「machine」の検索結果

Quantum machine learning and differentiable quantum computing

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。

Genomic interval machine learning for regulatory element analysis

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Build resilient, long-running, multi-step applications with AWS Lambda durable functions with automatic state persistence, retry logic, and orchestration for long-running executions. Covers the critical replay model, step operations, wait/callback patterns, error handling with saga pattern, testing with LocalDurableTestRunner. Triggers on phrases like: lambda durable functions, workflow orchestration, state machines, retry/checkpoint patterns, long-running stateful Lambda functions, saga pattern, human-in-the-loop callbacks, and reliable serverless applications.

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 workflows with AWS Step Functions state machines using the JSONata query language. Covers Amazon States Language (ASL) structure, state types, variables, data transformation, error handling, AWS service integration, and migrating from the JSONPath to the JSONata query language.

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。

Multi-agent source security audit with a coverage ledger, independently verified findings, and machine-checked records. Use when asked to audit a codebase, find vulnerabilities, or run a source-level pen test.

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 Amazon Machine Images (AMIs) with Packer using the amazon-ebs builder. Use when creating custom AMIs for EC2 instances.

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 cross-channel journeys as state machines, simulation-tested before build. "map the onboarding journey"

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。

Pick the right LLM for LEGAL TRANSLATION — translating contracts, statutes, case law, and legal correspondence across languages, including Arabic/MENA. Vendor-neutral routing triangulated from mid-2026 evidence (WMT25 human eval, SwiLTra-Bench legal-MT, multilingual-reasoning proxies, ArabLegalEval). There is NO clean legal-translation leaderboard, so this vertical is directional and treats human legal-linguist review as mandatory. Asks up to 4 quick questions (language pair, cost, speed, privacy/certification), then recommends a primary model + fallback + what to avoid + what a human must verify. Use when someone asks "which model to translate this contract/statute", "best AI for legal translation", "translate this legal doc to/from Arabic", or is about to machine-translate legal text without a fixed model.

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。

Assesses which of the Art. 50(1)-(5) transparency obligations of the EU AI Act apply to a given AI system's provider or deployer, grounded in the final Code of Practice on Transparency of AI-Generated Content (June 2026) and the Commission's draft Art. 50 Guidelines. Covers AI-chatbot disclosure, deepfake and synthetic-content marking/watermarking, emotion-recognition and biometric-categorisation notices, the machine-readable marking duty, the obviousness exceptions, and the implementation timeline. Outputs a formal mini-report plus a per-obligation compliance checklist with gap flags. For breadth-first tier triage use the EU AI Act System Classifier; for raw Art. 50 text and Q&A use the EU AI Act Knowledge Base; for the full role x tier matrix use the EU AI Act Obligations Mapper.

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When you or another skill needs to fetch the content of a social media post by URL — tweet, X thread, LinkedIn post, Instagram post, TikTok video, Bluesky post, Reddit thread, Mastodon status, Threads post, Hacker News thread. Returns normalized structured data (author, posted_at, text, engagement counts, media URLs, replies if requested) regardless of platform. Tries strategies in order: direct API (Bluesky, Mastodon, HN, Reddit), agent-browser with modal dismissal (LinkedIn, X preview), Wayback Machine (older posts), paid APIs (ScrapeCreators / Apify — only if env keys present). Triggers on "/social-fetch <url>," "fetch this tweet," "fetch this post," "what does this LinkedIn say," "read this thread," "pull this post." Used by deep-research (citing specific posts), jab-hook (inspiration account analysis), business-brainstorm (competitor / operator commentary).

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Clean up translation-ese and AI-writing tics in Korean prose so it reads like a person wrote it — content (facts, numbers, proper nouns, quotes) stays 100% unchanged, only style and rhythm are touched. Single-pass, done in this session. Triggers — AI 티 없애줘, 번역투 고쳐줘, 한글 자연스럽게 윤문해줘, sounds like a machine translation, remove AI tells from this Korean text, make this Korean read naturally. Not for: marketing copy generation (write it clean the first time — see rules/korean-writing-quality.md), English text, plain spell-check (see korean-spell-check), or book-length manuscripts.

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 patterns for combat systems including hitbox/hurtbox architecture, damage calculation (DamageData class), health components, combat state machines, combo systems, ability cooldowns, and damage popups. Use for action games, RPGs, or fighting games. Trigger keywords: Hitbox, Hurtbox, DamageData, HealthComponent, combat_state, combo_system, ability_cooldown, invincibility_frames, damage_popup.

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。

Bevy ECS skill for app states and per-entity state data. Keywords: Bevy, States, NextState, OnEnter.

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 patterns for 2D animation in Godot using AnimatedSprite2D and skeletal cutout rigs. Use when implementing sprite frame animations, procedural animation (squash/stretch), cutout bone hierarchies, or frame-perfect timing systems. Trigger keywords: AnimatedSprite2D, SpriteFrames, animation_finished, animation_looped, frame_changed, frame_progress, set_frame_and_progress, cutout animation, skeletal 2D, Bone2D, procedural animation, animation state machine, advance(0).

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 patterns for combat systems including hitbox/hurtbox architecture, damage calculation (DamageData class), health components, combat state machines, combo systems, ability cooldowns, and damage popups. Use for action games, RPGs, or fighting games. Trigger keywords: Hitbox, Hurtbox, DamageData, HealthComponent, combat_state, combo_system, ability_cooldown, invincibility_frames, damage_popup.

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 patterns for 2D animation in Godot using AnimatedSprite2D and skeletal cutout rigs. Use when implementing sprite frame animations, procedural animation (squash/stretch), cutout bone hierarchies, or frame-perfect timing systems. Trigger keywords: AnimatedSprite2D, SpriteFrames, animation_finished, animation_looped, frame_changed, frame_progress, set_frame_and_progress, cutout animation, skeletal 2D, Bone2D, procedural animation, animation state machine, advance(0).

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。

Bevy ECS skill for app states and per-entity state data. Keywords: Bevy, States, NextState, OnEnter.

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。

Provision, verify, and connect a self-hosted Temps platform instance on an explicitly authorized machine. Use when the user wants an agent to install Temps with the official deploy script, configure a local CLI context, start browser device authorization, present the approval URL, wait for approval, or perform initial platform, DNS, TLS, user, and service setup without exposing credentials.

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Curated download URLs and target directories, organized by family (Flux, WAN, LTX, Qwen, Z-Image, SD15/SDXL), for every model the comfyui-mcp skills reference, covering checkpoints, VAEs, text encoders, and LoRAs. Use when downloading models with download_model (action:"download" / action:"download_civitai"), when a workflow fails with a missing-model error, or when setting up a new machine.

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 implementing animations — AnimationPlayer, AnimationTree, blend trees, state machines, sprite animation, and code-driven animation

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 using the LimboAI addon — behavior trees and hierarchical state machines (C++ GDExtension) with a visual editor, BTTask subclassing, and a blackboard

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。

Train and deploy neural networks in distributed E2B sandboxes with Flow Nexus

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Stage-by-stage audit of an app's App Store growth machinery against a 54-item P0–P9 playbook — every item scored from an App Store Connect MCP call, a codebase check, or an explicit question to the user, then routed to the skill or command that fixes it. Read-only on App Store Connect. Use for a growth audit or scorecard, a pre-launch growth plan, a quarterly re-audit, or "which growth levers am I missing."

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The ORFhunteR package is a R and C++ library for an automatic determination and annotation of open reading frames (ORF) in a large set of RNA molecules. It efficiently implements the machine learning model based on vectorization of nucleotide sequences and the random forest classification algorithm. The ORFhunteR package consists of a set of functions written in the R language in conjunction with C++. The efficiency of the package was confirmed by the examples of the analysis of RNA molecules fr

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。