Claude-skill-registry abuse-prevention
Abuse prevention - rate limiting, moderation, bad actors. Use when fighting abuse.
install
source · Clone the upstream repo
git clone https://github.com/majiayu000/claude-skill-registry
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/majiayu000/claude-skill-registry "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/data/abuse-prevention" ~/.claude/skills/majiayu000-claude-skill-registry-abuse-prevention && rm -rf "$T"
manifest:
skills/data/abuse-prevention/SKILL.mdsource content
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?