Extended Thinking Architect

Category: Engineering Domain: AI Engineering

Overview

The Extended Thinking Architect skill helps you decide when an LLM task should spend a reasoning/thinking budget, how much (no-thinking / low / medium / high), and when the better move is a cheaper model with a sharper prompt instead. It turns task signals — error cost, ambiguity, step count, latency budget — into a deterministic recommendation with a rough cost multiplier, and allocates effort across the phases of an agent loop so you front-load reasoning where it pays and avoid runaway budgets.

Clarify First

Before recommending an effort level, confirm these inputs. If any is unknown or vague, ASK — do not assume:

• Task type & verifiability — what the model is actually doing (extraction, classification, planning, code-debug, math…) and whether the output is checkable (sets --task-type and --verifiable)
• Cost of a wrong answer — how expensive a bad output is, plus the latency budget the task must fit (sets --error-cost and --latency-budget)
• Shape of the work — how many reasoning/tool steps are expected and how ambiguous the request is (sets --steps and --ambiguity)

Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact.