「soft」の検索結果

Romantic couple hugging on a beach at sunset, cinematic lighting, soft focus, using reference faces

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。

Code auditor that grades software projects and the agents/developers who built them. Produces structured JSON + Markdown reports with a standardized rubric. Evaluates architecture, correctness, testing, authorship cohesiveness, ghost code, and more. Optionally ingests tmux session logs for process evidence and interrogates agents about specific problems found. Use when asked to "audit", "grade", "evaluate", "review code quality", "rate this project", "score the agents", or "assess the codebase". Especially useful after Marcus multi-agent experiments to grade agent output and track quality over time.

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。

Guide technical communication for software developers. Covers email structure, team messaging etiquette, meeting agendas, and adapting messages for technical vs non-technical audiences. Use when drafting professional messages, preparing meeting communications, or improving written communication.

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。

Analyze recent code changes via git history and extract software engineering lessons. Use when the user asks 'what is the lesson here?', 'what can I learn from this?', 'engineering takeaway', 'what did I just learn?', 'reflect on this code', or wants to extract principles from recent 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。

Design and configure Microsoft Entra app registrations for secure authentication and authorization. Use this skill when users need OAuth, OpenID Connect, API permissions, app roles, or multi-tenant identity. Covers auth flows, credentials, API permissions, consent models, and zero-trust patterns.

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 the user asks to run Codex CLI (codex exec, codex resume) or references OpenAI Codex for code analysis, refactoring, or automated editing. Uses GPT-5.2 by default for state-of-the-art software engineering.

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。

Build persistent agents on Azure AI Foundry using the Microsoft Agent Framework Python SDK.

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。

Generate architecture documentation using C4 model Mermaid diagrams. Use when asked to create architecture diagrams, document system architecture, visualize software structure, create C4 diagrams, or generate context/container/component/deployment diagrams. Triggers include "architecture diagram", "C4 diagram", "system context", "container diagram", "component diagram", "deployment diagram", "document architecture", "visualize architecture".

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。

Bootstrap self-improving Symphony + Linear orchestration for a software repository. Use when Codex needs to inspect a repo, review or generate repo-local guidance, shape Linear issues, render a Symphony workflow, launch a high-leverage first execution wave, or run an orchestrator-led review, recovery, and learnings loop that makes future runs better.

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。

Researches and compares free and open source software projects in a supplied domain, using either user-named candidates or a discovered shortlist. Use when evaluating FOSS alternatives, comparing GitHub popularity, maintenance, licences, domain fit, trade-offs, or producing a linked HTML comparison report.

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。

将法律文本（合同、法规、规章或司法意见）的诠释性歧义审查转化为精美的交付物。可生成多页面网站（默认）、单页面交互式网站、Microsoft Word 文档、PowerPoint 演示文稿或 LaTeX/PDF。当用户持有压力测试结果、歧义审查或"这将在何处被诉"场景分析，并希望发布、演示或分享时使用。触发词包括"发布审查报告"、"据此制作报告"、"将其转化为网站/演示文稿/简报/Word 文档"、"制作 Netlify 网站"、"制作这些歧义点的幻灯片"、"渲染场景"、"打包这份压力测试"、"生成 LaTeX 版本"等。与歧义压力测试技能（底层分析）配套使用，但也适用于任何结构化或半结构化的歧义分析。视觉设计有明确风格倾向，但用户可覆盖颜色、字体和品牌标识。大多数格式需要 Python 和可写的文件系统；单文件 HTML 在主机两者皆无时仍可运行。

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。

Adversarial dialectic tool inspired by GAN. Alternates between Generator (creative advocate) and Discriminator (adversarial critic). Supports forced role selection (g/d), intensity modes (hard/soft), and multi-language output. Use for stress-testing ideas, business plans, code, and spec designs.

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。

Choose what kind of knowledge to transfer between teacher and student models: response, feature, or relational, and decide among offline, online, self, or cross-modal distillation schemes. Best for distillation strategy selection, capacity-gap diagnosis, and transfer planning. Activate on "knowledge distillation", "teacher-student", "soft labels", "feature distillation", "online distillation", or "cross-modal transfer". NOT for generic compression checklists or unrelated training 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。

Applies insights from MetaGPT paper on how structured communication, role specialization, and SOPs prevent coordination failures in multi-agent LLM systems

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。

Schema design, normalization decisions, indexing strategies, and migration safety for relational databases. Use when designing tables, choosing between normalize vs denormalize, selecting index types, planning zero-downtime migrations, or setting up connection pooling. Activate on "schema design", "normalization", "denormalization", "foreign key", "index strategy", "migration", "connection pooling", "composite key", "surrogate key", "soft delete", "polymorphic association". NOT for PostgreSQL-specific query tuning or EXPLAIN analysis (use postgresql-optimization), NoSQL or document database design, or cloud database provisioning.

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。

Domain-Driven Design methodology using bounded contexts, ubiquitous language, and strategic/tactical patterns

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。

Building resilient distributed systems with circuit breakers, retries with full-jitter exponential backoff, retry budgets (per-request 3-attempt + per-client 10% ratio per Google SRE), deadline propagation, and the cascading-failure math (4 layers × 3 retries = 64x amplification). Grounded in Resilience4j, Microsoft Cloud Patterns, AWS Architecture Blog (Marc Brooker), and Google SRE Book.

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。

Build weighted gene co-expression networks to identify modules of co-regulated genes, relate them to phenotypes, and find hub genes using WGCNA, hdWGCNA, MEGENA, CEMiTool, and Gaussian graphical models. Covers signed-network choice, soft-threshold selection, module preservation, and the marginal-vs-partial-correlation distinction. Use when finding co-expression modules, identifying hub genes, relating gene networks to clinical or experimental traits, or building single-cell co-expression networks. For directed TF-target inference see scenic-regulons and grn-inference; for condition rewiring see differential-networks.

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。

Performs alchemical free-energy calculations including relative binding free energy (RBFE / FEP+) and absolute binding free energy (ABFE) via OpenFE, FEP+, GROMACS, AMBER pmemd, and OpenMM with explicit lambda scheduling, soft-core potentials, MBAR/BAR analysis, cycle-closure validation, and protocol-appropriate enhanced sampling. Compares ML alternatives (Boltz-2 affinity, DeepDock). Use when ranking analogs by binding affinity beyond docking accuracy, performing prospective lead optimization, or validating SAR predictions.

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。

Derive an execution roadmap from a project's vision/spec docs by first principles, then diff that fresh derivation against the existing roadmap to prove the task ORDER is correct. Use this whenever the user wants to review, validate, re-derive, or sanity-check an execution roadmap or delivery plan; asks whether the tasks/epics are "in the right order"; suspects a task is scheduled before its prerequisite (a producer-after-consumer / dependency-inversion error like "OpenAPI after the web client that consumes it", OR a cross-cutting inherited default like observability/auth/tenancy scheduled after the feature work that inherits it — a seam-after-consumer error); wants to find missing tasks, circular dependencies, or over-fragmented epics; or wants to compact a plan to raise velocity. Also trigger on "review the roadmap", "re-derive the plan from the spec", "is this the right build order", "check the dependency graph of our epics", "which task should come first". It reads the vision + spec corpus, decomposes actors → use-cases → modules → epics → typed tasks, identifies cross-cutting inherited-default seams, builds a real dependency graph with hard + soft (seam) edges (deterministic helper), breaks cycles, topologically orders, compacts, and emits a derived roadmap PLUS a delta report against the current one — it never blindly overwrites a hand-crafted roadmap.

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。

Azure Identity SDK for .NET. Authentication library for Azure SDK clients using Microsoft Entra ID. Use for DefaultAzureCredential, managed identity, service principals, and developer credentials.

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。

Build AI applications on Microsoft Foundry using the azure-ai-projects SDK.

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。

Authenticate Java applications with Azure services using Microsoft Entra ID (Azure AD).

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。

Actorization converts existing software into reusable serverless applications compatible with the Apify platform. Actors are programs packaged as Docker images that accept well-defined JSON input, perform an action, and optionally produce structured JSON output.

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。