Most AI coding failures happen before the first line of code. The human shows up with a half-formed idea, the model fills in the gaps with assumptions, and 3 hours later you have working code for the wrong problem.

Ouroboros runs a Socratic interview before execution. It scores ambiguity:

Ambiguity = 1 - Σ(clarity_i × weight_i)

If the score is above 0.2, it keeps asking. No code until the spec is locked.

Once running, it doesn't loop by count — it loops until convergence: Similarity = 0.5 * name_overlap + 0.3 * type_match + 0.2 * exact_match

When similarity hits 0.95 across iterations, it stops. It also detects pathological patterns (stagnation, oscillation, wonder loops) and breaks them explicitly. Built on the Claude SDK with parallel sessions — each node in the AC tree gets its own bounded context so autocompact doesn't silently corrupt state mid-run.

MIT licensed. Would be curious what this community thinks about the convergence threshold choices.