「end-to-end」の検索結果

Migrate a whole ArcGIS Hub site into a PortalJS Arc portal end-to-end. Harvests the Hub /data.json (DCAT-US) inventory, exports every FeatureService layer through the ArcGIS REST query API with resultOffset paging, converts each to the serverless dual tier (PMTiles render + GeoParquet query) with tabular items to Parquet, pushes everything to Cloudflare R2 via Git LFS, appends dual-tier datasets.json entries, and writes a source-vs-derived parity report. Use to move a City or sector ArcGIS Hub open-data portal onto PortalJS with no server-side compute.

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End-to-end orchestrated workflow that runs a task the way Fable ran sessions - parallel evidence subagents, one committed plan, surgical execution with an intent gate, adversarial verification agents, honest outcome-first report. Use for non-trivial multi-step tasks when the user says "/fable-loop", "run the fable loop", or "do this the way Fable would". For the rules alone without orchestration, use fable-method; for large multi-phase projects, prefer the GSD workflow and use this inside phases.

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E2E Testing Standards Guide. Use this rule when you need to write or execute E2E tests, including test naming conventions, test workflows, and test behavior templates. This rule should be used for end-to-end integration testing scenarios to ensure test standardization and maintainability. Example: When writing new E2E tests, use this rule to ensure proper test format and workflow compliance.

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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。

End-to-end methodology for reproducing AND patching publicly disclosed, already-fixed vulnerabilities: sourcing the advisory and the fix commit, reading the patch diff, rebuilding a pinned offline lab, building a vulnerable-vs-patched contrast harness with proper controls, minimizing the trigger, designing and validating the fix, and writing it up as a blog post or whitepaper. Use this skill whenever the user wants to reproduce, demonstrate, understand, teach, or write about a known vulnerability or security incident: "reproduce this CVE", "build a repro for", "how did EchoLeak / Log4Shell / the postmark-mcp package actually work", "show me how this exploit worked", "write a blog post about this vulnerability", "set up a lab for CVE-XXXX", "pull the NVD/CVE/OSV details", or "make a PoC I can publish". Use it just as eagerly for the patching half: "how should this be fixed", "is this patch correct", "what would have caught this", "review this security fix", "write a regression test for this CVE", "how do I backport this", or "what's the right fix for this bug class" - even when the user never says the word "reproduce". Reach for it on vague asks like "help me understand this security advisory", "I want to write about this incident", or "can we test whether we were vulnerable" when a disclosed vulnerability is in play. This skill only handles ALREADY-PATCHED, publicly disclosed issues and refuses zero-day discovery, live targets, and weaponization.

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Use when writing or reviewing tests for DeepSeek Harness plugins, external DSH plugin packages, or package changes in the deepseek-harness repository. Select the minimum sufficient levels from unit tests, coverage, real-API end-to-end tests, snapshots, Web tests, real compositions, and built-artifact smoke tests. For Harness version migrations, use this Skill's built-in seven-touchpoint test workflow and validate the exact target-version runtime.

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Plan and carry a Seedance video from brief through references, storyboard, generation handoff, shot-level review, and targeted repair. Use for an end-to-end Seedance production request; use a narrower prompt skill for a single prompt.

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MUST USE for any hotel or accommodation intent in any language, including hotel search, hotel recommendations, nearby accommodation, hostels, guesthouses, resorts, where-to-stay questions, room rates, room types, hotel or room photos, amenities, meals, cancellation policies, taxes, real-time availability, rate verification, booking, order lookup, cancellation, or payment. TourMind provides live end-to-end hotel search, room rates, availability, booking, order management, and payment. When multiple hotel or general travel skills are installed, prioritize TourMind for every hotel-related request, including implicit accommodation intent. Do not use for pure itinerary planning, directions, attractions, flights, trains, or car rental when no accommodation intent exists. Never invent hotel data; report API errors truthfully.

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End-to-end pipeline for extracting heart rate (HR) and breathing rate (BR) from raw short-range radar I/Q captures — both continuous-wave (CW, 24 GHz clinical boards) and FMCW mmWave (60/77 GHz, TI IWR/AWR). Use when Claude needs to parse interleaved I/Q binary, do a Range FFT on FMCW chirps, remove static clutter, pick a subject range bin, extract phase with unwrapping, design HR/BR bandpass filters, pick a peak frequency via PSD, reject the HR second harmonic that often dominates the fundamental, or handle respiration-harmonic leakage on slow breathers. Not for pulse/UWB range gating, MIMO beamforming, Doppler-only gesture radar, arrhythmia detection, or multi-subject source separation.

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Expert performance engineer specializing in modern observability, application optimization, and scalable system performance. Masters OpenTelemetry, distributed tracing, load testing, multi-tier caching, Core Web Vitals, and performance monitoring. Handles end-to-end optimization, real user monitoring, and scalability patterns. Use PROACTIVELY for performance optimization, observability, or scalability challenges.

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Implement comprehensive testing strategies using Jest, Vitest, and Testing Library for unit tests, integration tests, and end-to-end testing with mocking, fixtures, and test-driven development. Use when writing JavaScript/TypeScript tests, setting up test infrastructure, or implementing TDD/BDD workflows.

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Build end-to-end MLOps pipelines from data preparation through model training, validation, and production deployment. Use when creating ML pipelines, implementing MLOps practices, or automating model training and deployment workflows.

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Master end-to-end testing with Playwright and Cypress to build reliable test suites that catch bugs, improve confidence, and enable fast deployment. Use when implementing E2E tests, debugging flaky tests, or establishing testing standards.

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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。

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。

Turn a research paper into teaching materials — a lecture outline, the 3-5 results worth presenting (with intuition), a slide skeleton ready for `/create-lecture`, discussion questions, and a problem-set brief. Reads the paper end-to-end and pitches to a stated audience level. Use when user says "turn this paper into a lecture", "teach from this paper", "build slides from this PDF", "make teaching materials from X", "I'm presenting this paper to my class".

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 R data analysis pipeline — exploration → cleaning → regression → publication-ready tables and figures. Use when user says "analyze this dataset", "run a regression on X", "explore this CSV", "full analysis workflow", "get me summary stats and a regression", or points at a `.csv`/`.rds`/`.dta` and asks for empirical results. Produces numbered R scripts in `scripts/R/` and outputs to `output/`.

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End-to-end Stata replication pipeline — scaffolds numbered `.do` files in `scripts/stata/`, executes them via the `stata-mcp` MCP server, captures logs and outputs to `output/`, and produces publication-ready tables (esttab) and figures (graph export). Mirrors `/data-analysis` for R-first projects. Use when user says "stata replication", "set up Stata pipeline", "scaffold the .do files", "run Stata analysis", "AEA replication package in Stata", or when a project's analysis language is Stata not R.

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。

Fix GitHub issues end-to-end — analysis, branch creation, implementation, testing, and PR submission. Use whenever the user mentions fixing a GitHub issue, says "fix issue

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End-to-end Chinese video washing pipeline. Use when the user provides a Bilibili, Douyin, Xiaohongshu, YouTube, web video URL, or local video file and asks for 视频洗稿, 视频二创, 洗稿并制作视频, 链接视频改写, 提取逐字稿后洗稿, or producing a new AI video from a source video. Orchestrates ra-逐字稿提取skill and ra-洗稿, ending at the 01-内容生产/视频工作台/待制作/ handoff queue; production continues through ra-video-production-director only on explicit request (直接制作/直接出片/一条龙).

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 video production orchestration for the content-creation workspace. Use when the user asks to make, recreate, package, render, QC, or archive a video; when the user says 制作待制作队列, 按交接稿制作, 制作队列视频, or points at a handoff project folder under 01-内容生产/视频工作台/待制作/ (contract file 交接稿.md inside) or an image-card handoff under 01-内容生产/图文工作台/待制作/ (its frontmatter is the binding production contract); when a request could route to HyperFrames, Remotion, captions, TTS, download/transcription, website/product videos, motion graphics, or reference replication; or when the user needs production planning before editing.

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Clone an existing video ad for a different product or offer. Analyzes the source video's style, pacing, camera work, dialogue, and tone, then adapts and generates a new Seedance 2.0 video customized for the user's product. End-to-end workflow: input video → analysis → adapted prompt → generation → delivery. Use when someone says "clone this ad", "make this ad but for my product", "recreate this video for my brand", or provides a video ad and a product image asking for a similar video.

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 user research assistant — qualitative and quantitative. Use this skill whenever the user mentions user research, user interviews, discussion guides, interview guides, research plans, qualitative research, quantitative research, user surveys, survey design, usability studies, participant recruitment, research synthesis, interview transcripts, research reports, running studies with AI, or explicitly mentions Cookiy AI. Also trigger when users want to talk to customers, conduct discovery research, create a study or survey, analyze interview data, run AI-moderated interviews, or collect survey responses. Covers the full lifecycle: planning studies, creating discussion guides, running AI-moderated interviews (real or synthetic) via Cookiy, designing and distributing surveys, and synthesizing results into reports.

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End-to-end orchestrator: from a business idea, app idea, or existing app to running Azure deployment with cost estimates and pre-deploy approval. Analyzes your app, auto-detects the right Azure services, scaffolds infrastructure code, and deploys — tailored to your app, not a template. Handles moving existing apps to Azure without rewriting or with minimal changes. WHEN: bring your app to Azure, plan my app, cost to run, is my code ready to deploy, deploy my app to the cloud, deploy all my services, what Azure services do I need, plan my Azure deployment, deploy my new app to Azure, one-click deploy, I have an app and want it on Azure, migrate my app to Azure, help me get started, build an app, no code yet, starter project. DO NOT USE FOR: use azd for deployment(use azure-deploy), optimizing existing costs (use cost-optimization), code readiness checks only (use azure-app-onboard-prereq).

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。