Use when applying segmentation implementation.
Scanned 9/10/2026
Install to Claude Code
Skills Directorynpx -y skills add LoopyLuci/Skills --skill customer-segmentation-implementation --agent claude-codeInstalls into .claude/skills of the current project.
Are you the author of Customer Segmentation Implementation?
Add the live security badge to your README — it updates automatically with every re-scan.
[](https://www.skillsdirectory.com/skills/loopyluci-customer-segmentation-implementation)More formats (shields.io, HTML) on the badges page.
---
name: customer-segmentation-implementation
description: "Use when applying segmentation implementation."
version: 1.0.0
author: Hermes Agent
license: MIT
platforms: [linux, macos, windows]
metadata:
hermes:
tags: [marketing, segmentation, customer-segmentation-analysis]
related_skills: [general]
---
## Overview
Segment customers.
## When to Use
- Design and implement
- Apply best practices
- Optimize performance
- Troubleshoot issues
## Key Approaches
1. Define requirements
2. Choose tools
3. Implement modular
4. Test thoroughly
5. Document decisions
6. Monitor results
## Common Pitfalls
1. Ignoring constraints
2. Skipping standards
3. Poor alignment
4. Inadequate testing
5. No documentation
6. Over-engineering
7. No rollback
8. No monitoring
9. No scalability
10. No validation
## Verification Checklist
- [ ] Requirements validated
- [ ] Standards applied
- [ ] Design reviewed
- [ ] Tests defined
- [ ] Docs complete
- [ ] Monitoring configured
- [ ] Rollback planned
- [ ] Security checked
- [ ] Deploy verified
- [ ] Stakeholder approved
Is this your skill, or is something wrong with this listing? Request removal or report an issue. Author removals are honored within 72 hours.
No comments yet. Be the first to comment!
Ultra-compressed communication mode. Cuts token usage ~75% by speaking like caveman while keeping full technical accuracy. Supports intensity levels: lite, full (default), ultra, wenyan-lite, wenyan-full, wenyan-ultra. Use when user says "caveman mode", "talk like caveman", "use caveman", "less tokens", "be brief", or invokes /caveman. Also auto-triggers when token efficiency is requested.
Adversarial multi-agent planning skill. Self-orchestrates 5 hostile category members (unspecified-low, unspecified-high, deep, ultrabrain, artistry) via team-mode for ruthless cross-critique debate, distills only the defensible insights, then MANDATORILY hands the distilled insight bundle to the `plan` agent for executable plan formalization. Use when planning needs maximum rigor and surfacing of weak assumptions, blind spots, and over-engineering. Triggers: 'hyperplan', 'hpp', '/hyperplan', ...
Routes multi-tool workflows through MCP servers for large datasets and pipelines. Use when Bash tool overhead is limiting throughput on data-heavy tasks.
Recovers prior coding-agent session context by running `catchup <agent> --since-compact`, which extracts a clean summary of a previous Codex, Claude Code, Antigravity, OpenCode, or Pi Agent session. Use when the user says "catch up", "what did the last session do", "get me up to speed", "I switched agents", or asks to recover/summarize a previous session before continuing. Do NOT use for the current conversation, git history, or any non-agent log.
A comprehensive mathematical reasoning skill for AI assistants — handles arithmetic to research-level problems with rigorous step-by-step reasoning, systematic verification, and transparent uncertainty handling
