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Query code semantics (symbols, hover, definitions, references, diagnostics) across any language via the lsmcp MCP server, which bridges through kakehashi to real language servers (lua_ls, basedpyright, rust_analyzer, r_language_server, nixd, …). Use when exploring or analyzing a codebase, finding/understanding symbols, or when the first project-wide search misses a language. Prefer these tools over Grep/Read for code analysis.

Run the full multi-stage review pipeline on the current branch — /review (multi-perspective subagents) → codex MCP → PR → Copilot/Gemini bots — fixing findings at each stage until convergence. Use when the user asks to "revise", run the review pipeline, or prepare a branch/PR for merge review.

Run a strict multi-perspective review with fact-checked findings. Use when the user asks for a review, code review, design review, risk review, or wants independent reviewer perspectives before merging or shipping.

Perform deep multi-perspective analysis with problem-specific analytical lenses, cross-validation, and a final recommendation. Use when the user asks to ultrathink, deeply analyze a complex problem, compare strategic options, expose risks, or make a high-impact technical decision.

Stage meaningful diffs and create Conventional Commits with WHY-focused messages. Use whenever making git commits, grouping changes, choosing commit types, or separating structural and behavioral work.

Guide structural code improvements using Kent Beck's Tidy First methodology. Use when seeing messy code, before making behavioral changes, after completing features, documenting deferred cleanup, or discussing when to clean up code.

Guide Test-Driven Development using Kent Beck's Red-Green-Refactor cycle. Use when writing tests, implementing features via TDD, entering RED/GREEN/REFACTOR phase, or following plan.md test instructions.

Guide design decisions using the State > Coupling > Complexity > Code priority framework. Use when evaluating tradeoffs, reviewing design choices, deciding between implementations, or when code volume seems to be prioritized over deeper concerns.

AI Scrum Master facilitating events, enforcing framework rules, coaching team, and triaging impediments for human escalation. Use when coordinating sprints, escalating impediments, or ensuring Scrum compliance.

Guide Sprint Planning in AI-Agentic Scrum. Use when selecting PBI, defining Sprint Goal, or breaking into subtasks.

AI-Agentic Product Owner accountable for maximizing product value through effective Product Backlog management. Use when creating PBIs, ordering backlog, or accepting sprints.

Transform PBIs into ready status for AI execution. Use when refining backlog items, splitting stories, or ensuring Definition of Ready.

Execute Sprint with inspect-and-adapt cycles. Use when implementing subtasks, adapting plans based on progress, or managing sprint execution.

Maintain scrum.ts dashboard following Agentic Scrum principles. Use when reading and editing scrum.ts, updating sprint status, or managing Product Backlog.

Verify Definition of Done and acceptance criteria for Sprint increments. Use when completing sprints, running verification commands, or preparing for acceptance.

Guide Sprint Retrospectives to identify improvements. Use when reflecting on sprints, planning process improvements, or executing improvement actions.