Objective Loop

Objective Loop

The toolkit's iterate-until-verified-done loop. A user states an objective with verifiable done-criteria; each iteration routes one /do cycle, verifies the criteria by executing them, and reschedules itself via ScheduleWakeup until verified-done or budget-stop. This skill is a planner/verifier wrapped around the /do router — it executes no work inline. Objectives over tasks: describe what done looks like and how to verify it; the loop finds the path.

Phase 1: SPEC

Gather the objective spec from the request. Interview only for missing fields.

DONE-CRITERIA are verifiable checks. Each criterion has a type: command (default, preferred) or rubric.

• command — a deterministic command with an expected exit code/output: pytest -q exits 0; gh pr view N --json state -q .state prints MERGED; validate-doc-counts.py reports zero drifts. A criterion the model reasons about is not a criterion; each needs a command plus an expected observable.
• rubric — allowed only where no mechanical check exists, per the PHILOSOPHY.md verification ranking (exit code > fresh-context grader > self-critique). Store the rubric verbatim in the state file at SPEC time: pass conditions plus the evidence the grader must cite. Frozen once the loop starts — changes require the user, same as a guardrail.

NOT-DONE-YET guardrails name what may never be done to satisfy a criterion (e.g. "never weaken a gate to make it pass"). They bind every iteration: inject them verbatim into each /do dispatch.

Gate: spec complete. Proceed to Phase 2.

Phase 2: STATE

Write .objective/<slug>/state.md from the template in references/state-file.md: objective, criteria table, guardrails, per-iteration log, next planned step.

• Wakeups resume FROM THE STATE FILE, never from conversation memory — the wakeup prompt carries only a pointer.
• .objective/ mirrors .feature/'s ephemerality but stays separate: .feature/ is feature-lifecycle's phase machine, managed only by feature-state.py, and its presence reroutes /do into feature phases. Objectives are arbitrary goals.
• State is session working memory — keep .objective/ unstaged; stage repo files by name only.

Gate: state file written. Proceed to Phase 3.

Phase 3: ITERATE — one /do cycle

Plan the smallest next step toward the unmet criteria, then route it through the /do phases: classify → route → dispatch agents → evaluate. The loop dispatches work exclusively through /do — catching yourself editing or analyzing inline means stop and route. Multi-part objectives may dispatch parallel agents per /do's rules.

Learning capture is automatic. Every iteration dispatches through /do, so routing rows and outcomes record via the /do learning hooks. Add no manual capture.