922 件 ・ 関連度順
概要と使いどころ
コミュニケーション診断ツール（commu-checker）の各タイプ向け、90日トレーニングコンテンツを生成するスキル。 ユーザーが「D1型のトレーニングを作って」「〇〇型の90日プランを作りたい」「training-data.jsに追加したい」 「コミュ診断のトレーニングページ」などと言ったときに必ず使う。 タイプ定義・観察メモを受け取り、既存サイト（M PLUS Rounded 1c・グリーン基調・Material Icons）に合わせた インタラクティブなチェックリスト用HTMLコンテンツを training-data.js の TRAINING_PAGES オブジェクトに追加する形で出力する。 トレーニングは trainings/index.html + trainings/training-app.js + trainings/training-data.js の3ファイル構成。 個別の <type>.html は存在せず、URL の ?type=d1 などのパラメータで表示タイプを切り替える。
Implements HIPAA workforce training requirements under 45 CFR §164.530(b) (Privacy Rule) and 45 CFR §164.308(a)(5) (Security Rule). Covers initial onboarding training, periodic refresher cadence, role-based content differentiation, documentation of training completion, and sanction policy integration. Keywords: HIPAA training, workforce training, security awareness, privacy training, §164.530(b), §164.308(a)(5).
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。
予約済みのItô GPU環境で機械学習の訓練が利用可能かを確認するスキル。現状は訓練を実行せず、未提供の機能と将来の引き渡し条件を整理します。
Design a GxP training programme covering training needs analysis by role, curriculum design (regulatory awareness, system-specific, data integrity), competency assessment criteria, training record retention, and retraining triggers for SOP revisions and incidents. Use when a new validated system requires user training before go-live, an audit finding cites inadequate training, organisational changes introduce new roles, a periodic programme review is due, or inspection preparation requires demonstrating training adequacy.
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のモデルや学習コードを作成・点検し、データ読み込み、実験の再現性、学習状態の保存、GPUメモリや処理速度の改善を支援するスキル。
Classifies sensitive data in AI/ML training datasets including bias detection for Art. 9 categories, data card documentation, provenance tracking, and consent verification for model training. Keywords: AI training data, ML dataset, bias detection, data card, model training, Art 9, consent, GDPR AI.
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。
Manages AI model retention and machine unlearning requirements. Covers training data deletion verification, model versioning for compliance, machine unlearning techniques (SISA, gradient-based), and retraining triggers. Keywords: AI retention, machine unlearning, model versioning, training data deletion, retraining, storage limitation.
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 planning user adoption, structuring Salesforce training materials, drafting release communications, or running a change impact assessment for a Salesforce rollout or update. Triggers: user adoption plan, training materials, release announcement, change impact, go-live communication, communication plan, training plan by persona, adoption metrics, LoginHistory adoption report, PromptAction, training sandbox, go-live checklist, post-go-live feedback, super user program, pilot group. NOT for org deployment mechanics or sandbox promotion — use admin/change-management-and-deployment. NOT for adoption of an Agentforce or Einstein AI feature — use admin/ai-adoption-change-management. NOT for configuring the in-app prompts themselves — use admin/in-app-guidance-and-walkthroughs.
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。
Synthesises training survey responses and manager notes into a DRAFT training needs report by role: coded needs with record counts per role (n of N), the evidence type behind each count, anonymised quotes cited to record codes, disagreements between what staff report and what managers observe, gaps where a role is under-represented or a required skill has no evidence, and follow-up questions to settle before any course is designed. Never rates an individual, ranks a team or turns a count into a competence verdict. Use when the user asks to "analyse this training survey", "what training do our teams need", "summarise the manager feedback on skills gaps", "build a training needs analysis by role" or "which roles have the biggest skill gaps". Do not use for turning an agreed need into a course, use course-outline-builder instead; for exit interviews, use exit-interview-synthesis. Drafts for human review; never approves, authorises or signs off.
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。
言語モデルに指示への応答や好ましい回答を学習させるため、TRLによる追加学習を案内するスキル。データ準備、学習方式の選択、報酬モデルの学習や評価を扱います。
Provides guidance for training LLMs with reinforcement learning using verl (Volcano Engine RL). Use when implementing RLHF, GRPO, PPO, or other RL algorithms for LLM post-training at scale with flexible infrastructure backends.
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。
Provides PyTorch-native distributed LLM pretraining using torchtitan with 4D parallelism (FSDP2, TP, PP, CP). Use when pretraining Llama 3.1, DeepSeek V3, or custom models at scale from 8 to 512+ GPUs with Float8, torch.compile, and distributed checkpointing.
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。
Provides guidance for training LLMs with reinforcement learning using verl (Volcano Engine RL). Use when implementing RLHF, GRPO, PPO, or other RL algorithms for LLM post-training at scale with flexible infrastructure backends.
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。
Battle-tested PyTorch training recipes for all domains — LLMs, vision, diffusion, medical imaging, protein/drug discovery, spatial omics, genomics. Covers training loops, optimizer selection (AdamW, Muon), LR scheduling, mixed precision, debugging, and systematic experimentation. Use when training or fine-tuning neural networks, debugging loss spikes or OOM, choosing architectures, or optimizing GPU throughput.
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。
Provides PyTorch-native distributed LLM pretraining using torchtitan with 4D parallelism (FSDP2, TP, PP, CP). Use when pretraining Llama 3.1, DeepSeek V3, or custom models at scale from 8 to 512+ GPUs with Float8, torch.compile, and distributed checkpointing.
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 fine-tuning LLMs, training custom models, or adapting foundation models for specific tasks. Invoke for configuring LoRA/QLoRA adapters, preparing JSONL training datasets, setting hyperparameters for fine-tuning runs, adapter training, transfer learning, finetuning with Hugging Face PEFT, OpenAI fine-tuning, instruction tuning, RLHF, DPO, or quantizing and deploying fine-tuned models. Trigger terms include: LoRA, QLoRA, PEFT, finetuning, fine-tuning, adapter tuning, LLM training, model training, custom model.
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。
Battle-tested PyTorch training recipes for all domains — LLMs, vision, diffusion, medical imaging, protein/drug discovery, spatial omics, genomics. Covers training loops, optimizer selection (AdamW, Muon), LR scheduling, mixed precision, debugging, and systematic experimentation. Use when training or fine-tuning neural networks, debugging loss spikes or OOM, choosing architectures, or optimizing GPU throughput.
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。
Assesses lawful basis for AI training data processing per EDPB April 2025 report on LLMs and general-purpose AI. Covers legitimate interest balancing tests, consent challenges for ML training, public dataset assessment, and web scraping lawfulness. Keywords: AI training data, lawful basis, EDPB LLM, legitimate interest, consent, web scraping.
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。
Audit existing model training code — find reproducibility issues, data leakage, and missing best practices. Use when asked to "audit our training code", "is our training reproducible", or "check for training data leakage".
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。
Foundation obedience training for dogs — sit, stay, come, heel, and down using positive reinforcement and marker training. Covers timing, reward hierarchy, session structure, distraction proofing, and common handler errors. Use when a new puppy (8+ weeks) is ready for foundation training, an adult dog lacks reliable basic commands, a rescue dog needs to learn the household's command vocabulary, before advancing to complex behaviors or off-leash work, or when existing commands have degraded and need re-establishing.
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 a DRAFT course outline from a training need and audience: learning objectives with an observable verb, condition and standard, modules in a stated order with a duration basis and sources, activities and an assessment plan aligned objective by objective, and a coverage table showing what each objective still lacks. Never sets a pass mark, declares learners competent or invents content. Use when the user asks to "build a course outline", "design a training programme for", "turn this need into modules and objectives", "draft the curriculum for this workshop" or "what should this training cover". Do not use for quiz items from finished content, use training-quiz-builder instead; for needs from surveys and manager notes, use training-needs-synthesis; for a new starter's first weeks, use onboarding-plan-builder. Drafts for human review; never approves, authorises or signs off.
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。
Provides PyTorch-native distributed LLM pretraining using torchtitan with 4D parallelism (FSDP2, TP, PP, CP). Use when pretraining Llama 3.1, DeepSeek V3, or custom models at scale from 8 to 512+ GPUs with Float8, torch.compile, and distributed checkpointing.
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。
機械学習モデルを本番で使うために、データの仕様、再現可能な学習、品質評価、配備、監視、切り戻しを整理し、実装計画やレビュー項目にまとめるスキル。
An IT security-awareness training on spotting phishing — the tells, the drill, and what to do in the first 60 seconds. Built as a decision-grade professional training deck for all employees.
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。