price-optimization-tool

Evaluate ecommerce price candidates using unit economics.

What this skill does for you

Evaluate ecommerce price candidates using unit economics, historical observations, elasticity analysis, scenario modeling, and controlled experiments. Use when a seller asks what price to test, how price changes could affect contribution or revenue, how to estimate elasticity, how to optimize a bundle or tier, or how to design a price experiment across Amazon, Shopify, TikTok Shop, Walmart, eBay, or other channels. Do not claim a proven optimal price without sufficient clean data, and do not change live prices without explicit authorization.

Quoted from the skill description in the pinned source

Who made it

Publishernexscope-ai

Repositoryecommerce-skills

Installs435as of Sep 27, 2026

Use it with Edge

Set up Edge for me: read getedge.cc/SKILL.md and follow it. Then use Edge to load the price-optimization-tool skill from nexscope-ai/ecommerce-skills.

Paste it into Claude, ChatGPT, Codex or Cursor.

Or read the skill source first.

Security evidence

Security state at 2026-09-30: reviewed. State definitions. This records static evidence for this revision; live load eligibility is checked again.

3 static scans, 0 counted findings. The listed scanners recorded the results shown for this package revision.

Scanner	Date	Result
Edge static checks	2026-09-27	Pass
Cisco skill-scanner	2026-09-27	Pass
Semgrep Edge rules	2026-09-27	Pass

Static scans check the code, not how well the skill works.

Scanner scope, raw findings and mirrored provider records

Socket (skills.sh mirror): Version unavailable. Scope: Not supplied by the mirrored record. Revision: not supplied. Raw findings: not supplied. Counted findings: Not supplied.

Snyk (skills.sh mirror): Version unavailable. Scope: Not supplied by the mirrored record. Revision: not supplied. Raw findings: not supplied. Counted findings: Not supplied.

Edge static checks (Edge-run): edge-static/1.0.1. Scope: Static patterns for remote execution, credentials with network sends, obfuscation, prompt overrides, hidden Unicode, binaries and persistence. Revision: ee0fb29433d02ccc22e3e6cea9ab4586d49fd42e. Raw findings: . Counted findings: .

Cisco skill-scanner (Edge-run): cisco-skill-scanner/2.1.0. Scope: Local static, YARA, pipeline and behavioral analyzers; no LLM or AI Defense analysis. Revision: ee0fb29433d02ccc22e3e6cea9ab4586d49fd42e. Raw findings: 1. Counted findings: .

Semgrep Edge rules (Edge-run): semgrep/1.178.0+edge-rules.c15016a36fd3+offline-v1. Scope: Six Edge-authored static rules with Semgrep CE; offline execution and metrics disabled. Semgrep-maintained rules excluded. Revision: ee0fb29433d02ccc22e3e6cea9ab4586d49fd42e. Raw findings: . Counted findings: .

Source and licence

Repository: https://github.com/nexscope-ai/ecommerce-skills
Pinned skill file: View source at revision
Revision: ee0fb29433d02ccc22e3e6cea9ab4586d49fd42e
Package hash: sha256:8d8abfe68dcea5146a18396aae4999953f611d7e1ac8fda73cc79659844e0462
Licence: MIT