pr-review

Generate a complete, playable, visually beautiful game in one orchestrated multi-agent workflow. The orchestrator writes an immutable contract + an art-direction style bible, fans the build out across parallel implementers (with dedicated world/structure/character/fx/lighting art agents), runs a review → adversarial-verify → fix gauntlet, then actually runs the game, captures screenshots, and iterates against an art-director judge until it looks great. Stack-agnostic; browser/canvas games are the sweet spot. Composes the contract-first method with front-loaded art direction and a closed screenshot-judge feedback loop.

Test-driven feature-build workflow for an unfamiliar codebase: map it into a navigable wiki and a C4 architecture model, cut the task into vertical walking-skeleton slices (each E2E-testable), drive every slice through /red → /green → /refactor with outside-in TDD, shift-left gating, and atomic feature-scoped commits, then /presubmit (full real gate + parallel multi-agent grading panel). Stack-agnostic.

C4-model documentation tracker with SHA propagation and staleness detection

Research-backed implementation planning with a mandatory clarification dialog. Every open question (undefined terms, ambiguities, scope edges) must be resolved by user Q&A or codebase research before any plan body is written. Produces phased plans at docs/plans/ with SHA-tracked references and an embedded resolved-questions audit table.

Proof-based bug investigation. Forces explicit hypotheses, evidence-based verification, 5 Whys root cause analysis, and falsification check, with a written investigation log at docs/investigations/ before any code change is permitted.

On-demand codebase research. Parallel sub-agents produce SHA-tracked, line-referenced docs at docs/research/. Detects existing research on the same topic and updates instead of duplicating. Companion to docs.

Random plugins I built for myself

Install an opinionated Claude Code statusLine into ~/.claude/settings.json. Layout: <folder> <model> effort: <level> ctx: <human-tokens> / <pct>%. Conditional segments, 1M / 200K context window fallback by model name, non-destructive merge, JSON validation, and dry-run smoke test.