A command is the one you type. It runs exactly when you ask it to, and never before.
60 commands across 626 plugins.
Remove menhera-loop UI settings — clingy by default, clean with --farewell.
Record a brief Architecture Decision Record (ADR) about a recent technical choice.
Audit a BigQuery SQL query or dbt model for potential cost traps before it hits production.
Run automated tests and build checks first, then agent code review. For each bug found, propose or document a regression test.
Walk through a file, function, class, or module and explain what it does, how it works, and why it is structured the way it is. Suited for onboarding onto…
Use when a build, type check, or test suite is failing and needs to be unblocked with a minimal change.
Run a systematic cleanup on the provided code to resolve smells, simplify complexity, and enforce best practices.
Review source files changed during a phase for bugs, security issues, and code quality problems
Isolate a bug report into a minimal, standalone reproduction script or test case.
Review or design a data pipeline architecture based on requirements
Build a new analytics feature or dbt model guided by business requirements and data exploration
Perform a technical debt audit on the specified components and generate a prioritized remediation roadmap.
Systematically analyze a reported error, stack trace, or failure state to identify the root cause and propose a fix.
Gather phase context through adaptive questioning before planning.
Audit the repository's documentation for broken paths, missing setup commands, and stale code blocks.
Generate a comprehensive evaluation report for a trained model, highlighting performance, baselines, and subgroup metrics.
Audit an ML training script or pipeline for reproducibility, tracking, and basic hygiene before a long run.
Analyze codebase with parallel mapper agents to produce .planning/codebase/ documents
Use to check MCP server prerequisites before activating or using a server.
Use to verify an MCP server starts and responds before adding it to an agent config.
Build or refresh graph-backed memory for a corpus, using graphify first and helper conversions only when they improve corpus quality.
Choose the cheapest context path across direct read, markdown conversion, graph build, graph update, or graph query.
Query an existing graphify graph before rereading the full corpus, using query, path, or explain flows.
Review a machine learning training run or experiment configuration for soundness, reproducibility, and evaluation rigor.
Recommend the best model tier for the current task based on complexity, risk, and budget.
Perform a comprehensive pre-PR check: summarize changes, analyze risk, and generate a review checklist.
Execute a comprehensive quality gate check (lint, test, build, format) before allowing code to merge.
Run formatter, linter, and type checks for a file or directory. Report remediation steps.
Review the README.md to ensure it functions as an executable contract for new developers.
Draft a step-by-step refactoring strategy for complex code changes to ensure behavior is preserved.
Run the complete release readiness pipeline (version checks, changelog, final quality gates) and assign a release confidence score.
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