Inversion Thinking Prover Connector for AI agents.

It forces your AI agent to hunt for failure modes and stress-test your plans. Instead of just looking for ways to make things work, it actively looks for ways things will break, allowing you to build defenses before you deploy.

It is best used during the design and planning phase. It helps you identify architectural flaws and logic gaps before they become production bugs by simulating red-team attacks on your plans.

Unlike a standard capability that might just list risks, this Connector forces a 6-pivot cognitive trap. It requires the agent to state a hypothesis, identify a specific anti-pattern, and simulate a second-order failure for every defense proposed.

It identifies sycophancy. If the agent uses soft language like 'might' or 'could,' the Connector flags it as sycophancy and forces the agent to use deterministic language like 'will crash' or 'will exhaust'.

While it is built for technical architecture, you can use it for any high-stakes decision where you need to identify the worst-case scenario and a measurable metric for failure.

Kill criteria are measurable thresholds, like a specific latency number or a percentage of memory usage, that prove your initial hypothesis is wrong. They move your planning from opinions to engineering metrics.

Because LLMs use modal verbs to distance themselves from critique. True red-teaming requires certainty. The trap forces the AI to say 'This WILL fail because of X'.

An anti-pattern is a structural bad design choice (like storing raw passwords in a DB). A red team attack is an active exploit or failure mechanism (like exhausting memory via connection pooling) that breaks the system. You must define both.

Without measurable metrics, 'failure' is just a subjective opinion. Forcing the agent to define concrete thresholds (such as latency > 350ms, memory usage > 90%, or packet loss > 5%) creates absolute, falsifiable limits. It forces the AI to abandon hand-waving assertions.