「pipeline」の検索結果

Operating Roblox Studio for game development — the visual editor in which the 3D scene is built, tested, and published. Use this skill for: Studio basics (Explorer, Workspace, play-test, saving the place as .rbxl), the interplay with Rojo (Connect, scene-vs-code mode), AI control of Studio via the Roblox-Studio-MCP (execute_luau, insert_from_creator_store, generate_material, screen_capture, Play/Stop, reading the Console), the complete asset-pipeline workflow (Creator Store → clean up → kit → scene → .rbxl → Rojo brings it to life), and above all the MANDATORY malware scan for marketplace assets. Also trigger on "embed an asset from the Store", "Studio MCP not working", "studios: []", "generate material", "save scene", "is this Roblox asset safe", "scripts disappear after Play".

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Teststrategie-Berater für Softwareprojekte — wählt die richtigen Teststufen (Unit/Integration/System/Abnahme), Testarten (funktional, nicht-funktional, änderungsbezogen) und Testentwurfsverfahren (Äquivalenzklassen, Grenzwerte, Entscheidungstabellen, explorativ) für die jeweilige Situation und SDLC-/CI-Phase, inkl. Testpyramide, CI/CD-Gates, Shift-Left und Best Practices. Nutze diesen Skill IMMER, wenn Tests geschrieben, geplant, priorisiert oder bewertet werden sollen — bei "schreibe Tests", "Teststrategie", "Testplan", "Testkonzept", "was/wie soll ich testen", "welche Tests fehlen", "Testabdeckung verbessern", "QA aufsetzen", Fragen zu Regression/Smoke/Sanity/Last/Stress/Security-Tests oder beim Einrichten einer Test-Pipeline — auch wenn "Test" nur beiläufig fällt. Abgrenzung — für die TDD-Schleife selbst superpowers:test-driven-development, für Bug-Diagnose bugfix-protocol, für systematische Bug-Suche bugsweep nutzen.

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Einmaliger Tasksolver+Writer+Maintainer-Durchlauf fuer den Projektordner, in dem die Session gerade sowieso gearbeitet hat (inkl. Unterordner) — kein systemweiter Sweep. Loest offene triviale/autonome Register-Punkte, aktualisiert Dokumentation auf den gemessenen Ist-Stand (im Projekt UND in der Root der Pipeline, in der das Projekt liegt) und raeumt Strays/Temp- Dateien nach dem Papierkorb-Prinzip weg. Nutze diesen Skill bei /tidy-up, "raeum das Projekt auf", "mach hier sauber bevor wir weitermachen", am Ende einer Arbeitssitzung an einem Projekt, oder als faellige autonome Aufgabe innerhalb von /work-autonomous + /goal.

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Reviews a proposal, business case, deck or plan in character as a Chief Marketing Officer archetype and returns a DRAFT review in the chat with a verdict, findings cited to specific passages, marketing risks, what would change the verdict and five interrogation questions. Use when the user asks to "run a CMO review", "pressure-test the positioning", "what would a CMO say about this", "challenge the launch plan" or "check the messaging and audience". Do not use for price economics, margins or payback, use cfo-reviewer instead; for sales pipeline mechanics, use cro-reviewer. Drafts for human review; never approves, authorises or signs off.

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Reviews a proposal, business case, deck or plan in character as a Chief Business Officer archetype, covering commercial model, partnerships, deal structure, market positioning, strategic fit and opportunity cost, and returns a DRAFT review with a verdict, findings that cite the exact passage, commercial risks and five interrogation questions. Use when the user asks to "run a CBO review", "pressure-test this partnership", "check the deal structure" or "what would a commercial executive say about this". Do not use for pipeline, quota, sales-motion or forecast reviews, use cro-reviewer instead. Drafts for human review; never approves, authorises or signs off.

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。

Reviews a proposal, business case, deck or plan in character as an Investor archetype and returns a DRAFT review in the chat with a verdict whose conditions read as terms, findings that cite the exact passage, capital-allocation risks, what would change the verdict and five interrogation questions. Use when the user asks to "run an investor review", "what would an investor say about this", "pressure-test the capital case" or "prepare me for the investment committee". Do not use for internal budget, cost or finance-function review, use cfo-reviewer instead; for revenue forecast and pipeline, use cro-reviewer. Drafts for human review; never approves, authorises or signs off.

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Build an Anima LoRA from a single-character or style name, from Danbooru collection through curation, section-ordered WD14 captions, dataset checks, confirmed training, fixed-seed checkpoint validation, and a staged Civitai draft. Use for requests such as make a LoRA of X, collect images and train a character LoRA, train and upload to Civitai, or build a dataset from scratch and publish. Delegate training to lora-trainer and structural checks to dataset-doctor; never train without confirmation and never auto-publish.

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Finds high-value SEO and GEO keywords using web search, AI analysis, and optionally paid tools like Ahrefs or Semrush. Produces a validated keywords.csv file with a fixed schema for downstream pipeline consumption.

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Pick up and continue a serialized build-in-public book: read the current state (which days are written in which languages, what is uncommitted, where the gaps are), then keep writing through the established pipeline. Triggers: "/journey", "the book", "continue the journey".

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Reads existing brand DNA, keywords.csv, and prompts.csv, then produces a plan.csv — a strictly-schema'd content architecture telling the next pipeline step which pages to create, for which keyword/prompt clusters, and in what build order.

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LLM-judge stage of an alpha-mining pipeline: take a miner's deterministic candidate list, keep only real signal, drop noise, and propose additions to that miner's curated note. Use after any zero-token detector run. Reads only the candidate digest and the home note, never the corpus. Triggers: "/alpha-judge <miner>", "judge the candidates".

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Operational guide for the NV oOS Dictation desktop companion app (Tauri v2 + Rust, examples/nvoos-dictate) — build/run on Windows/MSVC, the local-first engine track (parakeet/whisper/moonshine/silero-vad, all API-verified against pinned crate versions), the embedded remote-STT fallback against the Embedded addon's transcribe route, model registry + verification harnesses, the plugins.updater boot-crash config rule, and the full test/clippy gate matrix. Use when building, extending, or debugging the dictation app, adding an engine or model, touching the cleanup pipeline golden tests, or continuing the P2/P4/P5/distribution follow-up work.

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Guide users through a 6-phase pipeline to plan a complete project — from brainstorming through project metadata to blueprint, starter-file generation, and per-milestone micro-task design with inline pedagogical choreography.

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Operational guide for WordPress.org submission readiness of NV oOS standalone plugins (nvoos-docs-hub live at 0.5.3; nvoos-content-graph live at 1.0.10 in the re-submission loop; nvoos-design-system next in the pipeline — issue #6811). Covers the 18 wp.org guidelines, the Plugin Check (PCP) gate in CI and Docker, PCP finding triage, the reviewer-reply loop (findings taxonomy from the real 0.4.3 review, related-issue sweep checklist, reply-email template), packaging exclusion tri-sync, readme.txt standards, .wordpress-org listing assets, and the NV oOS Complete checkout-flow disclosure discipline (docs-hub 0.5.3, content-graph). Use when preparing a wp.org submission, responding to a reviewer email, fixing the plugin-check CI job, triaging PCP findings, or capturing listing screenshots.

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Designs, generates, and packages Elementor template kits that import correctly — Envato Template Kit Import format, Elementor's native Kit format, and raw _elementor_data formats. Use when building/exporting template kits, fixing kit import errors ("newer Elementor Kit" warning, "nothing imports", broken images after import), migrating a site design into Elementor Pro, or packaging zips for any Elementor import pipeline. Covers verified manifest schemas, doc_type/template_type values, image URL sideloading rules, zip packaging constraints, and kit site-settings.

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Define product photography standards for e-commerce — shot types, composition, lighting, consistency rules, AI-assisted generation, and platform-specific variants. Covers the full product imagery pipeline from concept to published asset. Use when you need to plan product photoshoots, create photography briefs, set image standards for an e-commerce catalog, generate AI product lifestyle shots, or ensure visual consistency across hundreds of products.

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Research products via AI tools, cross-reference live stock, generate WooCommerce-ready copy, and extract marketing angles. Covers the full research-to-copy pipeline with remote_wp_connection stock checks and paper_store persistence. Use when researching products by SKU/name/category, generating WooCommerce descriptions, preparing product data for campaigns, or cross-referencing catalog files against live inventory.

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Produces WordPress draft posts from research findings — converts Paper Store research records or raw research data into publishable blog posts, buying guides, brand spotlights, and how-to articles. Use when the task asks to "turn research into a post", "create a blog post from findings", "publish my research", "research to content", or "write up the findings". Covers the full research-to-post pipeline with Paper Store integration.

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Systematically rewrites AI-generated or AI-sounding text on web pages, documents, or pasted content to sound human. Use this skill whenever a user asks to "humanize", "de-AI", "make this sound less like AI", "rewrite to avoid AI detection", or "clean up AI writing". Also trigger when a user pastes text and asks to make it sound more natural, more human, or less robotic. The skill applies a strict deterministic rule pipeline — NOT creative rewriting — making it safe for agentic/automated use.

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。

Designs event-driven architectures, webhook systems, API chaining flows, ETL pipelines, and integration patterns between 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。

Build production Apache Airflow DAGs with best practices for operators, sensors, testing, and deployment. Use when creating data pipelines, orchestrating workflows, or scheduling batch jobs.

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6 production-ready AI engineering workflows: prompt evaluation (8-dimension scoring), context budget planning, RAG pipeline design, agent security audit (65-point checklist), eval harness building, an

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。

AI and machine learning workflow covering LLM application development, RAG implementation, agent architecture, ML pipelines, and AI-powered 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。

Handles containerization, CI/CD pipelines, and deployment setup.

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。