AutoMod and Risk Score

Skool uses a member risk score and AutoMod to help you spot likely spam across membership requests and community activity. Treat these tools as a safety net, then use human judgment before you reject, remove, or restrict a real person.

What it is

The member risk score gives you an extra signal when you review a membership request.

AutoMod is active in all communities. It flags high-risk users and the posts and comments they publish, for an admin to review. It does not scan direct messages; Skool's documented DM defences are the Unlock Chat at Selected Level plugin and members blocking and reporting chat spam.

The system has changed over time and may continue to change. A risk signal is useful, but it is not proof that someone has bad intentions.

How it works

• Review the risk signal alongside the person's answers and the fit with your community.
• Watch direct messages as well as public posts and comments, because spam can begin privately.
• Let AutoMod catch obvious patterns, then review uncertain cases yourself.
• Keep a simple record of false positives and missed spam so you can improve your moderation habits.
• Use About Page And Discovery Card Packaging to set clear expectations before people request access.

What you should do

1. Write clear rules for promotion, direct messages, and acceptable conduct.
2. Keep AutoMod enabled when it helps your group, but do not let it replace moderator review.
3. Look at the whole request before deciding, especially when the score and the written answers disagree.
4. Check reports of unwanted direct messages quickly, because private spam can damage trust.
5. Explain how members can report a problem and contact an admin.
6. Review repeat incidents for patterns without treating every unusual member as a threat.
7. Look at the moderation settings in your own group before you write a fixed procedure, because Skool's controls change over time.

Best practice

• Pair automated detection with a named human reviewer.
• Apply the same rules consistently to low-risk and high-risk members.
• Separate spam signals from disagreement, criticism, or an unfamiliar writing style.
• Protect the regular members who make the community useful, as described in Ten True Regulars And Community Ops.
• Connect moderation review to your normal Retention Community Cadence.

Pitfalls

• Banning someone only because of a score.
• Assuming a low score guarantees good behavior.
• Ignoring direct messages because public posts look clean.
• Writing a permanent workflow around labels that may change in the product.
• Treating community-fit judgment as if it were spam detection.