I have been working on https://gametreecalculator.com, which is a canvas on which you can draw a decision tree. Assuming the payoffs you define are zero sum, you can calculate the optimal solution (nash equilibrium) by clicking a button. The code for the "calculator" was pulled from https://actionflop.com, where it's used for GTO poker bots you can play heads up no limit holdem against.
You may be interested in https://actionflop.com/. It lets you play heads up no limit holdem against a game theory optimal bot (solved by CFR). It keeps track of your profit as well as your loss from making mistakes. It's a work in progress, and a coming feature will show you precisely what mistakes you made in each hand.
How do it know that an action given a hand and state is a mistake? It's quite rare for a play to be 0 probability in a strategy, so any action might be considered optimal. The tool need to know your "probability distribution" for the state and action?
Right now, it's only keeping track of the mistakes where you pick a play which you should pick 0% of the time (for example, you should fold or raise but never call). These kinds of mistakes may not be quite as rare as you would think. For example, today, over about 1400 hands played against the bot, players have lost about 1350 in expected value by making these kinds of pure mistakes. This amounts to about 48 big blinds per 100 hands. (Granted, people might be playing haphazardly and not trying to play perfectly.)
I am currently thinking about how to also measure mistakes which involve the player's distribution of choices when the best play is a mixed strategy. One possibility is to keep track of the player's distribution over time. This would probably require too large of a sample size, so one possibility would be to merge similar situations in the game tree when assessing this kind of mistake. Another possibility is to have the player somehow actually choose a mixed strategy when making a decision.
