「machine」の検索結果

Applies the reasoning style of Ilya Sutskever (deep learning pioneer, co-founder of OpenAI and Safe Superintelligence Inc.) to problems involving AI architecture, scaling laws, alignment, and research strategy. Reach for this skill whenever discussing machine learning paradigms, the limits of compute and data, AGI timelines, superintelligence safety, or deciding between hardcoding vs. learning. Trigger this skill for questions about next-word prediction, reinforcement learning efficiency, generalization gaps, and transitioning from brute-force scaling to fundamental research, even if the user doesn't explicitly name him.

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Не для обхода детекторов; не для текста, который тебе не принадлежит. Находит следы машинного текста в русском и объясняет их; переписывает только по явной просьбе; вердиктов об авторстве нет. Отвечает на просьбы вида «очеловечь», «убери гпт-шность», «звучит как нейросеть», «проверь на ИИ», «убери штампы», «убери канцелярит», «сделай живым», «вставка из чата», «копипаста из ChatGPT», contentReference, utm_source, невидимые символы, машинная типографика (40 regex-маркеров, у 38 полная запись доказательств). Finds machine-text traces in Russian and explains them; rewrites only on explicit request; no authorship detection verdict. Английский профиль: только артефакты/факты; исходный код не обрабатывается; юридические и художественные тексты — только детерминированная очистка артефактов. Демо: vladimir-human.github.io/humanizer-ru

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 simulating vehicle motion, calculating safe following distances, time-to-collision, speed/position updates, or implementing vehicle state machines for cruise control modes.

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。

Deep expertise in ML/CV model selection, training pipelines, and inference architecture. Use when designing machine learning systems, computer vision pipelines, or 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。

Use when semgrep, osv-scanner, github-security or snyk MCP tools appear in your tool list - what each server is for, how to call it safely, what data leaves the machine, and how its findings are triaged

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 starting any game task, before writing tests - find out which editors and runners exist on this machine, keep gameplay rules in engine-independent classes with an injected clock and seeded RNG, and report exactly what could not run.

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Use when writing Godot 4 GDScript - static typing everywhere, scene composition with component nodes, call down/signal up, @onready caching, Resources as data duplicated per instance, autoloads only for services, state machines, pooling, physics in _physics_process, and threaded loading.

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。

Objectively evaluate a UI/web design against the pols.dev anti-slop design law: detect catalogued slop tells with cited evidence, score 8 weighted axes (color, type, components, layout, motion, execution, signature, cohesion), and emit a Slop Report with a 0–100 Slop Index and grade. Use when the user asks to "evaluate design slop", "slop report", "is this design AI slop", "audit this landing page design", "de-slop review", or wants an objective score of how generic/machine-made a design looks. To fix text (not design), use human-ai or humanizar skills instead.

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Sub-skill: Implement MPP Payment Discovery. Publish OpenAPI with x-payment-info extensions so AI agents discover payable endpoints via Machine Payment Protocol (mpp.dev).

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。

Master Godot 4 GDScript patterns including signals, scenes, state machines, and optimization. Use when building Godot games, implementing game systems, or learning GDScript best practices.

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。

Godot 4 有限状态机设计模式，包含状态基类、状态机管理、状态转换和输入处理。

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great_cto's own committed aesthetic — the instrument panel. Dark five-step surface ladder, exactly one accent, two faces divided by MEANING (Geist speaks, Geist Mono is machine-truth), tabular numerals, and a dash that is not a nought. Twelve checkable rules measured from packages/board and greatcto.systems, not designed for this file. Use when building or extending any great_cto surface — the board, the site, a report, a share 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。

Front-door router for the product-shots ecosystem. Clarifies underspecified visual creation requests through a 4-stage state machine, injects platform visual DNA (Amazon + 7 social platforms) and industry visual DNA (7 industries), enforces a unified Negative Constraints prompt patch, then routes to one of five downstream business skills (product-shots-main-image / product-shots-detail-page / product-shots-multi-angle / product-shots-ad-creative / product-shots-social-post). Use when the user says "I need a product image", "design something for my listing", "I need content for Instagram", "make an ad for me", "make a cover image", "create a post", "做一个商品图", "帮我做个详情页", "帮我做个广告图", "做一个社媒图", "做一张图", "帮我设计", "做个封面" — i.e. any underspecified visual creation request that needs clarification before generation. This is the intent-routing hub of the product-shots ecosystem.

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。

Before writing any implementation code, define the acceptance criteria and test cases that the code must satisfy. Agents then write code to pass these tests — not to match a vague description. Eliminates "it works on my machine" and "I think this is what you wanted" outcomes.

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。

Designing workflows and state machines. Use when state transition design, invalid transition detection, Saga patterns, or approval flow design is needed.

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

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。

Distill any intent input into the SPEC kernel + companions — the canonical, preservation-validated machine contract for downstream work. Use when the user says "create a spec", "distill this into a spec", "validate this spec", or "update the spec".

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。

Analyze Trigger.dev tasks, schedules, and runs for cost optimization opportunities. Use when asked to reduce spend, optimize costs, audit usage, right-size machines, or review task efficiency. Combines static source analysis with live run analysis via the Trigger.dev MCP tools (list_runs, get_run_details, get_current_worker).

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 building Solana-enabled games or apps in Godot 4 with the Godot Solana SDK. Triggers include: Solana transactions in GDScript, SPL tokens or NFTs in a Godot project, connecting browser or mobile wallets (Phantom, Solflare, Backpack) in a Godot game, calling Anchor smart contracts from GDScript, working with Candy Machine in Godot, or any mention of SolanaClient, Transaction, Pubkey, Keypair, WalletAdapter, or AnchorProgram in a Godot context. Do NOT use for non-Godot Solana SDKs (solana-py, solana-go, solana-web3.js).

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。

Maintain the durable, per-ticket run log that warp deploy/update skills append to at every milestone — a Linear-document-by-title primary with single-writer discipline and a local-file fallback, a machine-parseable per-chain row plus prose entry shape, and a surface-the-URL-as-proof hard gate. Referenced by any warp deploy/update skill that must leave a retrospective trail across worker restarts.

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。

Conjecture generation, sequence analysis, and experimental mathematics for mathematical discovery. Covers sequence analysis (OEIS lookups, recurrence relations, generating functions), pattern detection methods (finite differences, ratio analysis, modular patterns), conjecture generation workflow, classical integer sequences (Fibonacci, Catalan, partition numbers, Bernoulli numbers), combinatorial identities, the probabilistic method (Erdos, expected value arguments, Lovasz Local Lemma), AI-assisted discovery (FunSearch, Ramanujan Machine), and confidence calibration with cautionary tales (Mertens conjecture, Polya conjecture). Use when discovering patterns, analyzing sequences, generating conjectures, or working with combinatorial identities.

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。

Translation and interpretation as language meta-skills -- equivalence theory (Nida's formal vs. dynamic equivalence), translation strategies (borrowing, calque, transposition, modulation, adaptation), interpretation modes (simultaneous, consecutive, sight), source text analysis, register preservation, cultural adaptation, untranslatability and compensatory strategies, machine translation literacy, and back-translation for verification. Covers literary, technical, legal, and community interpreting contexts. Use when translating between languages, evaluating translation quality, teaching translation skills, or understanding the limits of cross-linguistic transfer.

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Design and analysis of combinational and sequential digital circuits using Boolean algebra, truth tables, K-maps, CMOS gate implementation, flip-flops, registers, counters, and finite state machines. Covers De Morgan's theorems, canonical sum-of-products and product-of-sums forms, minimization techniques, propagation delay, setup and hold times, metastability, synchronous design discipline, and FSM encoding. Use when designing, minimizing, verifying, or debugging any circuit whose signals take only discrete logic values.

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。

Systems programming from memory management through networking. Covers memory models (stack vs heap, manual allocation, garbage collection, ownership/borrowing), concurrency (threads, mutexes, channels, async/await, actor model, data races vs race conditions), operating system concepts (processes, virtual memory, page tables, system calls, file descriptors, signals), compilation (lexing, parsing, code generation, linking, static vs dynamic libraries), networking fundamentals (TCP/IP, sockets, HTTP, DNS, TLS), and the hardware-software boundary (caches, cache lines, false sharing, memory-mapped I/O). Use when working with low-level code, diagnosing system-level bugs, understanding performance characteristics, or bridging between high-level languages and machine 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。