abuse-prevention
Abuse prevention - rate limiting, moderation, bad actors. Use when fighting abuse.
Best use case
abuse-prevention is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Abuse prevention - rate limiting, moderation, bad actors. Use when fighting abuse.
Teams using abuse-prevention should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/abuse-prevention/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How abuse-prevention Compares
| Feature / Agent | abuse-prevention | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Abuse prevention - rate limiting, moderation, bad actors. Use when fighting abuse.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# 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?
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