Results for “parallel-testing”

Spawns GLM-5 powered teammates with native thinking mode for coding, review, testing, planning, and research tasks.

Jest is a comprehensive JavaScript testing framework built by Meta, designed for zero-configuration testing of JavaScript and TypeScript applications. It provides a complete ecosystem for unit testing, integration testing, and snapshot testing with built-in code coverage, mocking capabilities, and parallel test execution. Jest works seamlessly with React, Node.js, Angular, Vue, and virtually any JavaScript project, making it the most widely adopted testing framework in the ecosystem.

Run multiple AI agents against a product, SDK, or docs site to score setup friction, speed, efficiency, error recovery, and doc quality from a minimal prompt.

Test Parallelizer - Auto-activating skill for Test Automation. Triggers on: test parallelizer, test parallelizer Part of the Test Automation skill category. Use when writing or running tests. Trigger with phrases like "test parallelizer", "test parallelizer", "test".

Execute phased plans with multiple independent tasks per phase by fanning out one agent per task, reconciling outcomes per wave, gating between phases with verify commands, and emitting a phase × outcome report. Triggers include "execute this plan", "work through these phases", "swarm over this backlog", "parallelize this plan".

Run Semgrep static analysis scans with automatic language detection, parallel subagent execution, and merged SARIF output. Supports full ruleset coverage or high-confidence security vulnerability filtering.

Parallel execution engine for high-throughput task completion

Only to be triggered by explicit /parallel-task commands.

Applies the same change across multiple repositories in parallel using git worktrees, tests, and pull requests.

Parallel execution engine for high-throughput task completion

Test Python services with pytest, async coverage, monkeypatch, and boundary-focused fakes. Use when writing Python tests, fixtures, async tests, regression tests, or dependency-isolated verification.

GPU-safe parallel processing patterns for KINTSUGI to prevent OOM crashes and ensure Jupyter-compatible progress output

Runs a structured code review using four parallel subagents covering style, correctness, security, and performance, then synthesizes findings by severity and gives an approve/needs-changes recommendation.

Reviews CUDA, OpenMP, and OpenCL code for parallel correctness, covering data races, memory model violations, synchronization bugs, numerical precision issues, and API-specific pitfalls.

Multi-agent orchestration patterns. Use when multiple independent tasks can run with different domain expertise or when comprehensive analysis requires multiple perspectives.

Dispatching Parallel Agents

Only to be triggered by explicit /parallel-task commands.

Multi-agent orchestration patterns. Use when multiple independent tasks can run with different domain expertise or when comprehensive analysis requires multiple perspectives.

Dask parallel computing reference for Python. Covers Dask DataFrame (parallel Pandas), Dask Array (parallel NumPy), Dask Delayed for custom parallelism, Dask Bag, distributed clusters, dashboard monitoring, and scaling best practices.

Computação paralela/distribuída. Escale pandas/NumPy além da memória disponível, DataFrames/Arrays paralelos, processamento multi-arquivo, grafos de tarefas, para datasets maiores que RAM e workflows paralelos.

Orchestrating multi-session parallel execution via Claude Code Agent Teams API and Codex CLI Subagents — launch, manage, coordinate concurrent tasks. Use when parallel work is needed.

Simulate and detect software supply chain attacks including typosquatting via Levenshtein distance, dependency confusion testing, package hash verification, and vulnerability scanning with pip-audit.

Multi-agent orchestration patterns. Use when multiple independent tasks can run with different domain expertise or when comprehensive analysis requires multiple perspectives.

Parallel execution engine for high-throughput task completion

Parallel/distributed computing. Scale pandas/NumPy beyond memory, parallel DataFrames/Arrays, multi-file processing, task graphs, for larger-than-RAM datasets and parallel workflows.

Search the web, extract URL content, enrich datasets with web-sourced fields, and run deep research reports, prioritizing academic and scientific sources.

Plans parallel agents or worktrees with isolated ownership, validation lanes, and merge rules. Use only for safely separable work.

Testing patterns and principles. Unit, integration, mocking strategies.

Perl testing patterns using Test2::V0, Test::More, prove runner, mocking, coverage with Devel::Cover, and TDD methodology.

Multi-agent collaboration plugin that spawns N parallel subagents competing on the same task via git worktree isolation. Agents work independently, results are evaluated by metric or LLM judge, and the best branch is merged. Use when: user wants multiple approaches tried in parallel — code optimization, content variation, research exploration, or any task that benefits from parallel competition. Requires: a git repo.