morphaxl/ultragoal

ultragoal

Tell Claude what you want once. It works until the job is verifiably done — and it gets smarter every time.

You talk to it like a person — a messy, unedited voice note is fine. It asks the few questions it can't answer itself, agrees with you on what "done" means, then works on its own: turn after turn, session after session, until an independent reviewer confirms the work holds up. Then it writes down what it learned, so the next goal starts smarter.

Underneath is the goal-loop architecture Anthropic's engineers describe using with Fable 5 — the same one Claude Code ships natively as /goal: don't steer the model prompt by prompt, design a loop where it self-corrects against honest feedback and manages its own memory. The architecture is proven and the model is built for it. What ultragoal packages is everything the published workflow still assumes an expert does by hand: write a rubric where every line is checkable, wire up an independent verifier, keep a memory discipline, make the goal survive the session. Goals on steroids — the standard architecture, with the expertise built into the harness.

Every mechanism in the loop is research-backed — verifier design, evidence ledgers, rubric architecture, memory provenance all trace to published results from Anthropic, DeepSeek, Alibaba, ByteDance, Tencent, and academic agent-systems work. The full mechanism→evidence map lives in docs/research-foundations.md, fed by dated research sweeps in docs/research/.

  BRIEF ──► GOAL ──► LOOP ──► VERIFY ──► DISTILL
   │          │        │         │           │
   ramble    spec    work     fresh-eyes   memory
   (voice)  +rubric  turns    subagent     grows
                        ▲                    │
                        └──── consult ◄──────┘    next session starts smarter

Four parts keep each other honest:

• A real definition of done. Every goal becomes a spec whose rubric is che