Workshop to build the case
This is where you compose the file: issue, interpretive focus, relational actors, social entities, contexts, environments, prompts, and external AI outputs.
The problem-solving layer no longer lives in one confusing screen. SELFTRACE now separates the workshop where you build the case, the case inventory, and the dedicated card for each file, so you can think through a real problem without mixing composition, history, and reading.
Solving a problem no longer means filling an empty box. The system starts from your map and lets you organize the case with reusable pieces, prompts, and clearer outputs.
This is where you compose the file: issue, interpretive focus, relational actors, social entities, contexts, environments, prompts, and external AI outputs.
This is where you view and filter worked cases. It helps you compare, resume, and not lose the problems that have already gone through the system.
This is where you read the built file without editing noise. It is the useful piece for review, sharing, or revisiting the case with distance.
Apply starts from your identity map, active tensions, and observed signals. That is why the case does not start as generic advice, but as a situated reading of how you function.
You can now add relational entities, social entities, contexts, and environments. Each one adds stable context to the file and avoids having to re-explain the case world every time.
The case can be read inside SELFTRACE, exported as a prompt, or supported by external AI. AI expands the language; the analysis structure still comes from the map and the assembled file.
The current logic is: `identity map + case pieces + situated reading + reusable output`. That produces something much more useful than a general AI working from a text box without context.
Reusable libraries are now a central part of the system: they help the case start better assembled and with less ambiguity.
These let you save key people and reuse them across cases, with card, workshop, and auxiliary actions.
They add density to the file when the problem involves collectives, scenarios, or stable frames worth preserving.
Prompts should no longer feel like mysterious buttons: they work as bridges to research, import, or expand language depending on the case piece.
SELFTRACE can generate prompts that already include interpretive focus, active identity, relational pieces, and the case frame. That prevents another AI from starting blind or improvising the context.
The healthy rule is this: use SELFTRACE to detect and structure; use external AI to expand language, rehearse a conversation, generate options, or convert the case into another useful output.
One of the most important advantages is that the system can now live as a serious case file, not only as a self-knowledge demo. That makes it much more presentable to organizations, consultants, and teams.
The system is now better prepared for this thanks to relational entities, social entities, contexts, and environments. Even if not every crossing is fully automated yet, the final architecture already supports richer work across several human and situational pieces.
That matters for couples, direct authority, family, work conflict, mediation, and cases where the problem does not live in only one person but in a bond or in a system.
A stuck person does not always need more generic information: they need to understand how their own identity is participating in what they are living. When that piece becomes visible, resolution stops being abstract and becomes more actionable.
That is the value of SELFTRACE: turning identity reading into practical understanding, contextual resolution, and better-oriented decisions.