「deep」の検索結果

Complete PortSwigger deep-dive with exact payloads for every lab variant including zero-day techniques

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。

Complete PortSwigger deep-dive with exact payloads for every lab variant including zero-day techniques

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。

Complete PortSwigger deep-dive with exact payloads for every lab variant including zero-day techniques

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。

Complete PortSwigger deep-dive with exact payloads for every lab variant including zero-day techniques

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 linking patterns - Universal Links (iOS), App Links (Android), URI schemes, expo-linking API, React Navigation linking config, Expo Router automatic linking, AASA/assetlinks.json setup, deferred deep links, testing

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 research on technical topics using EXA tools with intelligent two-tier caching. Use when user asks to research a topic, investigate best practices, look up information, find patterns, or explore architectures. Also invoked by /research command. Triggers: "research", "look up", "investigate", "deep dive", "find information about", "what are best practices for", "how do others implement".

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 a decision needs deep multi-source technical research with cited evidence — technology evaluation, ecosystem comparison, standards/spec fact-finding, "how do others solve X". Triggers on "research X", "deep dive on X", "evaluate X vs Y", "find best practices for X". NOT for library/API doc lookup (see mk:docs-finder); NOT for one-shot URL fetch (see mk:web-to-markdown); NOT for internal codebase discovery (see mk:scout); NOT for project-only Q&A (see mk:ask-me); NOT for comparing solution designs (see mk:brainstorming); NOT for root-cause debugging (see mk:investigate); NOT for wiki ingestion of sources (see mk:wiki-research).

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 deployment-focused distillation systems that balance model size, accuracy, calibration, and cascade escalation under real resource limits. Best for teacher-student compression, threshold design, and failure-aware deployment. Activate on "model compression", "teacher- student", "distillation score", "cascade model", "edge deployment", or "model calibration". NOT for generic deep-learning overviews, prompt optimization, or training work without a concrete distillation objective.

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 analysis of knowledge distillation techniques for compressing large models into smaller efficient ones

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。

Strategic patterns for solving intractable problems through cascading approximation, self-improvement, and heterogeneous evaluation from DeepMind's AlphaGo system

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。

Visualises RNA-modification data with transcript-feature metagene plots (Guitar GuitarPlot; MetaPlotR; deepTools computeMatrix scale-regions), peak-centred heatmaps (ComplexHeatmap; deepTools plotHeatmap), IP-vs-input paired browser tracks (log2 IP/input bigWig via deepTools bamCompare; pyGenomeTracks; Gviz; IGV/UCSC track hubs), DRACH sequence-logo plots (ggseqlogo; MEME), feature-distribution stacked bars, and volcano/MA plots for differential modification. Establishes stop-codon enrichment in the metagene plot as the biological QC anchor for any MeRIP dataset (Dominissini 2012; Meyer 2012). Use when producing the canonical metagene plot with stop-codon enrichment as a QC anchor, building paired IP/input genome-browser tracks at single-locus resolution, plotting peak-centred heatmaps clustered by condition, summarising peak distribution across transcript features, generating DRACH motif logos as sanity checks, rendering volcano plots of differential m6A, or reproducing the stop-codon enrichment plot.

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。

Sequence-based deep learning for ATAC-seq using chromBPNet, BPNet, scBasset, or Enformer. Use when correcting Tn5 bias with neural networks beyond k-mer models, predicting per-base accessibility profiles, scoring in silico variant effects at GWAS or rare-variant SNPs, discovering motifs via DeepLIFT/TF-MoDISco from a trained model, or generating cell-type-specific accessibility predictions for unobserved cell states.

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。

Shared vocabulary for designing deep modules. Use when the user wants to design or improve a module's interface, find deepening opportunities, decide where a seam goes, make code more testable or AI-navigable, or when another skill needs the deep-module vocabulary.

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 analysis of articles/long-form via thinking frameworks (SCQA, mental models, inversion) — 'analyze article', 'deep dive', 'extract insights', URL/text wanting depth not summary.

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。

Shared vocabulary for designing deep modules. Use when the user wants to design or improve a module's interface, find deepening opportunities, decide where a seam goes, make code more testable or AI-navigable, or when another skill needs the deep-module vocabulary.

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。

Cursorで設計判断の重い依頼を、必要な探索・独立検討・統合から実装可能な計画へ整理する。親が計画と受入を所有し、固定人数・ラウンド・成果物を完了条件にしない。ユーザーが /deepplan と入力したときに使う。

Cursorで複雑な問いを、必要な探索・独立したコンテキストでの熟考・統合・十分性確認へ分けて考える。single/panelの方式を使い、親だけで熟考を完了させない。ユーザーが /deepthink と入力したときに使う。

設計判断が重い依頼について、必要な探索と独立した熟考を計画へ統合する親向けスキル。明示された独立性やモデル方式を保ち、固定人数・固定ラウンド・不要な成果物を完了条件にしない。ユーザーが /deepplan と入力したら使う。

難しい意思決定や論点を、必要な探索・独立したコンテキストでの熟考・統合・十分性確認へ分けて考える。single/panelの方式を使い、親だけで熟考を完了させない。調査はresearch、実装やbug修正はpir2/debugを使う。ユーザーが /deepthink と入力したときに使う。

Decision protocol for strategic work: recall the knowledge vault, run a gap analysis, emit a deep-research prompt for external tools, then synthesize the returned reports into a decision memo. Use before any new product, feature, market, pricing, GTM or architecture call, where local recall alone is not enough. Triggers: "/alfa-search-recall-deepresearch", "alpha protocol", "R+DR".

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。

Implements declarative Flutter routing using GoRouter v17 with URL-based navigation, deep linking, and authentication guards. Activates when configuring GoRoute path patterns, setting up StatefulShellRoute for persistent bottom navigation state, implementing ShellRoute for shared nested layouts, adding redirect guards with loop prevention (idempotent redirects), configuring deep links for iOS (FlutterDeepLinkingEnabled) or Android (intent-filters), debugging redirect loops or navigation stack issues, handling 404 error routes with errorBuilder, using named routes with path/query/extra parameters, or migrating from imperative Navigator.push to declarative routing. Ideal for Flutter web apps requiring URL bar synchronization, mobile apps with universal links or app links, or multi-level navigation hierarchies.

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 Deno Deploy Deep — deep Deno Deploy analysis covering project inventory, deployment history, KV database usage, cron job schedules, custom domain mappings, analytics metrics, and environment variable auditing. Use for comprehensive Deno Deploy platform management.

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 Dagster Deep — dagster deep data orchestration management covering job and asset inventory, run monitoring, schedule and sensor health, partition status, resource configuration, and daemon health checks. Use when investigating run failures, analyzing asset materialization, monitoring schedule ticks, or auditing Dagster configurations.

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 Cloudflare Workers Deep — deep analysis of Cloudflare Workers deployments including script inventory, route mappings, KV namespace usage, Durable Objects, cron triggers, CPU time metrics, and error rate tracking. Use for comprehensive Workers platform health checks and optimization.

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。