Implementation

Verification-driven coding with tight feedback loops. Distilled from 21,321 tracked operations across 64+ projects, 612 debugging sessions, and ~600 transcribed working sessions from the Apr–Jul 2026 corpus. These are the patterns that consistently ship working code.

Core insight: Verify in tight loops, roughly every 2-3 edits — 73% of fixes go unverified across the dataset, the single biggest quality gap. And proof lives where the artifact is consumed, not where it was produced: green producer gates are necessary, never sufficient.

How to read this skill: the loop and the heuristics below are calibrated for non-trivial implementation work. Trivial fixes (config, typo, single-line) shouldn't drag through five phases. Use judgment, scale planning to scope, and skip what doesn't apply. The Code Discipline section is principles that bias toward caution; for one-line changes, just make the change.

The Loop

Implementation work composes from five things that must all be true by the time a chunk ships. Not a fixed order; the graph below shows the natural dependencies and the loop-backs that are routine in practice.

digraph implement {
    rankdir=LR;
    node [shape=box];