Collaborative alignment discussion with the human. Use after /convergence-research (or directly for smaller features). Produces a ~200-line design document. The human makes all design decisions — the agent surfaces assumptions, patterns, and open questions.
Define and codify project-level architectural invariants, scope boundaries, and non-negotiable rules. Produces ARCHITECTURE.md — the persistent constraint file that shapes every AI interaction. Use once per project or when architectural drift is detected.
Execute outline phases with verification checkpoints. Use after /convergence-outline approval. Works through each phase sequentially: write tests first, implement, verify, commit. Stops and asks the human if 3+ fix attempts fail on any phase.
Code review on the actual diff against the base branch. Use after implementation or when asked to review a PR. Two-stage review: correctness (did we build the right thing?) then quality (did we build it right?). Defaults to NEEDS WORK until evidence proves readiness.
Architecture analysis with quantified quality gates. Use for codebase health checks, onboarding, or before major refactors. Detects layer violations, scores callbacks, finds god objects via churn x complexity, and checks code quality thresholds.
Capture what was learned after solving a bug or completing a feature. Reads recent git context, drafts a structured learning, and asks the human to correct or approve. Writes searchable artifacts to docs/convergence/learnings/.
Systematic root cause investigation. Use when encountering any bug, test failure, or unexpected behavior. Four phases: investigate, analyze patterns, hypothesize, fix. No fixes without root cause. If 3+ fixes fail, stop and question the architecture.
Objective codebase exploration. Use before designing a new feature or change. Produces a compressed, fact-only research document. The research context never sees the ticket — a separate step generates questions, this skill answers them.