「deep」の検索結果

Run iterative IA deepening, finalize shard, update indexes/tracker, and enforce ambiguity gates before handoff

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。

Execute recursive breadth-before-depth ideation discovery with Deep Think, CX gate enforcement, and feature deepening

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 Cypress Deep — cypress end-to-end testing management and analysis. Covers test execution monitoring, dashboard integration, screenshot and video analysis, test report parsing, flaky test detection, and configuration auditing. Use when investigating test failures, reviewing Cypress Cloud results, or managing Cypress test suites.

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 Aws Ses Deep — advanced AWS SES management including sending quotas, identity verification, configuration sets, dedicated IPs, reputation dashboard, suppression list, email receiving rules, and deliverability advisor. Goes beyond basic SES to cover DKIM/DMARC compliance, event publishing, and virtual deliverability manager.

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 Aws Cdk Deep — aWS CDK application and stack management. Covers stack synthesis, CloudFormation template inspection, diff analysis, deployment orchestration, context values, asset management, and construct library usage. Use when managing CDK apps, debugging synthesis errors, reviewing stack diffs, or analyzing deployed CloudFormation resources.

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 Algolia Deep — advanced Algolia search platform management including indices, search analytics, A/B tests, query rules, synonyms, API key audit, and infrastructure monitoring. Covers search performance metrics, relevance tuning, click-through analysis, indexing health, and usage quota tracking.

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 Bigquery Deep — google BigQuery job analysis, slot utilization, storage optimization, materialized views, BI Engine capacity, and query performance diagnostics via bq CLI and gcloud.

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 Firestore Deep — google Cloud Firestore document and collection analysis, index management, security rules review, usage monitoring, and performance diagnostics via gcloud CLI.

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。

Data scientist v3 — ML, deep learning, NLP, CV, experimentation, MLOps

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。

PyTorch, TensorFlow, neural networks, CNNs, transformers, and deep learning for production

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 guidance for deep learning, transformers, diffusion models, and LLM development with PyTorch, Transformers, Diffusers, and Gradio.

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。

Guidelines for deep learning development with PyTorch, Transformers, Diffusers, and Gradio for LLM and diffusion model work.

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。

This is the core skill for the deep research and writing tool—combining AI-driven deep research with well-formatted written outputs. Every document produced is backed by comprehensive literature search and verified citations through the research-lookup 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。

web アプリの機能を構造化して書き下すワークフロー。新規/既存の 2 モード、5 フェーズで構造化（先行事例調査は並列3エージェント、Reader Test 付き）。「機能を考えたい」「機能定義したい」「web アプリ作りたい」「機能から設計したい」「新しい機能」で発動。※ 曖昧な状態の掘り下げは deep-interview、要件統合は discovery-council、設計は design を使う。

完成した plan/design を対話で容赦なく詰問し、実装前に穴を潰す。決定木を1枝ずつ降り、各質問に推奨回答を添える。コードで答えが出る点は聞かず自分で調べて埋める。「grill して」「叩いて」「この設計で大丈夫?」「plan を詰めて」「設計を対話レビュー」で発動。※ ゼロから要件を掘り下げるなら deep-interview、非対話で一括批判レポートなら critic。

Produces thorough, multi-source cited research reports from web and news without paid APIs. Use when the user asks for deep research, landscape analysis, competitive intel, market research, or a cited report needing broad source coverage.

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。

Agente que simula Andrej Karpathy — ex-Director of AI da Tesla, co-fundador da OpenAI, fundador da Eureka Labs, e o maior educador de deep learning do mundo.

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。

Ses steganografi — WAV LSB, DTMF decode, spektogram analizi, DeepSound, Morse code ve Python ile tam çözüm şablonları.

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 multi-source research synthesis — web, academic, patent, and internal KB cross-referencing

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。

Write, review, and improve blog posts for the Sentry engineering blog following Sentry's specific writing standards, voice, and quality bar. Use this skill whenever someone asks to write a blog post, draft a technical article, review blog content, improve a draft, write a product announcement, create an engineering deep-dive, or produce any written content destined for the Sentry blog or developer audience. Also trigger when the user mentions "blog post," "blog draft," "write-up," "announcement post," "engineering post," "deep dive," "postmortem," or asks for help with technical writing for Sentry. Even if the user just says "help me write about [feature/topic]" — if it sounds like it could become a Sentry blog post, use this 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。

High-performance RLHF framework with Ray+vLLM acceleration. Use for PPO, GRPO, RLOO, DPO training of large models (7B-70B+). Built on Ray, vLLM, ZeRO-3. 2× faster than DeepSpeedChat with distributed architecture and GPU resource sharing.

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。

Training patterns for autoregressive neural PDE solvers (FNO, DeepONet, CNO). Covers rollout training, noise injection for stability, multi-component loss functions (H1, frequency-sensitive, boundary-aware), per-channel normalization for coupled multi-variable systems, and the PDEBench nRMSE metric. Use when training any neural operator that predicts time-dependent PDE solutions.

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 guidance for fine-tuning LLMs with Axolotl - YAML configs, 100+ models, LoRA/QLoRA, DPO/KTO/ORPO/GRPO, multimodal support

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。

Builds and validates predictive time-to-event models on clinical and omics data with penalized Cox, random survival forests, gradient-boosted and deep survival models, and prediction-grade evaluation (Uno's C, time-dependent AUC, integrated Brier, calibration, competing risks). Use when building an individualized risk predictor or prognostic omics signature, choosing a survival model, or evaluating one beyond the C-index. For Kaplan-Meier, log-rank, and classical Cox hazard-ratio inference in a trial see clinical-biostatistics/survival-analysis.

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。