「nat」の検索結果

Solves complex, open-ended problems using a knockout tournament mechanism. Generates an initial population of diverse candidate answers, pairs them into head-to-head matches, produces a hybrid "child" solution from each pair, and advances the best of the three through a bracket until a winner emerges. Use this skill whenever the user wants to competitively select the best answer from a field of alternatives, asks for a "tournament" or "bracket" style search, wants a faster alternative to full evolutionary search, or uses phrases like: "run a tournament on this", "bracket-style search", "let solutions compete", "knockout search", "find the best answer by elimination", or "which solution wins?". Also trigger when the problem is open-ended and the user wants a clear winner quickly rather than a deep multi-generation refinement.

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。

Control and interact with a live browser session on any scraped page — click buttons, fill forms, navigate flows, and extract data using natural language prompts or code. Use when the user needs to interact with a webpage beyond simple scraping: logging into a site, submitting forms, clicking through pagination, handling infinite scroll, navigating multi-step checkout or wizard flows, or when a regular scrape failed because content is behind JavaScript interaction. Also useful for authenticated scraping via profiles. Triggers on "interact", "click", "fill out the form", "log in to", "sign in", "submit", "paginated", "next page", "infinite scroll", "interact with the page", "navigate to", "open a session", or "scrape failed".

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。

Detect network reconnaissance and port scanning using Suricata and Snort IDS signatures, threshold-based detection rules, and traffic anomaly analysis to identify Nmap, Masscan, and custom scanning activity.

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。

Remote-sensing literature search, citation verification, sensor/platform/dataset query strategy, citation file management (.ris/.bib/.enw conversion), and reference management (BibTeX, related articles, DOI/arXiv lookup) across CrossRef, arXiv, Scopus, ScienceDirect, Semantic Scholar, and domain web sources. Use when the user needs coordinated multi-step literature workflows for remote sensing, Earth observation, geospatial AI, GIScience, photogrammetry, SAR, hyperspectral imaging, LiDAR, satellite missions, benchmark datasets, land-cover mapping, change detection, object detection, segmentation, retrieval, or multimodal remote-sensing foundation models. Also trigger on general literature-search needs during academic writing even without the word "Nature", such as searching for papers/literature, doing a literature review, verifying a citation, converting citation files, and Chinese phrasings like 文献检索、查文献、找文献、 文献综述检索、查论文、引文核对、参考文献管理、文献去重.

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 a complete but efficient Nature-style Chinese PPTX presentation from a remote-sensing, Earth-observation, geospatial AI, or broader scientific paper, preprint, PDF, article text, abstract, figure legends, or reading notes. Use this skill whenever the user asks to make slides/PPT/PPTX for journal club, group meeting, paper sharing, thesis seminar, lab meeting, department report, or academic presentation from a remote-sensing research paper. It identifies the paper type and argument, selects only the figures needed for the story, writes Chinese slide content and speaker notes, creates the actual .pptx deck, and runs an explicit self-review/corrective revision loop focused on figure quality, text overflow prevention, and non-template visual design before delivery. Also trigger on general academic-presentation requests even without the word "Nature", such as turning a paper into slides, building an academic talk deck, conference/defense presentations, and Chinese phrasings like 论文做PPT、遥感论文汇报、组会PPT、文献汇报、学术汇报、做幻灯片、讲paper、读书报告PPT.

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 orchestrator for B2B SaaS marketing campaigns using the CATALYST framework (Coordinated Agents for Targeted, Analytics-Led, Year-round SaaS Transformation). Coordinates 79 specialist agents across 17 marketing disciplines. Use this skill for ANY large-scale marketing initiative: full GTM launches, annual marketing plans, multi-channel campaigns, quarterly planning, or any task requiring coordination across content, SEO, paid media, social, email, design, sales enablement, product marketing, analytics, project management, and client operations. Also triggers on: marketing strategy, campaign plan, GTM launch, quarterly plan, marketing ops, full-funnel campaign, marketing audit, growth plan.

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。

legacy workaround を避け、semantic HTML / Web Platform Native API / modern CSS の利用機会を提示する。

Code-native UI design lifecycle engine (Figma alternative) with Contextual Decision Layer (CDL). Supports all 7 design stages with zero per-page CSS/JS and pluggable CSS foundations (Native, Tailwind, daisyUI, Pico, Hybrid). Trigger on commands 'brief', 'study', 'init', 'brand', 'tokens', 'palette', 'layout', 'components', 'page', 'dashboard', 'motion', 'i18n', 'audit', 'deploy', or design system requests.

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。

Prepares mobile releases across Flutter, native Android, native iOS, React Native and Kotlin Multiplatform, including separate Android/iOS artifacts from a shared KMP revision. Covers versions, signing, symbols, store readiness, rollout, release notes and CI/CD. Use for release audits, store submission, build pipelines or rollout planning.

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。

Diagnoses and prevents mobile performance regressions on every stack — Flutter, native Android, native iOS, React Native and Kotlin Multiplatform. Covers startup time, rendering and jank, memory, battery and background work, app size, the profiler and benchmark to use on each platform, and regression detection in CI. Use when an app is slow, janky, heavy or draining the battery, when setting performance budgets, or when a release must not regress.

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 and reviews offline-first mobile data on every stack — Flutter, native Android, native iOS, React Native and Kotlin Multiplatform. Covers the local store, the outbox of pending mutations, idempotency keys, retry and backoff, conflict resolution, reconnection, and how to test a flaky network deterministically. Use when an app must work without connectivity, when writes are lost or duplicated after reconnecting, or when local and server data disagree.

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 and reviews mobile architecture across Flutter, native Android, native iOS, React Native and Kotlin Multiplatform. Covers state ownership, data flow, modules, dependency boundaries, navigation, offline data and KMP shared source sets versus host apps. Use when structuring a feature, choosing state management, refactoring layers or deciding where a real class or screen belongs.

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。

Diagnoses build and runtime failures across Flutter, native Android, native iOS, React Native and Kotlin Multiplatform. Covers pub/Dart, Gradle/AGP, Xcode/CocoaPods, Metro, KMP source sets and iOS frameworks, crashes and deep links. Use for failing builds, app launches, stack traces or /reis-mobile:debug.

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。

Integrates and reviews Firebase across Flutter, native Android, native iOS, React Native and Kotlin Multiplatform. Covers Auth, databases, Storage, Functions, FCM, Analytics, Crashlytics, Remote Config, App Check and KMP shared-to-platform SDK boundaries. Use for any Firebase setup, bug, security rule, emulator, configuration file or product integration.

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 mobile security across Flutter, native Android, native iOS, React Native and Kotlin Multiplatform using OWASP MASVS. Covers credential storage, secrets, transport, deep links, WebViews, logging and platform implementations of shared KMP contracts. Use for security audits, auth/token handling, pentest findings or release hardening.

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。

Writes, runs and reviews mobile tests across Flutter, native Android, native iOS, React Native and Kotlin Multiplatform. Covers Dart widget/integration tests, JUnit/Compose/Espresso, XCTest/XCUITest, Jest/Detox, and KMP common/target tests. Use to add, fix, run or audit tests, test setup, mocks, coverage and flaky tests for a real app 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。

Designs and reviews production observability for mobile apps on every stack — Flutter, native Android, native iOS, React Native and Kotlin Multiplatform. Covers crash and error reporting with usable context, breadcrumbs, structured logs, traces and custom metrics, release and cohort segmentation, correlation with backend requests, alerting thresholds, sampling and cost, and what must never be collected. Use when production problems cannot be explained from the available data, when setting up crash reporting or telemetry, or when defining release health alerts.

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 the user wants to create competitor comparison or alternative pages for SEO and sales enablement. Also use when the user mentions 'alternative page,' 'vs page,' 'competitor comparison,' 'compa...

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 the user wants to humanize text, make writing sound less like AI, or rewrite content to feel more natural. Triggers on "humanize", "make it sound natural", "too robotic", "sounds like AI", or similar. Works in Hebrew and English. Always output only the rewritten text — no explanations.

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 working with Gcp Idle Resources — detect unused and idle GCP resources that incur cost without providing value. Covers unattached Persistent Disks, unused external IPs, stopped VMs, idle Cloud NAT, idle load balancers, old snapshots, idle Cloud SQL instances, abandoned GKE node pools, unused VPC connectors, and orphaned Filestore instances. Includes estimated monthly waste per resource and anti-hallucination rules for safe detection.

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 read-only MongoDB queries (find) or aggregation pipelines using natural language, with collection schema context and sample documents. Use this skill whenever the user asks to write, create, or generate MongoDB queries, wants to filter/query/aggregate data in MongoDB, asks "how do I query...", needs help with query syntax, or discusses finding/filtering/grouping MongoDB documents. Also use for translating SQL-like requests to MongoDB syntax. Does NOT handle Atlas Search ($search operator), vector/semantic search ($vectorSearch operator), fuzzy matching, autocomplete indexes, or relevance scoring - use search-and-ai for those. Does NOT analyze or optimize existing queries - use mongodb-query-optimizer for that. Does NOT handle aggregation pipelines that involve write operations. Requires MongoDB MCP server.

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。

Make .NET projects compatible with Native AOT and trimming by systematically resolving IL trim/AOT analyzer warnings. USE FOR: making projects AOT-compatible, fixing trimming warnings, resolving IL warnings (IL2026, IL2070, IL2067, IL2072, IL3050), adding DynamicallyAccessedMembers annotations, enabling IsAotCompatible. DO NOT USE FOR: publishing native AOT binaries, optimizing binary size, replacing reflection-heavy libraries with alternatives. INVOKES: no tools — pure knowledge skill.

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。

Symbolicate .NET runtime frames in Apple platform .ips crash logs (iOS, tvOS, Mac Catalyst, macOS). Extracts UUIDs and addresses from the native backtrace, locates dSYM debug symbols, and runs atos to produce function names with source file and line numbers. Automatically downloads .dwarf symbols from the Microsoft symbol server using Mach-O UUIDs. USE FOR triaging a .NET MAUI or Mono app crash from an .ips file on any Apple platform, resolving native backtrace frames in libcoreclr or libmonosgen-2.0 to .NET runtime source code, retrieving .ips crash logs from a connected iOS device or iPhone, or investigating EXC_CRASH, EXC_BAD_ACCESS, SIGABRT, or SIGSEGV originating from the .NET runtime. DO NOT USE FOR pure Swift/Objective-C crashes with no .NET components, or Android tombstone files. INVOKES Symbolicate-Crash.ps1 script, atos, dwarfdump, idevicecrashreport.

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。

Alternative Slowbar coffee intelligence — scientific analysis from seed to cup using First Principle + System Thinking. Covers: varieties, terroir, cultivation, disease diagnosis, processing, roasting physics/chemistry, extraction science, brewing, business strategy, storytelling. ALWAYS trigger for coffee-related questions requiring scientific depth. DO NOT trigger for simple recipes or general food questions. Style: science-driven, farmer-first, quality-obsessed.

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。