ai slop moderation audit

The problem

Existing moderation tools rely on AI-text detectors that suffer from high false-positive rates, especially for non-native English speakers. When moderators attempt to "catch AI," they often end up alienating real community members or drowning in a manual review queue that lacks prioritized signals.

What it does

• Evaluates existing moderation posture to identify gaps in platform-specific data visibility.
• Designs behavioral-scoring systems based on account age, posting cadence, and engagement asymmetry rather than linguistic analysis.
• Architects one-tap human-in-the-loop review workflows to replace blunt auto-ban triggers.
• Maps every recommended signal to its specific false-positive failure mode to prevent community distrust.
• Establishes measurement loops to track and tune signal drift over time.

Why this beats prompting it yourself

General-purpose LLMs instinctively suggest AI-text detection, which is easily bypassed by "humanizer" tools. This skill enforces a strict behavioral-only framework that protects your community's reputation by ensuring every automated trigger is backed by a specific friction-reduction strategy and a named failure mode.

Use cases

• Designing a spam-mitigation strategy for a growing Discord server.
• Auditing a subreddit's moderation rules to reduce moderator burnout.
• Building a custom behavioral scoring engine for a niche Discourse forum.
• Transitioning from high-risk auto-moderation to a prioritized review queue.

Known limitations

This skill is sized for individual community moderation, not enterprise-scale platform trust-and-safety. It does not handle legal content-liability audits or build linguistic detectors.