Amazon Product Targeting Skill: From Keywords to ASIN/Category Flow

Where this fits

You're an agent that manages Amazon Ads for a seller whose keyword campaigns have plateaued. The seller asks: "How do I get traffic from competitor detail pages, category nodes, and complementary products?" You need to research, diagnose, and draft a product-targeting plan — without touching live campaigns. That's exactly the gap this skill fills: it turns a vague request into a structured, reviewable plan that complements existing keyword targeting.

Why agents benefit

• Structured research chain — The skill walks the agent from product and consumer-task analysis to candidate ASIN/category pools, then to independent campaign drafts, avoiding the common trap of just dumping competitor ASINs.
• Guardrails built in — It enforces separation between official Amazon mechanisms and third-party seller hypotheses (tagged as SELLER_HYPOTHESIS), so the agent's output is accurate and defensible.
• Single-variable experiment design — Drafts plans that isolate one variable per campaign/ad group, making results interpretable and actionable.
• Bilingual trigger coverage — Recognizes both English and Chinese prompts (商品投放, 竞品详情页抢流量, etc.), useful for sellers operating across marketplaces.
• Never mutates live data — Defaults to research and draft mode, requiring explicit human approval before any campaign changes — a safety property that fits agent delegation.

Practical scenarios

Keyword plateau on a best-selling kitchen gadget

The seller's manual keyword campaigns have hit diminishing returns. The skill guides the agent to identify category nodes (e.g., "kitchen timers") and top competitor ASINs, then drafts a separate ASIN-targeting ad group with bid modifiers, plus a negative-targeting list to avoid wasted spend on irrelevant product pages.

Cross-sell and upsell for a subscription consumable

A seller of coffee pods wants to appear on coffee machine detail pages. The agent uses the skill to map complementary products, verify current marketplace capabilities (e.g., whether category targeting supports refined conditions), and propose a cross-sell campaign with placement analysis — all as a draft for review.

Self-defense against competitor attacks

A competitor is targeting the seller's own ASINs. The agent drafts a defensive campaign targeting the seller's own ASINs with aggressive bids, plus negative targeting to block competitor ASINs that drain budget. The skill ensures the plan includes a single-variable test to measure defense effectiveness.

Add it to your agent workflow

Install the skill via the OpenAgentSkill CLI:

npx skills add xjli360/sealeap-amazon-ad-skills --skill sealeap-amazon-product-targeting

Then load it in your agent's tool registry and invoke it with a scenario:

result = agent.run(
    skill="sealeap-amazon-product-targeting",
    input={"product_asin": "B0EXAMPLE", "goal": "expand beyond keywords", "marketplace": "US"}
)
# result contains a research summary, candidate ASIN/category pools, and a draft campaign structure.

The skill reads a local reference file (if present) and defaults to research mode — no live campaign mutations without explicit approval.

Compare before adopting

Before committing, evaluate alternatives:

• Quality signals — Does the skill produce a verifiable chain from product analysis to targeting decisions, or just a list of ASINs?
• Maintenance freshness — Check the GitHub repo for recent commits and issue responses; Amazon Ads capabilities change frequently.
• Alternatives — Generic Amazon Ads prompt templates may lack guardrails and single-variable discipline.
• Workflow fit — Does it integrate with your agent's existing approval flow and marketplace-specific data sources?

Why it is worth tracking

With 16 stars and an active author, this skill shows early but real community interest in structured, safe Amazon Ads automation. Its emphasis on research-and-draft over mutation aligns with responsible agent design. Track it as a benchmark for how specialized e-commerce skills handle compliance and experimentation — and revisit when your agent needs to handle product-targeting requests beyond simple keyword expansion. For more context, see the skill's detailed page.