How do I install the Paid Ads Amazon skill?

Run npx skillmds add majiayu000/paid-ads-amazon in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Paid Ads Amazon safe to use?

This skill has not completed SkillMD's automated safety review yet. Capability flags: docs only. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Paid Ads Amazon?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Paid Ads Amazon free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Paid Ads Amazon?

majiayu000 (@majiayu000) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Paid Ads Amazon by majiayu000 — Agent Skill

Name: Paid Ads Amazon
Rating: 4.2 (5 reviews)
Author: majiayu000

Paid Ads Amazon

Plan and review Amazon Ads with margin-aware ACoS, product, and search-term guardrails. Use for Amazon advertising, Sponsored Products, Sponsored Brands, Sponsored Display, ASIN targeting, Amazon ACoS, or Amazon Ads performance exports.

majiayu000 e731081 2 files · 3.0 KB Updated Sep 9, 2026 567 repo stars

File contents

Amazon Ads Planning

Read ../shared/operating-contract.md and ../shared/measurement-framework.md. This plugin does not declare a first-party NotFair Amazon Ads MCP mutation surface; use a verified connector or supplied report and deliver a reviewable operator brief.

Start with unit economics

Record the product/ASIN, marketplace, currency, contribution margin, price, inventory constraint, and target ACoS. ACoS is spend divided by attributed ad revenue; it is only good or bad relative to margin and the user's strategic goal. Separate the advertised product from any measured cross-sell before judging performance.

Propose the narrowest learning plan: product scope, campaign intent, automatic discovery or manual term/ASIN hypothesis, budget, negative/exclusion rule, and review window. Treat search-term findings as evidence for targeted negatives or promotion into controlled targeting, not as a reason to remove broad discovery prematurely.

Review and handoff

Report spend, attributed sales, ACoS, ROAS if useful, orders, conversion rate, search-term quality, and inventory risk for a complete comparable window. State reporting lag and attribution source. Mark changes ready_for_review until a verified connector or authorized Amazon Ads operator confirms the exact result.

Frequently asked questions