mintables

Work autonomously on a project without waiting for direction. On command, survey the codebase, pick one high-value low-risk improvement, do it, verify it, commit it, then pick the next, looping like a self-directed intern. Use when the user hands off open-endedly ("work on your own", "keep improving this", "find something worth doing", "don't wait for me", "go autonomous", "run for a while", "be a passionate intern", "do whatever you think is valuable") or invokes /autopilot, and you have to choose the work yourself rather than wait for instructions or feedback.

Author, validate, and maintain knowledge for AI agents as conformant Open Knowledge Format (OKF) bundles, Google's vendor-neutral spec (v0.1) of markdown files with YAML frontmatter for portable agent context. Provides commands init, add, enrich, link, index, log, validate, export, and consume over a bundle. Use when the user types /okf or asks to start an OKF bundle, document a BigQuery table, dataset, view, metric, runbook, playbook, API, or join path so an agent can read it, make internal knowledge agent-readable or portable, export a data catalog, metadata, or webpages and external URLs to OKF, or follow the OKF spec. Also apply implicitly whenever you write, transform, or export knowledge an agent will later read (table and metric docs, runbooks, deprecation notices, schemas, docstrings), so it ships as a conformant bundle with a type field per concept instead of ad-hoc prose, and whenever you edit an existing OKF bundle, keeping it conformant by refreshing timestamps, log.md, index.md, and cross-links.

Write and review React code as if the React Compiler is enabled. Skip manual useMemo, useCallback, and React.memo by default, and audit existing code for stale manual memoization. Use when writing or refactoring any React component in a React 19+ codebase, when reviewing a React PR, when setting up the eslint-plugin-react-hooks configuration, or when seeing memoization patterns in code an LLM produced. Counterweight to pre-compiler React in LLM training corpora, which biases default output toward manual memoization unless prompted otherwise.

Detect React and TypeScript maintainability smells across the whole component, hook, and module, explain the cost of each, and propose a fix with a source link. Covers prop and API design (naming, booleans, callbacks, variants, controlled state, generics, refs, styling, JSDoc), state and data flow (redundant state, stale closures, context and ref misuse), effects and lifecycle, component structure and hooks, rendering correctness (keys, mutation, impure renders, hydration, the leaked 0 from &&), accessibility and focus management, async handlers, error boundaries, unsafe HTML, TypeScript discipline (any, casts, untyped catch, exhaustiveness), and a cross-file duplication pass. Defers memoization and re-render performance to react-compiler and color literals to theme-colors. Use when reviewing or writing React components or hooks, auditing for maintainability, or asked to sniff, smell-check, lint, or clean up a codebase, folder, file, or pasted snippet. Respects native HTML attributes and library conventions.

Audit and refactor UI code so every color value comes from the theme. Replace hex codes, rgba(), hsl(), oklch() literals, and named CSS colors with theme palette tokens (primary, secondary, error, warning, info, success, background, text) and the alpha() helper for transparency. Use when writing or reviewing any UI component, when starting a new MUI or design-system theme, when a designer hands off colors as hex annotations, when seeing color literals scattered across component files, or when an LLM produced styling with hardcoded hex values. Counterweight to LLM training corpora dominated by tutorial code that inlines hex codes and rgba literals instead of role-based theme tokens.