Find agent skills by outcome

[turn 2] assistant: Use flexbox: display flex

justify-content center

Introduces consequences

temporality

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

assumptions

Data

corpus_character: In this dataset

the right question is one that shifts the frame from doing or describing toward understanding oneself and the meaning beneath the problem.

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

mechanical_signals: [string

string

M4: What do you do instead? → behavioral detail

no meaning redirect

transformative_signals: [string

string

Coding & Debugging

- (Derived from this dataset

not hardcoded)

M3: And Germany? → sequential facts

same epistemic stance

M2: What accomplishments would you highlight? → checklist

no perspective change

{ axis: emotional/self-reflective depth

weight: primary }

{ axis: epistemic stance (facts vs examining assumptions)

weight: secondary }

{ axis: intent (instrumental vs exploratory)

weight: secondary }

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.

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

consequences

mechanical_pattern: Mechanical questions maintain the same conversational frame by clarifying

extending

question: Sure

what's it about?

[assistant]: Sure

what's it about?

{ axis: intent (instrumental vs exploratory)

weight: secondary }