ThumbGate Verification evidence
guide | pre-action checks for ai coding agents

# What Are Pre-Action Checks?

Pre-action gates stop the risky move before the agent executes it. ThumbGate uses thumbs-up/down feedback to decide what should be reinforced, warned, or blocked.

👍 Thumbs up reinforces good behavior
👎 Thumbs down blocks repeated mistakes

## Why this page exists

- Prompt rules are advisory. Pre-action gates are enforcement.
- A repeated thumbs down can become a warning gate or a hard block.
- The right proof asset is not the rule text alone but the evidence that the gate fired before damage.

## Why this matters

Most AI coding failures are not mysterious. They are repeated mistakes: force-pushes, destructive scripts, missed verification steps, or breaking architectural constraints.

A pre-action check turns that failure pattern into a runtime checkpoint. The agent sees the stop before the bad action lands.

## How ThumbGate makes the loop useful

- Capture structured thumbs-up/down feedback.
- Promote repeated failures into prevention rules.
- Score and enforce the rules with Thompson Sampling and pre-action hooks.
- Publish verification evidence so the system is auditable.

## Best next step

If a buyer is exploring the category, this page should move them to either a comparison page or the main product proof pack.

## FAQ

How are pre-action checks different from prompt rules?

Prompt rules ask the model nicely. Pre-action gates intercept the tool call and block it before execution when the known-bad pattern matches.

Can a thumbs up matter too?

Yes. ThumbGate explicitly uses thumbs up to reinforce successful behavior so the system is not only punitive.