Find agent skills by outcome

decision_question: Does this question redirect the conversation from executing or describing a task...

assumptions

transformative_signals: [string

string

mechanical_signals: [string

string

Coding & Debugging

- (Derived from this dataset

not hardcoded)

M4: What do you do instead? → behavioral detail

no meaning redirect

M3: And Germany? → sequential facts

same epistemic stance

M2: What accomplishments would you highlight? → checklist

no perspective change

{ axis: epistemic stance (facts vs examining assumptions)

weight: secondary }

M1: Sure

what's it about? → task clarification

{ axis: problem framing (surface task vs underlying meaning)

weight: primary }

{ axis: intent (instrumental vs exploratory)

weight: secondary }

{ axis: emotional/self-reflective depth

weight: primary }

Mechanical questions clarify/extend within existing frame without prompting reflection on meaning

assumptions

Mükemmel! Şimdi Chain 4'e geçelim - Lens oluşturma. Bu chain

önceki tüm çıktıları alıp bu veri setine özgü bir lens oluşturacak.

mechanical_pattern: Mechanical questions maintain the same conversational frame by clarifying

extending

question: Sure

what's it about?

why_mechanical: The question elicits behavioral details but does not yet redirect attention toward m...

consequences

[assistant]: Sure

what's it about?

{ axis: epistemic stance (facts vs examining assumptions)

weight: secondary }

{ axis: intent (instrumental vs exploratory)

weight: secondary }

{ axis: emotional/self-reflective depth

weight: primary }

{ axis: problem framing (surface task vs underlying meaning)

weight: primary }

Questions redirect from surface behaviors toward internal meaning

causing reinterpretation as self-understanding rather than task execution.

Data

This is excellent! The GPT is producing high-quality outputs. The transformation pattern is very ins...

causing the speaker to reinterpret the problem as one of self-understanding rather than task execution.