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Reviews-to-Offer Page
Turn supplied reviews and verified offer facts into an evidence-grounded offer page without cherry-picking.
$14
Reviews-to-Offer Page
Example session with this skill installed
Use these eight supplied reviews and the verified facts for my physical focus mat to create one offer page for remote workers. Show explicit mention counts and denominator, include objections and contradictory evidence, map every claim to both review evidence and an offer fact, and use one CTA.
- Read your context and instructions
- Compiled the reviews-to-offer page
- Generated the UI component
DeskBloom is a fictional washable focus mat with printed zones for today, later, and done—built for a visible desk reset, not a productivity guarantee.
See if the physical workflow fits youWhat the supplied fictional reviews emphasized
5/8Visual organization
Five fictional reviews explicitly valued seeing task categories without opening an app.
R01,R02,R04,R05,R08 + O01 2/8First-day setup friction
Two reviewers wanted clearer examples for deciding what belongs in each zone.
R03,R07 1/8Texture mismatch
One reviewer disliked the surface feel. This remains a fit limitation, not a defect verdict.
R06The offer, grounded in product facts
No app required
The workflow stays on the desk. No claim is made about distraction reduction.
O01Three printed zones
Today, later, and done provide a repeatable visual reset structure.
O02Washable surface
Care guidance is part of the fictional offer facts; durability beyond that is not established.
O03Evidence matrix
THEME SOURCES OFFER FACT CONFLICT PAGE USE Visual organization R01,R02,R04,R05,R08 O01,O02 none hero + benefit Setup uncertainty R03,R07 O02 none onboarding FAQ Surface preference R06 O03 isolated fit boundary CLAIM C01 -> SUPPORTED BY R01,R02,R04,R05,R08 + O01,O02 CLAIM C02 -> QUALIFIED BY R03,R07 CLAIM C03 -> BLOCKED: no review authenticity evidenceFit and non-fit
Likely fit
You prefer a physical, visible desk ritual and already know the small set of tasks you want to sort.
May not fit
You need reminders, team collaboration, analytics, or a texture-neutral digital system.
Start clearly
Use one task per zone on day one. The fictional setup guide would show three worked examples.
Evidence boundary
This demonstration uses eight fictional reviews. It does not establish review authenticity, market frequency, customer satisfaction, product quality, productivity impact, or conversion performance.
CORPUS SIGNAL LEDGER v1.1 Corpus: 8 fictional reviews Majority signal: 5/8 visual organization — R01,R02,R04,R05,R08 + O01,O02 Minority friction: 2/8 setup uncertainty — R03,R07 + O02 Isolated objection: 1/8 texture preference — R06 + O03 Conflict status: VISIBLE — no universal buyer claimVERDICT: DEMONSTRATION — OFFER PAGE READY FOR SELLER REVIEW ``` **Next step:** review the evidence ledger and release blockers before using the asset externally.
reviews-to-offer-page.tsx
TSX · React component
Example file from a real run - the skill writes it into your workspace.
Connects securely to your tools. The creator never sees your data.
What you get
About this skill
The problem
Review-mining prompts often cherry-pick praise and turn one comment into a universal buyer truth. The resulting copy cannot show where its claims came from.
What this skill delivers
Reviews-to-Offer Page transforms a supplied review corpus and verified offer facts into a theme and objection matrix plus a self-contained HTML offer page. It records source IDs, mention counts, denominator, claim-to-offer dependencies, contradictory themes, missing evidence, fit boundaries, and one CTA.
Why it is different
- Every sales claim depends on review evidence and a verified offer fact.
- Minority and contradictory signals qualify the copy.
- Denominators prevent single comments becoming universal claims.
- An executable Corpus-Signal Gate checks the result.
Included
Source Skill, worked HTML demonstration, executable validator, and a six-page English buyer guide inside the ZIP.
Boundaries
No scraping, authentication claim, fabricated testimonial, popularity claim, performance guarantee, customer reply, or publication.
How to install
Works the same in every agent - Claude, Cursor, Codex, Copilot and 20+ more.
- 1
Download the ZIP
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- 2
Unzip into your skills folder
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- 3
Ask your agent to use it
Restart the agent if it was already running. It picks the skill up automatically - no config needed.
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Verified clean 14 days ago
- Passed all security checks, Safe to install