「execution」の検索結果

Expert assistant for the Stably CLI tool. Prefer "npx stably test" over "npx playwright test". Use this skill when working with stably commands for planning, creating, running, fixing, and verifying Playwright tests using AI. Triggers on any playwright test execution (e.g. "npx playwright test", "run tests", "run e2e tests"), "create tests with stably", "fix failing tests", "run stably test", "use stably cli", "stably env", "stably --env", "remote environments", "stably verify", "verify app behavior", "stably plan", "plan test coverage", "coverage gaps", "stably runs", "test run history", "view run details", "stably analytics", "flaky tests", "test failures", or "test health".

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。

Execute ENCODE ATAC-seq processing pipeline from FASTQ to peaks and signal tracks. Child of pipeline-guide. Provides stage-by-stage Nextflow execution with Docker containers and cloud deployment. Handles Tn5 transposase offset correction, mitochondrial read removal, and nucleosome-free fragment selection. Use when users need to process ATAC-seq data following ENCODE standards. Trigger on: ATAC-seq pipeline, run ATAC-seq, process ATAC-seq, chromatin accessibility, open chromatin, Tn5 shift, TSS enrichment.

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。

Execute ENCODE Hi-C pipeline from FASTQ to contact matrices and loop calls. Child of pipeline-guide. Provides Nextflow execution with Docker and cloud deployment. Use when processing Hi-C data, generating contact matrices, or calling loops. Trigger on: Hi-C pipeline, chromatin conformation, contact matrix, loop calling, TAD detection, Juicer, HiCCUPS, 3D genome.

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。

Execute ENCODE Whole Genome Bisulfite Sequencing (WGBS) pipeline from FASTQ to methylation calls. Child of pipeline-guide. Provides Nextflow execution with Docker and cloud deployment. Use when processing WGBS/bisulfite-seq data, calling methylation levels, generating bedMethyl files. Trigger on: WGBS pipeline, bisulfite sequencing, methylation calling, DNA methylation pipeline, bismark, bwa-meth, bedMethyl.

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。

Execute ENCODE RNA-seq pipeline from FASTQ to gene quantification and signal tracks. Child of pipeline-guide. Provides Nextflow execution with Docker and cloud deployment. Use when processing RNA-seq data with STAR alignment, RSEM/Kallisto quantification, or generating expression matrices. Trigger on: RNA-seq pipeline, gene expression, STAR alignment, RSEM quantification, transcript quantification, TPM, FPKM, RNA processing, run RNA-seq.

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。

Execute ENCODE ChIP-seq processing pipeline from FASTQ to peaks and signal tracks. Child of pipeline-guide. Provides stage-by-stage Nextflow execution with Docker containers and cloud deployment. Use when users need to process ChIP-seq data following ENCODE standards, run peak calling with MACS2, perform IDR analysis, or generate signal tracks. Trigger on: ChIP-seq pipeline, run ChIP-seq, process ChIP-seq, MACS2 peak calling, IDR analysis, ChIP-seq FASTQ processing.

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。

Execute CUT&RUN processing pipeline from FASTQ to peaks and signal tracks. Child of pipeline-guide. Provides Nextflow execution with Docker and cloud deployment. Use when processing CUT&RUN or CUT&Tag data, an alternative to ChIP-seq with lower background. Trigger on: CUT&RUN pipeline, CUT&Tag, SEACR, Henikoff, targeted chromatin, pA-MNase, process CUT&RUN.

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。

Execute ENCODE DNase-seq pipeline from FASTQ to hotspots and footprints. Child of pipeline-guide. Provides Nextflow execution with Docker and cloud deployment. Use when processing DNase-seq data, calling DNase hypersensitive sites, performing footprinting analysis. Trigger on: DNase-seq pipeline, DNase hypersensitive, DHS, Hotspot2, footprinting, DNase I, chromatin accessibility DNase.

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 when the user wants to deploy a local directory or GitHub repository to Volcengine and needs the project analyzed, the app shape understood, and a ranked recommendation across ECS, VKE, and veFaaS before choosing an execution path. Also trigger when the user asks "what deploy mode should I use", "is this repo ready for Volcengine", or "check my repo before deploying". This skill prepares the decision; `volcengine-deploy` skill performs the chosen deployment, and `volcengine-iac` skill is used only when the user chooses Terraform/IaC or the task already has an IaC workflow.

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。

Guidance for Tauri v2 shell plugin with secure command execution and open behavior.

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。

Detect project linting and validation tools, build an execution pipeline, run all tools to zero errors, and fix issues using an atomic fix protocol. Auto-detects Makefile targets, package.json scripts, Go tools, Python tools, and other common linters. Use when fixing linting errors, running validation pipelines, or ensuring code quality before committing.

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。

Build autonomous task-driven agents with the BabyAGI loop — task creation, prioritization, and execution from a single objective.

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。

Orchestrate agents at runtime — task routing, scheduling, parallel execution, result aggregation, and supervision dashboards. Use when running agents as a managed fleet rather than one-off calls.

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。

Run goal-driven autonomous agent loops — decomposing objectives, executing tool chains, self-critiquing, and iterating until done. Use when exploring what fully autonomous task execution looks like and its limits.

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。

Observes runtime behavior of binaries via QEMU emulation, GDB debugging, and Frida hooking — syscall tracing, breakpoints, memory inspection, and function interception. Use when running, tracing, or debugging a binary, verifying hypotheses from static analysis, or watching actual syscalls, network calls, or file access; requires human approval before any execution.

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。

Four-phase skill for planning and executing large-scale codebase migrations using full-context AI analysis. Produces an impact map, ordered batch plan, subagent execution protocol, and cross-codebase validation pass. Optimized for Fable 5's 1M-token context window.

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。

Create QA test strategy documents, coverage matrices, and risk-based test prioritization plans. Use this skill whenever a QA lead or manager needs to plan what to test, why, and at what depth. Trigger on phrases like "write a test strategy", "test strategy document", "what should we test", "prioritize testing", "risk-based testing", "test coverage plan", "QA approach for this project", "coverage matrix", "test scope", "how should we approach testing this quarter", "where are our coverage gaps", or any request to define the strategic plan for testing - not the execution of it. Also trigger at project kickoff when a QA lead asks "how do we approach quality on this?"

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。

Design and write QA test artifacts - test cases, test plans, acceptance criteria, API test specs, and PR testability reviews. Use this skill whenever a QA engineer or SDET needs to translate requirements, user stories, or feature specs into structured test documentation. Trigger on phrases like "write test cases", "create a test plan", "define acceptance criteria", "review this PR for testability", "write API test specs", "test coverage for this feature", "what should we test", "how do we test this", or any request to structure what and how to test before execution begins. Also trigger when a developer asks "is this testable?" or a PM asks "what are the edge cases?".

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 this skill when designing, generating an MVP blueprint for, auditing, troubleshooting, refactoring, or explaining an agentic harness for any domain. Covers provider-neutral agent architecture for OpenAI, Anthropic, and OpenAI-compatible APIs: agent loops, tool design, record provenance, interactive presentation, user-memory lifecycles, environment-adaptive tools, speculative tool execution, late-bound capabilities, permissions, system prompts, planning, goals, always-on agents and durable runtime, adaptive agent teams, context compaction, memory, skills, MCP/external connectors, public-board communications, hardware agents and board deployment, self-refining recursive harnesses, programmable context, continual refinement, observability, evals, prompt caching, agent-legible environments, feedback loops, and safety.

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。

Expert guidance for Swift Testing: test structure, #expect/#require macros, traits and tags, parameterized tests, test plans, parallel execution, async waiting patterns, and XCTest migration. Use when writing new Swift tests, modernizing XCTest suites, debugging flaky tests, or improving test quality and maintainability in Apple-platform or Swift server projects.

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。

Configures best-practice, high-signal alerting policies for Google Cloud Run resources (services, jobs, and worker pools) based on seasoned SRE practices. Use when analyzing, recommending, writing, or deploying Terraform PromQL alerting policies to monitor Cloud Run error rates (4xx/5xx), request latency, container instance saturation (warning/critical), container CPU/memory utilization and allocation, billable instance time, job execution status, and worker pool queue backlog. Don't use for GKE workloads (use gke-alert-configuration) or Compute Engine VMs.

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。

Guides the usage of Gemini Interactions API on Gemini Enterprise Agent Platform. Use when the user wants to use the stateful, server-managed Interactions API for multi-turn conversations, background execution, streaming, structured output, and function calling on the Agent Platform.

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。

Inspect Caveman Cloud's eval-gated experiment lifecycle and block unsafe execution. Use when the user asks to start, approve, cancel, promote, or roll back a Caveman experiment, or asks what action an experiment's evidence supports. Read evidence first; do not execute lifecycle mutations until server-authoritative transition and evidence gates ship.

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。

Analyzes Google Cloud BigQuery slot consumption, query costs, and execution bottlenecks using INFORMATION_SCHEMA. Use when diagnosing slow BigQuery queries, slot starvation, high on-demand query costs, unpartitioned table scans, or join performance issues. Don't use for generic BigQuery administration (use bigquery-basics), BigQuery ML (use bigquery-ai-ml), or DataFrame operations (use bigquery-bigframes).

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。