# Abuse Prevention

> Abuse prevention - rate limiting, moderation, bad actors. Use when fighting abuse.

- Skill: `diegosouzapw/abuse-prevention` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add diegosouzapw/abuse-prevention`
- Raw SKILL.md: https://api.skillmd.com/api/skills/diegosouzapw/abuse-prevention/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: diegosouzapw (https://skillmd.com/u/diegosouzapw)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/diegosouzapw/abuse-prevention

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# Abuse Prevention Guideline

## Tech Stack

* **Analytics**: PostHog
* **Database**: Neon (Postgres)
* **Workflows**: Upstash Workflows + QStash

## Non-Negotiables

* All enforcement actions must be auditable (who/when/why)
* Appeals process must exist for affected users
* Graduated response levels must be defined (warn → restrict → suspend → ban)

## Context

Trust & safety is about protecting users — from each other and from malicious actors. Every platform eventually attracts abuse. The question is whether you're prepared for it or scrambling to react.

Consider: what would a bad actor try to do? How would we detect it? How would we respond? What about the false positives — innocent users caught by automated systems? A good T&S system is effective against abuse AND fair to legitimate users.

## Driving Questions

* What would a motivated bad actor try to do on this platform?
* How would we detect coordinated abuse or bot networks?
* What happens when automated moderation gets it wrong?
* How do affected users appeal decisions, and is it fair?
* What abuse patterns exist that we haven't addressed?
* What would make users trust that we're protecting them?