「concurrent」の検索結果

Route a vague Prisma Next prompt to the right specific skill. Use for "help me with Prisma Next", "what is Prisma Next", "explain Prisma Next", "I'm new to PN", "where do I start", "what can I do with Prisma Next", "what can I do next with Prisma", "just ran createprisma", "tour of Prisma Next", "Prisma Next overview", and comparison questions like "Prisma Next vs Prisma 7", "PN vs Drizzle", "PN vs Kysely", "PN vs TypeORM". Do NOT use when the prompt clearly matches a workflow skill — adoption / quickstart / first-touch orientation / brownfield introspection, schema / contract editing, migration authoring (db update / migration plan / migrate), migration review on deploy / concurrent migrations, queries / db.orm / db.sql / TypedSQL, runtime / db.ts / middleware wiring, build / Vite plugin / Next.js plugin, debug / structured error envelopes / PN-* error codes, or feedback / bug report / feature request — load that sibling skill directly.

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Use when developing desktop applications with Qt in C++ - CMake setup, signals and slots, QThread and QtConcurrent, QML integration, model/view programming, Qt5 to Qt6 migration, or windeployqt/macdeployqt/linuxdeployqt deployment

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Use when handling PyQt/PySide6 threading - QThread patterns, QThreadPool/QRunnable, thread safety rules, moveToThread, Qt Concurrent, QTimer, testing threaded code, lifecycle management

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Scaffold an isolated CLOUD dev box per agent (via crabbox + Daytona) for any codebase — the parallel-safe counterpart to dev-local-setup. Each agent gets its own full stack (own DB + dev server) and an in-box browser for e2e, so concurrent loops never collide on ports/state. Sets up the snapshot image, .crabbox.yaml, an idempotent setup.sh (also boots the stack locally), and a cbx.sh wrapper. Use when the user says "set up crabbox", "give each agent its own box", "add cloud testing", "make this repo testable in the cloud / on Daytona", "parallel-test this", or when setup-codebase-harness needs true per-agent isolation.

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Mounts Cloud Storage buckets as a POSIX file system with Cloud Storage FUSE (gcsfuse). Use when interacting with gcsfuse: decide whether FUSE, native gs:// reads, or Filestore/Managed Lustre fits a workload, deploy tuned mounts on GKE, Compute Engine, or Cloud Run, enable and size file, stat, and list caches, tune mount flags or config-file settings, apply workload profiles, keep ML checkpointing safe (rename atomicity, hierarchical namespace/HNS, close-time finalization, concurrent writers), or diagnose slow training, low throughput, or bill spikes with gcsfuse metrics. Covers mount semantics, gcsfuse CLI and config files, GKE gcsfuse CSI driver (Workload Identity principal:// bindings, profile StorageClasses, sidecar sizing), and Cloud Run volume mounts. Don't use for bucket administration or data management without a mount (google-cloud-storage-basics) or fully POSIX-compliant shared file systems (Filestore, Managed Lustre).

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Use when monitoring, diagnosing, or managing Batch Apex, Scheduled Apex, Queueable, and Flow scheduled jobs: Setup > Apex Jobs, AsyncApexJob queries, concurrent limits, failure detection, and notification patterns. NOT for writing the Batch or Schedulable class itself — use apex/batch-apex-patterns or apex/apex-scheduled-jobs. NOT for BatchApexErrorEvent alerting in code — use apex/scheduled-apex-failure-detection-and-monitoring. Trigger keywords: Setup > Scheduled Jobs, Apex Flex Queue, CronTrigger, CronJobDetail, FlowInterview, System.abortJob, System.schedule, CRON expression, Holding status, BLOCKED state, reschedule after sandbox refresh, scheduled job runbook.

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Install, configure, and work with Turso DB — an in-process SQLite-compatible relational database engine written in Rust. Use when the user needs to (1) install Turso DB, (2) create or query databases with the tursodb CLI shell, (3) use Turso from JavaScript/Node.js via @tursodatabase/database, (4) work with vector search or embeddings in Turso, (5) set up full-text search with FTS indexes, (6) configure transactions including MVCC concurrent transactions, (7) enable encryption at rest, or (8) use Change Data Capture (CDC) for audit logging.

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Grind through ready beads in priority order by running several issues at once, each in its own isolated git worktree managed by worktrunk (`wt`), then merging the finished branches back one at a time. Use when asked to work the beads queue in parallel, to run N issues concurrently, or when a serial grind is too slow and the ready issues are independent.

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Grind through ready beads serially in priority order, one at a time in the current worktree, without stopping between issues. Use when asked to work the beads queue, grind the backlog, keep working through ready issues, or make unattended progress overnight. For concurrent worktrees instead, use beads-parallel-grinding.

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Identify independent failures and spawn concurrent agents. Turns sequential N×time into 1×time.

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Run an org-team workflow — a flat pod of up to four peer Claude Code sessions claims lane-tagged slices of an approved spec over the baseline MCP channel and implements them concurrently, each in its own git worktree. Each peer DECIDES its own in-lane implementation choices in its own main context (Article X); only un-decidable or cross-lane forks escalate peer→lead→human (yield_fork for task-bound forks, ask_lead for free-form queries). Opt-in and OFF by default (velocity.org_mode.enabled). Graduates the retired sprint-dispatch prototype into the constitutionally-sanctioned org-team model (Article X — Multi-session coordinated workflows). Requires git.

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Comprehensive guidance for implementing asynchronous Python applications using asyncio, concurrent programming patterns, and async/await for building high-performance, non-blocking systems.

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Use when running analytical SQL over Parquet/CSV/JSON without a warehouse, replacing pandas for data wrangling, joining S3 data in-place, building local data marts, or embedding OLAP into an app. Triggers: read_parquet/read_csv setup, partitioned dataset queries, hive partitioning, glob patterns for S3, COPY TO export, attach Postgres/MySQL, UDFs in Python/R, MotherDuck cloud sync, columnar performance vs row stores. NOT for OLTP workloads (concurrent writes), distributed analytics at petabyte scale (use Spark/Trino), or vector search (use pgvector/Lance).

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Executes DAG workflows with parallel wave processing, agent spawning, context isolation, permission enforcement, and full execution tracing. Use when running a planned DAG, managing concurrent agent execution, enforcing isolation boundaries, or tracing execution for debugging. Activate on "execute DAG", "run workflow", "spawn agents", "parallel execution", "execution trace", "agent isolation". NOT for planning DAGs (use dag-planner), validating outputs (use dag-quality), or matching skills (use dag-skills-matcher).

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Foundational concurrent computation model where actors communicate exclusively through asynchronous message passing

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Extract clean markdown from any URL, including JavaScript-rendered SPAs. Use this skill whenever the user provides a URL and wants its content, says "scrape", "grab", "fetch", "pull", "get the page", "extract from this URL", or "read this webpage". Handles JS-rendered pages, multiple concurrent URLs, and returns LLM-optimized markdown. Use this instead of WebFetch for any webpage content extraction.

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Bulk extract content from an entire website or site section. Use this skill when the user wants to crawl a site, extract all pages from a docs section, bulk-scrape multiple pages following links, or says "crawl", "get all the pages", "extract everything under /docs", "bulk extract", or needs content from many pages on the same site. Handles depth limits, path filtering, and concurrent extraction.

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Use when designing, implementing, reviewing, debugging, testing, profiling, or porting concurrent, parallel, distributed, high-performance, network dataplane, kernel-bypass, or real-time software, including locks, semaphores, monitors, MPI, CSP rendezvous, DPDK, VPP, eBPF, XDP, AF_XDP, SR-IOV, RSS, RoCEv2/RDMA, RMS/EDF, priority inversion, and environment selection from silicon to edge.

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Brief on concurrent sessions — who else is working, in which repos, on which files, and what landed underneath you. Use when a commit appears that you did not make, before touching a shared file, or when orienting mid-task in a busy workspace.

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Upgrade React applications to latest versions, migrate from class components to hooks, and adopt concurrent features. Use when modernizing React codebases, migrating to React Hooks, or upgrading to latest React versions.

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Leverage concurrent terminal background processes to debug multiple independent failures simultaneously using ACH methodology. Use when you have 2+ failures across different subsystems.

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Orchestrate concurrent analysis or implementation across independent domains. Use when a task can be decomposed into independent workstreams (e.g., via simultaneous tool calls or background processes) — spec writing, code review, security audit, feature implementation.

日本語の概要は準備中です。原文の説明を表示しています。

Comprehensive guidance for implementing asynchronous Python applications using asyncio, concurrent programming patterns, and async/await for building high-performance, non-blocking systems.

日本語の概要は準備中です。原文の説明を表示しています。

Use multiple Claude agents to investigate and fix independent problems concurrently

日本語の概要は準備中です。原文の説明を表示しています。