Ecomm New Products

Provides end-to-end analysis and strategy for e-commerce new product development using category data, competition, market gaps, validation, and launch planning.

Security Scan

This is an instruction-only e-commerce product-research workflow that does not request credentials, install code, or access unexpected system resources — its requirements and instructions are coherent with the stated purpose.

Purpose & Capability

The name/description (new product research & launch planning) match the SKILL.md: it asks for category export files and describes data analysis, competitor review, gap identification, validation, and launch planning. No unrelated capabilities (cloud access, system administration, or unrelated third-party credentials) are requested.

Instruction Scope

Runtime instructions are limited to analyzing provided export files and publicly available signals (Google Trends, Reddit, social platforms). The skill does not instruct the agent to read other system files, environment variables, or store secrets. It does assume access to the web for trend/social queries, which is reasonable for the stated tasks.

Install Mechanism

No install spec and no code files are provided (instruction-only). That minimizes disk/write and execution risk; nothing is downloaded or installed by the skill itself.

Credentials

The skill declares no required environment variables, credentials, or config paths. The inputs it asks for (category export file, price range, specs) are appropriate and proportional to the described functionality.

Persistence & Privilege

Flags show normal defaults (always:false, model invocation allowed). The skill does not request permanent presence or system-level configuration changes. There is no indication it writes to agent config or other skills' settings.

Assessment

This skill appears coherent and low-risk: it only needs your product export files and public-data lookups. Before using it, avoid uploading files that contain unrelated sensitive data (API keys, personal customer PII, or internal config). If you expect the agent to query private services (vendor dashboards, paid tool APIs), confirm what credentials it will need and grant only the minimum necessary. Finally, validate any high-impact recommendations (manufacturing specs, supplier commitments, large ad spend) with your own checks before acting.

Skill: E-commerce New Product Research & Development

Overview

This skill provides an end-to-end workflow for new product research and development across e-commerce platforms (e.g., Amazon, Shopee, SHEIN). It covers:

• Category data analysis
• Target segment identification
• Competitive and feature analysis
• Market gap discovery
• Product definition
• Validation and launch strategy

This framework is category-agnostic and applicable to both private label and branded product development.

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Inputs Required

Input	Description	Example
Category export file	Product data export from research tools or platforms	.xlsx / .csv export
Target price range	Desired selling price band	$20 – $40
Material / positioning	Target quality tier and audience	mid-tier / premium / budget
SKU count goal	Number of products to develop	2–5 SKUs
Sample specs (optional)	Prototype dimensions and features	size, weight, components

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Supported Data Sources

This workflow supports export files from:

• Amazon tools: Sorftime, Helium10, Jungle Scout, SIF
• Shopee tools: Shopdora, platform exports
• SHEIN tools: SHEIN selection assistant
• Any structured dataset containing product-level metrics

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Workflow Steps

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Step 1 — Category Overview

Parse the export file and produce:

• Total monthly and annual unit volume
• Seasonality trends (monthly demand peaks)
• Price distribution (bucketed by intervals)
• Top brands / sellers by SKU count and sales

Typical fields to extract:

Monthly Sales, Annual Sales, Price,
Gross Profit, Margin,
Category Rank, Subcategory,
Listing Age (days),
Review Count, Rating,
Brand / Seller,
Bullet Points / Description,
Dimensions, Weight

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Step 2 — Target Segment Deep Dive

Filter products by:

• Target price range
• Positioning (quality tier / material / use case)

Analyze:

• SKU count per sub-price band
• Average monthly sales per SKU
• Average margin per band
• Brand concentration per band

Decision rule:

Select the segment where:

• High average sales per SKU
• Low brand dominance / fragmentation

→ This indicates a potential opportunity zone

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Step 3 — Competitor & Feature Analysis

Brand Landscape

• Identify top brands by SKU count
• Analyze their pricing patterns
• Understand positioning (budget vs premium vs niche)

Feature / Attribute Analysis

Extract recurring attributes from titles, bullets, or descriptions:

Group into:

• Functional attributes (performance, utility)
• Design attributes (style, form factor)
• Usage attributes (scenarios, convenience)

For each attribute:

• Count number of SKUs
• Calculate average sales

Interpretation:

• High sales + low SKU count → opportunity
• High sales + high SKU count → competitive but validated
• Low sales → weak demand signal

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Step 4 — Market Gap Identification

Identify 3–5 opportunities using:

Gap Type	Criteria	Signal
Attribute gap	High demand attribute with low SKU presence	Underdeveloped niche
Positioning gap	Missing tier (e.g. mid-tier between budget & premium)	Pricing imbalance
Format gap	Inefficient or outdated product formats	Optimization opportunity
Trend gap	Emerging trend not yet saturated	Early mover advantage

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Step 5 — Product Definition (per SKU)

For each recommended SKU:

Product Name (working title)
Suggested Price: $XX.XX

Why this opportunity exists:
[Data-backed reasoning]

Positioning:
[Target audience + tier]

Core Specifications:
- Dimensions:
- Weight:
- Key components:
- Structure / format:

Variants:
[Color / style / version options]

Key Differentiation:
[Clear advantage vs competitors]

Estimated Margin:
~$XX (~XX%)

Launch Timing:
[Quarter / season]

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Step 6 — Sample / Prototype Validation

If prototype or spec sheet is available:

Dimension Validation

• Confirm size aligns with intended use case
• Benchmark against top competitors
• Check compatibility with primary usage scenarios

Feature Feasibility

• Validate physical feasibility of components
• Ensure no over-complexity or cost inefficiency
• Confirm manufacturability

Scoring (1–5 scale)

• Use-case fit
• Competitive differentiation
• Feature completeness
• Cost vs value balance
• Margin viability

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Step 7 — Social Proof Validation

Validate demand using external signals:

Platforms

• Reddit (category discussions, sentiment)
• Google Trends (search demand over time)
• Media / blogs (trend mentions)
• Social platforms (indirect trend signals)

Validation Criteria

• At least one product with strong sales performance in niche
• Evidence of growing or stable search demand
• Community or media discussion indicating awareness

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Step 8 — Keyword Advertising Framework

Tier 1 — Awareness

• Broad category and attribute keywords
• Goal: traffic discovery

Tier 2 — Conversion

• Specific long-tail keywords
• Use-case driven queries
• Goal: improve conversion rate

Tier 3 — Competitor Targeting

• Competitor brand + product type
• Goal: capture high-intent traffic

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Campaign Structure

Campaign	Type	Goal
Auto discovery	Automated ads	Keyword discovery
Core keywords	Manual broad	Ranking
Long-tail	Phrase / exact	Conversion
Competitor	Exact	Traffic capture
Retargeting	Display	Conversion recovery

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Step 9 — Launch Timeline

Phase 1: Initial launch (test demand)
Phase 2: Optimization (ads + listing improvements)
Phase 3: Expansion (variants / additional SKUs)

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Pre-launch Checklist

• Supplier validation
• Sample approval (quality, usability)
• Listing assets (images, descriptions)
• Pricing and promotion strategy
• Advertising budget allocation
• Review acquisition process

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Output Format

The workflow should produce:

1. Market Overview
2. Target Segment Analysis
3. Competitive Landscape
4. Market Gaps
5. Product Recommendations
6. Sample Validation (if applicable)
7. Social Proof Summary
8. Keyword Strategy
9. Launch Plan