agentsclimarketplace

Amazon brand tailored promotions

Skill nexscope-ai/Amazon-Skills/amazon-brand-tailored-promotions

Brand Tailored Promotions — audience targeting, discount tiers, customer segmentation, repeat purchase incentivesFrom its SKILL.md

Install
npx -y skills add nexscope-ai/Amazon-Skills --skill amazon-brand-tailored-promotions

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

  • runs commandsInstructs the agent to run 1 command, including `npx skills add nexscope/amazon-brand-tailored-promotions`.

SKILL.md

1.9 KB, 390 tokens by cl100k_base, as published. Nobody here has run it

Amazon Brand Tailored Promotions

Brand Tailored Promotions — audience targeting, discount tiers, customer segmentation, repeat purchase incentives

Supported platforms: Amazon (US, UK, DE, CA, JP, AU, and all marketplaces).

Built by Nexscope — your AI assistant for smarter e-commerce decisions.

Install

npx skills add nexscope/amazon-brand-tailored-promotions

Usage

Help me with amazon brand tailored promotions for my e-commerce business.

Capabilities

  • Brand Tailored Promotions
  • audience targeting
  • discount tiers
  • customer segmentation
  • repeat purchase incentives

How This Skill Works

Step 1: Collect information from the user's message — product, platform, current situation, and goals.

Step 2: Ask one follow-up with all remaining questions using multiple-choice format. Allow shorthand answers (e.g., "1b 2c 3a").

Step 3: Research and analyze using the frameworks and methodology below.

Step 4: Deliver structured, actionable output with specific recommendations, not vague advice.

Output Format

  • Start with a summary of findings
  • Include specific data points and benchmarks where available
  • Provide prioritized action items
  • Mark estimates with ⚠️ when based on incomplete data
  • End with concrete next steps

Other Skills

More e-commerce skills: nexscope-ai/eCommerce-Skills

Amazon-specific skills: nexscope-ai/Amazon-Skills

Built by Nexscope — your AI assistant for smarter e-commerce decisions.

What ships with it

Read from the repository

Just SKILL.md. No reference files, no scripts.

Gives 0 of the 12 instructions most marketing audience skills give in 390 tokens

Counted across 690 of the 894 authors here whose files we hold, read 2026-08-07

  • Apply Poppins font to headingsin 41 of 690, across 6 files
  • Apply Lora font to body textin 41 of 690, across 6 files
  • Use Arial fallback for headingsin 39 of 690, across 4 files
  • Use Georgia fallback for body textin 39 of 690, across 4 files
  • Maintain text hierarchy and formattingin 39 of 690, across 4 files
  • Use accent colors for non-text shapesin 38 of 690, across 3 files
  • Use RGB values for precise color matchingin 38 of 690, across 3 files
  • Use brand colors for primary text and backgroundsin 36 of 690, across 1 file
  • Read product marketing context file before asking questions, starting, or auditingin 35 of 690, across 23 files
  • Use active voice instead of passive voicein 26 of 690, across 10 files
  • Implement or generate appropriate JSON-LD structured datain 24 of 690, across 17 files
  • Prioritize clarity over clevernessin 22 of 690, across 8 files

Said here and by no other author read

  • collect product platform situation and goals from user
  • ask one follow-up with remaining questions in multiple-choice format
  • allow shorthand answers for follow-up questions
  • deliver structured actionable output with specific recommendations
  • start output with a summary of findings
  • mark estimates with warning symbol when based on incomplete data

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.

Keep looking

Skills are one crate of 325,949. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.