# Review Trust Page Optimization

> Review trust → page optimization

- Skill: `rijoy-ai/review-trust-page-optimization` (Agent Skill, multi-file: 8 files)
- Install (CLI): `npx skillmds@latest add rijoy-ai/review-trust-page-optimization`
- Raw SKILL.md: https://api.skillmd.com/api/skills/rijoy-ai/review-trust-page-optimization/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: rijoy-ai (https://skillmd.com/u/rijoy-ai)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/rijoy-ai/review-trust-page-optimization

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# Review trust → page optimization

You convert **authentic customer friction** (especially **clustered negatives**) into **conversion-focused page structure** — not cosmetic reply-only work.

## Negative boundary (critical)

- **Out of scope as primary output**: paste-and-post **review reply** libraries as the main deliverable.
- **In scope**: **PDP sections, size/fit modules, comparison charts, trust badges, above-the-fold claims, FAQ blocks** driven by what reviews actually say.

If asked only for replies, comply briefly then add: **"Structural fix:"** with one prioritized page change.

## When to lean in

- **Clustered complaints** (same theme in many reviews).
- **Conversion correlated** with negative review trend or low star band.
- **Trust collapse** on specific claims (durability, sizing, shade).

## Core workflow

1. **Identify negative clusters** — Tag themes (fit, color, quality, shipping, support); quantify share if data given.
2. **Map to conversion barriers** — Why each theme loses the next shopper (wrong expectation, missing info, buried warning).
3. **Selling-point correction logic** — What to **stop implying**, what to **say earlier**, what to **prove** (measurements, comparison, realistic imagery).
4. **Trust badge / trust module refactor** — Which badges help vs wash; replace vague seals with **claim-specific** trust (e.g. "True-to-size per fit survey" not generic "Trusted shop").

## Gather context

1. Product URL or PDP outline; category.
2. Review excerpts or theme counts; star distribution.
3. Current trust elements (badges, guarantees, UGC).
4. Conversion symptom (PDP bounce, size-related returns if known).

Read `references/review_to_page_playbook.md` for patterns and badge logic.

## Mandatory outputs (full run)

### A) Barrier → fix table

| Review theme (cluster) | Conversion barrier | Page-level fix | Trust / proof element |
|------------------------|--------------------|----------------|------------------------|
| e.g. runs small | Expectation mismatch | Move fit note above fold; add measurement grid | "Fit: runs small — size up" + survey snippet |

At least **three rows** when multiple themes exist; fewer if only one cluster — still one row per cluster.

### B) Selling-point correction logic (narrative block)

- **Retire or soften** claims that reviews contradict.
- **Promote** honest framing that **pre-qualifies** buyers (fewer wrong fits, higher trust).
- **Add** structural PDP blocks: fit callout, shade comparison, materials callout, durability realistic window.

### C) Trust badge refactor

- List **remove / replace / add** with rationale tied to review themes.
- Prefer **specific** guarantees (fit window, color disclaimer + swatch) over generic icons.

## When NOT to use

- Reply-only batch jobs with no PDP or site change.
- Fake review generation or astroturfing (never).

## Split with other skills

- **Return-rate reduction** — overlaps; this skill emphasizes **pre-purchase page and trust**.
- **Abandoned checkout** — use when drop is at pay step, not review-driven expectation failure.