Results for “turborepo”

Turborepo monorepo build system guidance. Triggers on: turbo.json, task pipelines, dependsOn, caching, remote cache, the "turbo" CLI, --filter, --affected, CI optimization, environment variables, internal packages, monorepo structure/best practices, and boundaries. Use when user: configures tasks/workflows/pipelines, creates packages, sets up monorepo, shares code between apps, runs changed/affected packages, debugs cache, or has apps/packages directories.

Navigate, manage, and optimize monorepos with tools like Turborepo, Nx, pnpm workspaces, and Lerna. Enables cross-package impact analysis, selective builds and tests, remote caching, dependency graph visualization, and structured multi-repo to monorepo migrations.

Use in TURBO mode when Codex should compare top current solutions before choosing architecture, UI components, 3D stack, mobile stack, SEO/PPC strategy, or programming patterns.

Nx smart monorepo build system reference. Covers affected commands, computation caching, project graph visualization, nx.json and project.json config, code generators for React/ Angular/Node/Next.js, module boundary enforcement, Nx Cloud remote caching, and migration from Turborepo.

Detect pnpm workspace structure, optional Turborepo overlay, package metadata, and internal dependency graph for monorepo-aware skills

Monorepo Navigator

Use when the user explicitly enters TURBO, mode TURBO, enable TURBO, or TURBO for this task, and Codex should optimize for maximum quality without normal token-cost restraint.

Provides a repeatable, auditable workflow for large coding tasks by initializing repo-local task artifacts, installing subagent templates, and running a spec-freeze → build → evidence → verify → fix loop with fresh-session verification.

Use in TURBO mode before finalizing implementation, architecture, UI/UX, 3D web, mobile, SEO/PPC/growth, or programming work.

Next.js 16+ and Turbopack — incremental bundling, FS caching, dev speed, and when to use Turbopack vs webpack.

Monorepo Navigator

Next.js 16+ and Turbopack — incremental bundling, FS caching, dev speed, and when to use Turbopack vs webpack.

Autopsy a stuck project, extract salvageable artifacts, and route to a fresh start via $bootstrap-repo reset or new-repo bootstrap

Turbine — small Kotlin testing library for kotlinx.coroutines Flows. Provides ergonomic API to test Flow emissions deterministically: awaitItem, expectMostRecentItem, awaitComplete, awaitError. Works with StateFlow, SharedFlow, Channel-backed flows, combine/map/debounce. KMP-friendly. USE WHEN: user mentions "Turbine", "app.cash.turbine", ".test {}", "awaitItem", "Flow testing", "StateFlow test", "SharedFlow test", "expectMostRecentItem", "cancelAndIgnoreRemainingEvents" DO NOT USE FOR: Mobile E2E - use `testing/maestro` DO NOT USE FOR: Compose snapshot - use `testing/compose-snapshot` DO NOT USE FOR: Generic Kotlin testing - use `testing/kotest` DO NOT USE FOR: Suspend function (non-Flow) testing - use `kotlinx-coroutines-test` directly

Analyzes a local codebase to infer its actual architecture from code-first evidence, producing a dominant architecture label, coexisting patterns, drift, and repo-fit advice for downstream agents.

Optimizes Next.js 16+ development with Turbopack for faster bundling, caching, and dev startup, and clarifies when to use Turbopack vs webpack.

Repo housekeeping: sync READMEs to match current architecture, remove dead files (logs, screenshots, deprecated code, build artifacts), update npm/pip/cargo dependencies to latest. Use when asked to: "housekeep", "clean up repo", "update README", "update dependencies", "fix vulnerabilities", "remove dead code", "tidy.

Parallel execution engine for high-throughput task completion

Free offline desktop video/image compression (Tauri+React) — batch compress, trim/split, convert formats, embed subtitles. Install: brew install --cask codeforreal1/tap/compresso

Next.js 16+ and Turbopack — incremental bundling, FS caching, dev speed, and when to use Turbopack vs webpack.

Detect monorepo structure, identify shared chokepoints, and generate safe parallel lane specs aligned to package boundaries

Expert guidance for GRPO/RL fine-tuning with TRL for reasoning and task-specific model training

Codebase-wide Retrieval-Augmented Generation for deep code understanding. Use when: (1) Answering questions about large codebases by searching across all files, (2) Finding related code patterns, implementations, or dependencies across a project, (3) Building context from multiple files before making changes, (4) Understanding how a feature works end-to-end across the codebase, (5) Tracing data flow through multiple modules

Regenerate the cross-repo roadmap aggregate in your Claude memory directory by walking every curated repo's `docs/specs/**` frontmatter. Run at session start for cross-repo orientation or after any `spec-new`/`spec-ship` in any repo.

Sets up Microsoft Testing Platform hot reload to iterate fixes on failing tests without rebuilding, covering package installation, environment configuration, and the iterative workflow.

Diseña estrategias de backup, disaster recovery, RPO/RTO, restauración y continuidad de negocio para servicios y bases de datos.

Expert guidance for GRPO/RL fine-tuning with TRL for reasoning and task-specific model training

Fine-tune and align language models using reinforcement learning with TRL, including SFT, DPO, PPO, GRPO, and reward model training.

Parallel execution engine for high-throughput task completion

Python package reorganization with pyproject.toml inside package directory

RF Toolbox and RF Blockset in MATLAB -- S-parameter I/O, network conversions (S/Z/Y/ABCD/T/H/G, mixed-mode), cascade/de-embedding, rfbudget analysis, circuit composition, matching networks, amplifier stability, mixer spurs, rational fitting, SI channels, baseband processing, Circuit Envelope simulation. Trigger: sparameters, Touchstone, .s2p, .s4p, rfplot, smithplot, rfparam, rfwrite, zparameters, yparameters, abcdparameters, s2sdd, cascadesparams, deembedsparams, rfbudget, noise figure, OIP3, IIP3, amplifier, modulator, nport, rffilter, attenuator, seriesRLC, shuntRLC, lcladder, txline, circuit, setports, clone, matchingnetwork, stabilityk, stabilitymu, powergain, gammams, gammaml, mixerIMT, OpenIF, rational, rationalfit, stepresp, txlineWRLGC, rf.Amplifier, rf.Mixer, rf.Filter, rf.Sparameter, rfsystem, RF Blockset.

Train Mixture of Experts (MoE) models using DeepSpeed or HuggingFace, covering architectures, routing, load balancing, and expert parallelism.