Find agent skills by outcome

Bu 6 aylık çalışmanın özü şu an çalışan bir sisteme dönüştü. Hardcoded değil

dinamik. Sokratik. Ve en önemlisi: insan kararını merkeze alıyor.

Yeni: introduces value trade-offs (money vs belonging

loss vs gain)

Whether a question is 'good' or appropriate in a real conversation given emotional

ethical

When to prioritize empathy

timing

Explicitly add value trade-offs (e.g.

loss vs gain

Refine the decision question to emphasize reinterpretation of stakes

not just depth of reflection.

reasoning: Explores intent but stays instrumental

no deep self-reflection.

- Was this captured in the lens

or something new?

reasoning: Contrasts material gain with emotional/identity needs

reframes priorities.

reasoning: The question deepens reflection by contrasting material gain with emotional and identity-level needs

leading to a clear reframing of priorities.

decision_question: Does this question redirect from executing/describing toward examining internal meaning

assumptions

1 Uncertain

3 Mechanical

reasoning: By introducing loss and trade-offs

the question shifts the frame from financial comparison to values and personal meaning.

reasoning: The question explicitly redirects from surface topics to underlying meaning and emotional assumptions

triggering a reinterpretation of the problem.

For each QUESTION

apply the lens. Report only TRANSFORMATIVE and UNCERTAIN.

[turn 3] human: Thanks

what about vertically only?

Introduces consequences

temporality

Decision Question: Does this question redirect from executing/describing toward examining internal meaning

assumptions

uncertain: If the question adds detail or mild depth but does not yet force a reframing of meaning or assumptions

it is uncertain.

mechanical: If the question only gathers information or advances execution within the same frame

it is mechanical.

It requests facts

details

It introduces consequences

temporality

transformative_signals: [string

string

mechanical_signals: [string

string