459 skills · sorted by relevance
Helps startup leaders make strategic data decisions: assess permission to train AI on customer data, choose data architecture, evaluate customer data as a business asset, and sequence hiring around the decisions the business needs to make.
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。
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Review data strategy plans through six focused questions covering training data permissions, infrastructure choices, data asset value, and hiring. Produce a structured verdict with unresolved issues and three concrete next steps.
Prepare datasets for language model training by formatting examples, checking conversation templates, and packing examples into training sequences. Document data sources, synthetic data ratios, and validation results in a dataset card before starting a run.
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.
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Build data processing pipelines for machine learning, from preprocessing to batch predictions. Uses Ray Data to handle datasets larger than memory, distribute work across CPUs and GPUs, and feed data into training frameworks.
Build data processing pipelines for machine learning, from preprocessing to batch predictions. Uses Ray Data to handle datasets larger than memory, distribute work across CPUs and GPUs, and feed data into training frameworks.
Prepare training datasets for large language models with GPU-assisted cleaning. Covers duplicate removal, quality filtering, and personal information redaction, with workflows for text, images, video, and audio using NeMo Curator.
Prepare training datasets for large language models with GPU-assisted cleaning. Covers duplicate removal, quality filtering, and personal information redaction, with workflows for text, images, video, and audio using NeMo Curator.
Plan further training for AI agent models by diagnosing gaps in knowledge, output consistency, or decision making. Compare continued pretraining, supervised fine-tuning, reinforcement learning, and distillation, then design training data, feedback environments, and evaluation checks.
Find and download public cancer imaging datasets from NCI Imaging Data Commons. Filter scans and pathology images by metadata, preview them in a browser, check licenses, and generate source citations for research or AI training.
Build training datasets for LLM specialization from production data, frontier model distillation, and synthetic bootstrapping. Use when formatting production logs into SFT data, distilling from frontier APIs, or preparing data for fine-tuning. Covers JSONL formatting, data quality validation, deduplication, and train/eval splitting.
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Convert recorded robot workflows from HDF5 into local LeRobot datasets for training or browser inspection. Preserves actions, state, camera recordings, and task labels, then checks episode counts, metadata, videos, and data dimensions.
Check training data and machine learning models for malicious changes, including backdoors that cause unexpected behavior on specific inputs. Combine data quality checks, model analysis, and file integrity verification, then document findings and retest after remediation.
Guides LLM training with verl using rewards to improve model responses. Covers data preparation, reward functions, GRPO and PPO configuration, distributed GPU training, and troubleshooting memory shortages or unstable training.
Adapt large language models to specific tasks through fine-tuning, or additional training. Prepares and validates datasets, configures LoRA or QLoRA to train a small subset of parameters, and supplies training settings, evaluation scripts, and deployment optimization guidance.
Build machine learning workflows that connect data checks, training, evaluation, and deployment. The skill implements experiment tracking, model version management, and automated retraining, with reproducible environments and explicit validation thresholds before models enter production.
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).
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Prepare high-quality datasets for LLM fine-tuning with filtering, deduplication, augmentation, and RLHF data formatting. Activate on: fine-tuning data, training data curation, RLHF dataset, data quality filtering, SFT dataset. NOT for: model training infrastructure (ai-engineer), prompt engineering without fine-tuning (prompt-engineer).
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Prepare high-quality datasets for LLM fine-tuning with filtering, deduplication, augmentation, and RLHF data formatting. Activate on: fine-tuning data, training data curation, RLHF dataset, data quality filtering, SFT dataset. NOT for: model training infrastructure (ai-engineer), prompt engineering without fine-tuning (prompt-engineer).
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Helps write and review PyTorch models, training loops, and data loading code. Covers repeatable experiments, saving training progress, and techniques for reducing GPU memory use or improving training speed, with practical code examples.
Organize PyTorch models and data pipelines with PyTorch Lightning, automate training, save checkpoints, and track experiments. Includes templates and guidance for configuring training across multiple GPUs or TPUs.
Set up reinforcement learning, which trains models using rewards, with verl. Follow workflows for preparing data, configuring GRPO or PPO training, scaling across GPUs, and addressing memory errors or unstable training.
Guide reinforcement learning, training models through rewards, with slime. Prepare data, configure Megatron-LM training and SGLang response generation, and set up asynchronous runs or custom multi-turn workflows for supported language models.