「learning」の検索結果

Expert-level computational biology covering molecular dynamics simulation, protein structure prediction, systems biology, network analysis, and machine learning in biology.

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Expert-level linear algebra knowledge. Use when working with vectors, matrices, determinants, eigenvalues, linear transformations, vector spaces, inner products, decompositions, or applications in machine learning and physics. Also use when the user mentions 'matrix', 'determinant', 'eigenvalue', 'eigenvector', 'linear transformation', 'vector space', 'basis', 'orthogonality', 'SVD', 'PCA', 'least squares', 'null space', or 'rank'.

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-level bioinformatics covering sequence analysis, structural bioinformatics, database searching, phylogenetic analysis, NGS data processing, single-cell analysis, and machine learning in biology.

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。

Claude Code workflow tips from Boris Cherny (creator of Claude Code) and the Claude Code team. Use when: setting up Claude Code, optimizing workflows, running parallel sessions, configuring CLAUDE.md, using skills/commands, subagents, hooks, MCP integrations, or learning best practices. Covers: git worktrees, plan mode, verification, permissions, Slack MCP, BigQuery, prompting tips, plugins, custom agents, sandboxing, keybindings, status lines, output styles, customization, /simplify for code quality, /batch for parallel code migrations, /loop for scheduled tasks, code review agents, /btw for mid-task questions, /effort max reasoning, remote control sessions, voice mode, setup scripts, session naming, /color, PostCompact hook, auto mode, /schedule cloud jobs, iMessage plugin, auto-memory, and auto-dream, mobile app, session teleporting, Cowork Dispatch, Chrome extension, Desktop app, /branch forking, --bare SDK startup, --add-dir multi-repo, --agent custom agents, /voice input.

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。

Codex workflow tips from Boris Cherny (creator of Codex) and the Codex team. Use when: setting up Codex, optimizing workflows, running parallel sessions, configuring AGENTS.md, using skills/commands, subagents, hooks, MCP integrations, or learning best practices. Covers: git worktrees, plan mode, verification, permissions, Slack MCP, BigQuery, prompting tips, plugins, custom agents, sandboxing, keybindings, status lines, output styles, customization, /simplify for code quality, /batch for parallel code migrations, /loop for scheduled tasks, code review agents, /btw for mid-task questions, /effort max reasoning, remote control sessions, voice mode, setup scripts, session naming, /color, PostCompact hook, auto mode, /schedule cloud jobs, iMessage plugin, auto-memory, and auto-dream, mobile app, session teleporting, Cowork Dispatch, Chrome extension, Desktop app, /branch forking, --bare SDK startup, --add-dir multi-repo, --agent custom agents, /voice input.

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Map existing materials into a first knowledge record, or investigate a subject's history in depth. Use whenever the user asks to map, survey, go through, reconstruct or investigate what already exists - folders of documents, decision records, meeting notes, emails, exports, code - or wants a starting record built from existing materials, even when the word 'record' is never said: consult this before listing or reading the files yourself, because a map has an agreed scope, a protocol and a report. Also, once, at a suitable moment, to offer the starting choices where a workspace has no record and the work is clearly ongoing - never on a greeting. Learning while working needs no map. Requests come in any language.

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Route data analysis and machine learning work to the right skill in this suite. Use when starting any analysis, modeling, validation, or reproducibility task and the matching specialized skill is not yet clear; not needed when one specific skill already clearly applies.

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Orchestrate an end-to-end data analysis or machine learning project from decision framing through reproducible handoff. Use when a request spans multiple lifecycle stages or an ambiguous modeling request must become a decision-ready result; use narrower audit or experiment-design skills for isolated reviews.

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Design leakage-safe machine learning experiments that mirror real deployment and support fair model comparisons. Use when defining prediction timing, feature eligibility, train-validation-test splits, baselines, metrics, or controlled model iterations; not for auditing whether raw labels are trustworthy.

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Run the skill-improvement pipeline after a learning was detected — capture, classify, create, validate, apply; explicit request only.

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Use when a learning, new skill, rule improvement, or bug fix from a consumer project should be contributed back to the shared agent-config package.

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。

Blind dual-implementation pattern where two agents independently create artifacts, then a third agent judges which is better and synthesizes learnings.

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Development skill from everything-claude-code

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Fornece orientação para treinamento RL agentico nativo do PyTorch usando torchforge, biblioteca da Meta que separa infraestrutura de algoritmos. Use quando você quer abstrações limpas de RL, fácil experimentação de algoritmos, ou treinamento escalável com Monarch e TorchTitan.

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Fornece orientação para treinamento RL de nível empresarial usando miles, um fork pronto para produção do slime. Use ao treinar grandes modelos MoE com FP8/INT4, necessitando alinhamento treino-inferência ou exigindo RL especulativo para máxima taxa de transferência.

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。

Fornece orientação para pós-treinamento de LLM com RL usando slime, um framework Megatron+SGLang. Use ao treinar modelos GLM, implementar fluxos de trabalho personalizados de geração de dados, ou precisar de integração estreita com Megatron-LM para escalabilidade em RL.

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Orientação especializada para ajuste fino com GRPO/RL usando TRL para treinamento de modelos com raciocínio e tarefas específicas

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Fornece orientação para treinar LLMs com aprendizado por reforço usando verl (Volcano Engine RL). Use ao implementar RLHF, GRPO, PPO ou outros algoritmos de RL para pós-treinamento de LLMs em escala com backends de infraestrutura flexíveis.

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。

Cientista de dados especializado em análises avançadas, machine learning e modelagem estatística. Trabalha com análise de dados complexa, modelagem preditiva e business intelligence.

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[Content] Use when you need to build structured learning/teaching course material with Bloom taxonomy objectives, modules, lessons, exercises, and assessments.

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。

辅导中国中小学生作业的家长助手。当用户要"辅导作业""孩子这题不会""怎么给孩子讲""检查作业""孩子不想写作业"时使用。重点是教家长怎么引导，而不是直接给答案。

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。

中文拆书与读书笔记助手。当用户要"拆书""读书笔记""这本书讲什么""帮我读这本书""做读书卡片""写书评""整理一本书的框架"时使用。重点是把书变成能用的东西，不是复述目录。

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。

生成中国中小学教案与配套材料。当用户要"备课""写教案""设计一节课""做课件大纲""出分层作业""写说课稿""评课"时使用。覆盖语文/数学/英语/科学等学科，小学到高中；先确认学段、教材版本与适用课程标准，再按学科核心素养、可观察任务和评价证据组织目标。

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。

Decision transparency, feedback collection, and debate invitations for AID methodology. Active in ALL phases for ALL roles.

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。