「end-to-end」の検索結果

Optimize end-to-end application performance with profiling, observability, and backend/frontend tuning. Use when coordinating performance optimization across the stack.

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。

Automated end-to-end UI testing and verification on an Android Emulator using ADB.

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。

Act as FriendlyBet's CEO and primary front door for every FriendlyBet request through automatic company preflight. Own end-to-end routing, planning, execution coordination, department orchestration, decision-rights enforcement, quality gates, board-style reporting, and escalation only for strategic, legal, brand, cost, risk, or taste decisions. Use by default in FriendlyBet threads, with full department planning for broad, ambiguous, strategic, cross-functional, or outcome-driven requests such as SEO, new features, multi-sport expansion, product direction, release execution, company operations, or agent coordination.

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。

End-to-end Android indie idea workflow — feature-build phase only (no monetization). Phased — discover (if no idea) → generate 2 candidates → refine scope (offline/online, sharing, MVP vs full, core-value features) → risk/reward/tradeoff analysis when scoping down → emit idea brief. Use for greenfield app concepts, new feature ideation, idea discovery, or pre-design discovery before writing a design document. Triggers — new app idea, feature concept, indie dev brainstorm, validate idea, refine idea, what should I build, find an app idea, idea pipeline, I have no idea, help me find an app idea, scope down, MVP scoping, core value features.

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。

End-to-end eval-suite orchestration with the `eval` command: run -> grade -> compare -> calibrate, using strict JSON contracts and schema discovery.

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 working with Cypress Deep — cypress end-to-end testing management and analysis. Covers test execution monitoring, dashboard integration, screenshot and video analysis, test report parsing, flaky test detection, and configuration auditing. Use when investigating test failures, reviewing Cypress Cloud results, or managing Cypress test suites.

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。

Orchestrator for the full academic research pipeline: research -> write -> integrity check -> review -> revise -> re-review -> re-revise -> final integrity check -> finalize. Coordinates deep-research, academic-paper, and academic-paper-reviewer into a seamless 10-stage workflow with mandatory, coverage-bounded integrity checks, two-stage peer review, and auditable quality-assurance artifacts. Triggers on: academic pipeline, research to paper, full paper workflow, paper pipeline, end-to-end paper, research-to-publication, complete paper workflow, 연구부터 논문까지, 연구 주제 설정부터 논문 완성까지, 논문 전체 워크플로.

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。

Complete end-to-end MLOps pipeline orchestration from data preparation through model deployment.

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。

Orchestrator for the full academic research pipeline: research -> write -> integrity check -> review -> revise -> re-review -> re-revise -> final integrity check -> finalize. Coordinates deep-research, academic-paper, and academic-paper-reviewer into a seamless 10-stage workflow with mandatory integrity verification, two-stage peer review, and reproducible quality gates. Triggers on: academic pipeline, research to paper, full paper workflow, paper pipeline, end-to-end paper, research-to-publication, complete paper 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。

Orchestrates the tdmcp feature team end-to-end: design/wireframe → build → integrate → QA → release. Use whenever the user wants to build, implement, develop, ship, or add one or more tdmcp features/tools/effects/controls/AI-prompts for TouchDesigner, or to run them through a coordinated design→develop→QA→deploy pipeline. Also use for follow-ups: re-run, continue, update, fix, or improve a feature/batch, ship the next version, or rebuild just one part of a previous run. Single feature or a whole batch.

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。

General-purpose browser automation skill. Use when a task needs custom Playwright code — end-to-end testing, scraping a JS-rendered page, or driving a browser interactively — executed via this skill's universal executor rather than a one-off script.

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。

Generate an AI video end-to-end on Ozor.ai from a short text brief. Use this skill whenever the user wants to actually create a video (not just write a prompt) from a creative description, idea, or short brief — e.g. 'make me a video about X', 'generate a launch video for Y', 'create an explainer for Z', 'render a video', 'produce a 30s ad about ...', 'spin up a quick video for our new feature'. This skill runs the full Ozor MCP pipeline: generate_video → wait_for_job → export_video → wait_for_export → get_embed_code, and returns the shareUrl, editorUrl, downloadUrl, and a ready-to-paste <iframe> embed. Do NOT use this skill when the input is a document/PDF/PPTX/deck/URL — use ozor-document-video or ozor-url-to-video instead.

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。

When the user wants to optimize their subscription business end-to-end — from trial start through renewal, cancellation, and win-back. Use when the user mentions "subscription lifecycle", "trial conversion", "churn", "cancellation", "win-back", "lapsed subscribers", "dunning", "billing retry", "grace period", "renewal rate", "subscriber LTV", or "resubscribe". For paywall design and pricing strategy, see monetization-strategy. For subscription analytics dashboards, see app-analytics.

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 Xcode build optimization by benchmarking first, running the specialist analysis skills, prioritizing findings, requesting explicit approval, delegating approved fixes to xcode-build-fixer, and re-benchmarking after changes. Use when a developer wants an end-to-end build optimization workflow, asks to speed up Xcode builds, wants a full build audit, or needs a recommend-first optimization pass covering compilation, project settings, and packages.

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: Tester needs UI state evidence for VBR, or Builder needs to verify an integration wire is observable end-to-end. Powered by agent-browser MCP — token-efficient browser automation (200–400 tokens/page).

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: 'do work', 'dispatch builder', or team lead triggers automated task implementation. Governs end-to-end flow from dispatch through verification, fail handling, batch mode, and escalation.

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 end-to-end backend feature development from requirements to deployment. Use when coordinating multi-phase feature delivery across teams and services.

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。

Optimize end-to-end application performance with profiling, observability, and backend/frontend tuning. Use when coordinating performance optimization across the stack.

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。

Automated end-to-end UI testing and verification on an Android Emulator using ADB.

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 a coding task should be driven end-to-end from issue intake through implementation, review, deployment, and acceptance verification with minimal human re-intervention.

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。

Deploy a new LLM model via LiteLLM (GitOps) and/or Azure (Terraform + node backend), add it to the ai toolkit, create the required pull requests, and verify end-to-end. Use when rolling out a model to any environment, adding a model to the toolkit, troubleshooting "model not available" errors, or finding pricing/token limits/model ids for any provider.

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。

End-to-end ABM program design and orchestration for B2B SaaS mid-market and enterprise motions. Use when building a target account list, designing account tiering criteria, planning ABM channel orchestration, coordinating BDR outreach with marketing campaigns, defining ABM measurement frameworks, or running an ABM program review. Also use when asked to "build an ABM program," "set up account-based marketing," "design a target account strategy," "plan ABM campaigns," or "measure ABM results." Designed for demand gen teams selling to Mid-Market companies with $10K-$50K+ ACV, multi-persona buying committees, and 3-6 month sales cycles.

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。

Investigates a single PostHog error tracking issue end-to-end. Use when the user provides an issue ID or pastes an issue URL (`/error_tracking/<id>`) and wants to understand the error — who it affects, what triggers it, when it started, whether it correlates with a release, browser, OS, or feature flag, and what the next step should be. Pulls aggregated metrics, sample exception events, segment breakdowns, linked replays, and synthesizes a hypothesis-grade summary in one pass.

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。

Debug the signals pipeline locally end-to-end. Covers emitting test signals from fixtures, monitoring Temporal workflows via the REST API, reading sandbox agent logs from object storage, inspecting Docker sandbox containers, and diagnosing common failures (stale ClickHouse embeddings, agentsh network denials, inactivity timeouts). Use when a signal isn't reaching the inbox, a signal-report-summary workflow fails, or a sandbox task run times out.

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。