Curie Measurement Prover Connector for AI agents.

It forces your AI agent to provide actual numbers instead of just saying something is faster. It requires baselines, delta percentages, and specific testing methods for every claim.

Yes, it ensures that when you refactor code, the agent has to prove which specific change caused the improvement by isolating variables one at a time.

It is a reasoning framework for empirical rigor. It doesn't just analyze data; it forces the agent to follow a strict scientific method of measurement, isolation, and validation.

It prevents the agent from giving vague safety assurances. It requires the agent to list every danger, assign a probability, estimate the impact, and provide a mitigation plan.

Yes, by forcing the agent to document systematic investigations. It prevents the agent from giving up after one try and requires it to log every variation tested.

You should use it if you need to move from 'I think this works' to 'I have proven this works.' It's for high-stakes engineering where accuracy and evidence are required.

No. Curie's method applies to any domain requiring empirical validation. process improvement (measure before/after cycle times, isolate each change), vendor evaluation (measure cost/quality/reliability, not 'it seems better'), risk assessment (quantify probability and impact, not 'the risk is minimal'), method selection (benchmark each candidate in isolation), controlled experiments (single variable, controlled conditions). Anywhere you would say 'better' or 'faster' or 'more reliable,' replace the adjective with a number.

Sometimes variables are genuinely coupled. changing the supplier requires changing the delivery schedule. The engine does not demand artificial isolation. It demands AWARENESS of what was changed together and WHY isolation was impractical. Document: 'We changed X and Y together because X requires Y. We cannot isolate their effects. We accept that the 67% improvement is from X+Y combined, with Y alone contributing approximately 15% based on a separate controlled test.' Honest documentation of coupled changes is acceptable. Pretending 3 changes are one is not.

Watt validates EFFICIENCY ENGINEERING. finding waste, instrumenting baselines, designing feedback loops, isolating bottlenecks, quantifying improvements. It asks 'where is the bottleneck?' Curie validates EMPIRICAL RIGOR. measuring instead of claiming, isolating variables, cross-domain validation, persistence, risk quantification. It asks 'where is the number?' Watt finds WHERE to optimize. Curie proves THAT you optimized. Use Watt to identify bottlenecks. Use Curie to prove your fix actually worked. with numbers, not adjectives.