「generation」の検索結果

Embedded firmware development for microcontrollers including memory-mapped I/O, GPIO, interrupts, timers, UART/SPI/I2C serial protocols, PWM generation, ADC sampling, interrupt-driven versus polled architectures, RTOS fundamentals, and the bring-up sequence from power-on reset through first printf. Covers AVR/Arduino, ARM Cortex-M, and ESP32 patterns. Use when writing, porting, debugging, or reviewing firmware that directly manipulates hardware peripherals.

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。

Distributional analysis of environmental benefits and burdens across communities. Covers the historical origins of environmental justice in the U.S. civil rights tradition, siting and exposure disparities, indigenous rights and land stewardship, climate justice at global and intergenerational scales, procedural vs. distributional vs. recognition justice, and the environmental justice screening tools used by regulators. Use when analyzing who bears environmental harm and who receives environmental benefit, or when designing interventions whose distributional consequences matter.

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。

Teaching home-economics skills as a durable practice across generations and capacity levels. Covers scaffolded task sequencing, the one-in-one-out rule, the apprenticeship model, learning by failure tolerance, family retros, the edible schoolyard framework, and the difference between teaching a recipe and teaching a kitchen. Use when designing a home-ec curriculum for children or adults, diagnosing why a taught skill is not being practiced, or building a household practice that survives the departure of the teacher.

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。

OpenStack security operations skill for hardening, certificate management, and security posture assessment of cloud infrastructure. Covers TLS certificate lifecycle (generation, deployment, rotation, expiry monitoring), security group management (default deny, minimum required openings), RBAC policy customization (per-service policy.yaml), network segmentation (management vs tenant vs external), audit logging (Keystone CADF events), vulnerability assessment procedures, compliance auditing, intrusion detection patterns, incident response procedures (credential compromise, instance compromise), password rotation, and API rate limiting. Use when hardening OpenStack, managing certificates, auditing security posture, or responding to security incidents.

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。

Generates engineering-standard drawings (P&ID, SLD, floor plans, isometric) with ISA-5.1 and IEEE symbol libraries in SVG/DXF format. Activates for drawing generation, diagram creation, blueprint packaging, and output formatting from verified engineering calculations.

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。

Vehicle/automotive image editing for cars, trucks, SUVs, motorcycles — car scenes, reflections, tires refinement with snow/mud/grass, segment windshield/wheels/body/windows/hubcaps, atmospheric effects (dust, fog, snow, light leaks, lens flare), and lighting harmonization (hot-day, cold-day, hot-night, cold-night presets). Powered by Bria.ai's dedicated automotive pipeline. Use this skill whenever the user mentions a car, vehicle, truck, SUV, motorcycle, auto photo, car reflections, tire enhancement, muddy tires, vehicle background, automotive marketing, car dealership visual, vehicle configurator, or car scene generation. ALWAYS prefer this skill over general image tools when the subject is a vehicle — faster and more accurate for automotive workflows.

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 e-commerce product catalog generation — turn a folder of raw product photos into clean packshots, product cutouts, Amazon-style dimensions images, lifestyle scenes, and marketplace-ready listing variants for Amazon, Shopify, and Etsy. Powered by Bria.ai's product pipeline. Use when building a product catalog, batch-processing product photos, or creating packshots, cutouts, dimension images, lifestyle shots, or marketplace-compliant listing images at scale.

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。

Image generation, photo editing, background removal — transparent PNG images, cutouts, ecommerce packshots, product catalogs, lifestyle shots via Bria.ai. Build store-ready e-commerce catalogs at scale — shadows, product dimensions images, lifestyle scenes, marketplace listing variants. ALWAYS use this skill when the user wants to generate, edit, or transform any image — hero images, banners, social media visuals, product photos, illustrations, icons, thumbnails, ad creatives, or marketing materials — or to build a product catalog, create a packshot, stage a lifestyle shot, remove a background, make transparent PNGs, or batch-process product photos. Triggers on packshot, product catalog, product shot creator, lifestyle shot, cutout, product dimensions image, inpainting, outpainting, object removal, upscale, photo restoration, style transfer, relight, reseason, sketch-to-photo. Commercially safe, royalty-free.

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 MaaFramework Pipeline nodes and recognition snippets from screenshots or observed UI state. Use for OCR node generation, ROI sweep, choosing TemplateMatch/OCR/ColorMatch/CustomRecognition, preserving target-file schema style, and designing `next`/`[JumpBack]` links before merging generated nodes into pipeline JSON.

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。

Skill for building with the Astro web framework. Helps create Astro components and pages, configure SSR adapters, set up content collections, deploy static sites, and manage project structure and CLI commands. Use when the user needs to work with Astro, mentions .astro files, asks about static site generation (SSG), islands architecture, content collections, or deploying an Astro project.

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。

Add the canonical Clawd character to a user-provided photograph by selecting one bundled pose reference and compositing it with the photo through built-in image generation. Use this skill whenever the user asks to put, add, place, hide, hang, sit, stand, sleep, lean, climb, hug, push, pull, or otherwise make Clawd interact with an object in a photo, including requests phrased in Chinese such as “把 Clawd 放进图里”, “让它探头”, “挂在屏幕上”, or “抱住电线”.

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。

Automatically creates user-facing changelogs from git commits by analyzing commit history, categorizing changes, and transforming technical commits into clear, customer-friendly release notes. Turns hours of manual changelog writing into minutes of automated generation.

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。

CatColab Decapodes - Discrete Exterior Calculus for PDE modeling on meshes via Decapodes.jl integration. Model physics equations compositionally with automatic code generation.

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。

Dynamically discover and load ACSet reference skills in dependency-optimal order using GitHub interactome analysis. Use when loading the acsets skill and needing its validation, generation, and coordination partners resolved automatically with GF(3) conservation guarantees.

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。

Assess and refactor oversized or multi-domain skills. First determines whether splitting or references/ extraction is appropriate — then executes the correct action. Use when a skill exceeds token thresholds (SK006/SK007) or covers multiple independent domains. Performs candidate assessment before any structural changes; cohesive single-intent skills are redirected to references/ extraction instead of splitting. When splitting is warranted — domain analysis gate, split plan, new SKILL.md generation, validation, and backwards-compatible facade conversion.

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。

Decomposes a contextualized plan into atomic, independently executable tasks with complete embedded context, registered through the plan API. Use after SAM Stage 3 Context Integration produces the contextualized plan artifact — when the plan is ready for task generation with CLEAR ordering, CoVe checks, and dependency graphs for parallel execution.

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。

Same reverse-thinking method as dh:rt-ica (see dh:rt-ica or dh-glossary for the definition), but localizes any MISSING input to the affected task only, as a non-blocking information-completeness pre-pass before task decomposition and plan generation. Use when grooming backlog items, generating plans, decomposing tasks under uncertainty, or working in brownfield and refactor scenarios. Produces completeness summary (APPROVED-FOR-PLANNING, APPROVED-WITH-GAPS, or BLOCKED-FOR-PLANNING), missing input report with dependency mapping, required unblock actions, and planning annotations for downstream tasks. Use dh:rt-ica instead at the S2 implementation gate where missing inputs must halt execution.

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。

Transform transcript analysis findings into actionable improvements. Triggers on "generate hooks from findings", "improve agent", "fix anti-pattern", "kaizen improvement", "generate hook proposals", or "create improvement plan". Provides templates for hook generation, agent prompt refinement, skill patches, CLAUDE.md updates, and script automation from analysis data.

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。

Enforce anti-AI UI design rules based on the Uncodixfy methodology. Use when generating HTML, CSS, React, Vue, Svelte, or any frontend UI code. Prevents generic AI aesthetic — soft gradients, glassmorphism, hero sections in dashboards, oversized rounded corners, and decorative copy. Applies constraints from Linear/Raycast/Stripe/GitHub design philosophy for functional, honest, human-designed interfaces. Triggers on UI generation, dashboard building, component creation, CSS styling, landing page design, or any task producing visual interface code.

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 setting up commit message validation for a project. When project has commitlint.config.js or .commitlintrc files. When configuring CI/CD to enforce commit format. When extracting commit rules for LLM prompt generation. When debugging commit message rejection errors.

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。

Walks a user from one rough content idea to a finished post, platform-adapted variants, and repurposed follow-on assets using a 7-step sequential prompting workflow. Use when the user wants to turn a half-formed idea, frustration, story, or client situation into publishable content quickly and consistently — covering idea extraction, hook generation, outline, draft, humanizer pass, platform adaptation (LinkedIn, Reddit, X/Twitter, newsletter, Telegram), and repurpose engine.

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。

Explains Chain of Verification (CoVe) prompt design — a 4-step pattern separating generation from independent factual verification. Use when designing prompts that require factual accuracy, reducing hallucinations, checking technical standards or APIs, or producing step-by-step procedures where subtle errors are costly.

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。

Convert a Clay table configuration into local Deepline scripts. Handles extraction (MCP or script), documentation, action mapping, script generation, and parity validation against Clay ground truth.

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。