Fee applies

The RPF is an FBA fee on high-return products, introduced in 2024 and still in force in 2026. Two different rules, so confirm which one is yours:

  • Apparel and shoes: charged on every returned unit, with no threshold.
  • Most other categories: charged only on returned units above a category-specific return-rate threshold, measured over a trailing three-month window. Below the threshold you pay nothing. Above it you pay per unit on the excess.
  • Low volume is exempt: products shipping under 25 units in a month are not charged that month, and New Selection enrollments get an initial waiver on the first units over the threshold.

The per-unit RPF amount scales with product size and is not a single flat number. Published amounts span a wide range across size tiers, so do not hardcode a figure. Check the RPF for your exact category and size tier in your Seller Central fee schedule or settlement report and use that number. If you cannot confirm it, hedge in the output rather than inventing one. The strategic point holds regardless of the exact cents: on a high-return ASIN the RPF makes cutting returns higher ROI than it was before it existed, because every return you prevent now saves the RPF too.

Step by step

  1. Collect inputs. The return reason codes and their counts if available, the buyer return comments, the 1 to 3 star reviews, the product, the listing copy and images, and the packaging. If the seller has it, pull a 90 day window. shorter is noisy.

  2. Tag every return into a bucket. Use the reason code plus the comment text, not the code alone. "Defective" can mean a broken clasp, a dead battery, or the wrong color. the comment tells you which. Build the distribution. which bucket is the biggest share.

  3. Price each bucket. Apply the fully loaded per-return cost from the section above to the count in each bucket. Now the buckets are ranked in dollars, not just counts. The top one or two usually account for most of the return dollars.

  4. Diagnose the dominant cause. Focus the plan on the one or two biggest dollar buckets. Do not chase the "no longer needed" noise. that effort is wasted.

  5. Fix at the source. Make every fix a specific edit, not generic advice. "Add 'fits 6.5 to 8.25 inch wrists' to the title" beats "improve the title".

    • FIT: add a size chart graphic, put real dimensions on an image, and write the exact range into the title and bullet one. for variation sizing, fix the chart.
    • FAULT: this is a supplier or design problem, and no copy change fixes it. quantify the cost, then tighten QC, upgrade the failing component, or add a warranty. address the product, not the listing.
    • MIRAGE: correct the images and copy so the listing promises exactly what arrives. reshoot with honest scale and true color, add a scale reference, and dial overstated claims down to what the unit actually delivers.
    • MISMATCH: sharpen who the product is for. add negative keywords so the wrong searches stop converting, clarify the title, and state the intended use up front so the wrong buyer self-selects out before checkout.
    • MANGLED: improve protective packaging, change the master carton if units knock together, and fix FBA prep. see amz-fba-prep.
  6. Project the savings. For each fix, estimate the return-rate cut in that bucket and multiply by the bucket's dollar cost. A focused listing or packaging fix commonly takes a bucket down by roughly a third to a half. keep the estimate conservative and tie it to a dollar figure so the fix is prioritized correctly.

  7. Run the quality check, then deliver.

Output format

## Return Reduction Plan. [product or ASIN]

### Baseline
- Return rate: [N%] over [window]
- Return volume: [N units]
- Fully loaded cost per return: $[N]
  (refunded cost + return shipping + refund admin fee + RPF if it applies + writedown)
- Period return cost: $[N]  (annualized roughly $[N x 4])

### Returns Fault Tree distribution
| Bucket | % of returns | Returns/period | $ cost | Top signals |
|--------|--------------|----------------|--------|-------------|
| FIT | [%] | [N] | $[X] | [top quotes] |
| FAULT | [%] | [N] | $[X] | [top quotes] |
| MIRAGE | [%] | [N] | $[X] | [top quotes] |
| MISMATCH | [%] | [N] | $[X] | [top quotes] |
| MANGLED | [%] | [N] | $[X] | [top quotes] |

### Dominant cause: [bucket]

### Top fixes
1. [Bucket]: [specific edit] | projected bucket reduction: [N%] | $ saved: $[N]
2. [Bucket]: [specific edit] | projected bucket reduction: [N%] | $ saved: $[N]

### Projected savings if the top buckets are fixed
$[N] over [window], roughly $[N] annualized.
(If the per-unit RPF was not confirmed, state that the figure assumes it and to verify.)

Worked example

A bamboo watch, $48 price, return rate well above the category norm, 87 returns over 90 days.

Price the bleed first. This is a standard-size non-apparel item, and its return rate is over the category threshold, so the RPF does apply on the units above that threshold. The per-unit RPF depends on the size tier, so the seller pulls the exact figure from the settlement report rather than guessing. Fully loaded cost per return here: refunded item cost plus return shipping plus the refund administration fee (the lesser of $5.00 or 20 percent of the referral fee) plus the confirmed per-unit RPF plus the inventory writedown, since most come back unsellable. Note both the refund admin fee and the RPF are counted only because this ASIN actually clears the threshold. on a below-threshold ASIN you would count the refund admin fee but not the RPF.

Tag the returns into the Fault Tree:

  • FIT 38 percent: "band too tight", "wrist 8.5 did not fit". biggest bucket.
  • MIRAGE 25 percent: "darker than the photo", "face is smaller than it looks".
  • FAULT 22 percent: "clasp broke in week three".
  • MISMATCH 9 percent and MANGLED 6 percent: small, park them.

Top two fixes by dollar cost:

  1. FIT. Add an adjustable-band size chart graphic as image four, put "fits 6.5 to 8.25 inch wrists" in the title, and add the dimension callout to bullet one. A buyer who sees the range before checkout does not order a watch that will not fit.
  2. MIRAGE. Reshoot the main image and image two in daylight-balanced lighting that matches the real bamboo tone, and add a scale shot with a coin reference. Honest images and copy sell slightly fewer units, but the ones they sell stay sold, and the return cost and the rank drag both fall.

Project each fix as a conservative bucket reduction times that bucket's dollar cost, then sum for the annualized saving. FAULT, the broken clasp, is logged separately for the supplier. no listing change fixes a part that breaks.

Quality check

  • Every return is tagged into one of the five Fault Tree buckets using comments, not reason codes alone.
  • Buckets are ranked by dollars, not just counts.
  • The per-return cost only stacks the RPF when the RPF rule is actually met, and never double counts the refund administration fee and the RPF as if both always apply.
  • If the per-unit RPF could not be confirmed, the output hedges it instead of inventing a figure.
  • The plan focuses on the dominant one or two buckets, not all five.
  • The "no longer needed" noise is acknowledged as mostly unavoidable, not over-fixed.
  • MIRAGE and FIT fixes make the listing honest and accurate, not just prettier.
  • FAULT is sent to the product or supplier, not patched with copy.
  • Each fix is a specific edit and is tied to a projected dollar saving.

Common mistakes

  • Treating returns as one problem. A FAULT and a MIRAGE need opposite fixes. lumping them wastes the effort.
  • Reading reason codes only. "Defective" can be a broken clasp, a dead unit, or a wrong color. the buyer comment tells you which bucket it is.
  • Ignoring the 4 star reviews. Many returners leave a "would have been great if X" review at four stars. that is a goldmine for the MIRAGE bucket.
  • Double counting the fees. The refund administration fee and the Returns Processing Fee are separate triggers. counting both on a below-threshold ASIN inflates the cost and misranks the buckets.
  • Hardcoding a Returns Processing Fee number. It varies by size tier and only hits high-return or apparel ASINs. pull the real figure or hedge it.
  • Overselling the listing. Images and copy that promise more than the product delivers buy a sale and then a return.
  • Patching a FAULT with copy. No listing change fixes a product that breaks.
  • Chasing "no longer needed" returns. Mostly unavoidable. effort spent there is wasted.
  • Ignoring the rank cost. A high return rate is not only refunds. it drags organic rank and can throttle ad eligibility too.

Built by Jay GPT Pro

Part of Amazon Pro Skills. Production-grade skills for serious Amazon sellers. Free and open. Built by Jay Margaliot.

I share a new AI play for Amazon sellers every week, free, in my WhatsApp group. Join here: https://chat.whatsapp.com/ILX65p1yWcaIG3c9WGHpTY

Add the canonical catalog link to the repository README so users can inspect current installs and available audits. The publishing guide covers the complete discovery path.

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