「soft」の検索結果

Senior software engineer for story execution and code implementation. Use when the user asks to talk to Amelia or requests the developer agent.

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 minimal, abstract, UI-inspired illustrations for software product features — as deterministic SVG. Use when asked for a feature illustration, product illustration, spot illustration, empty-state graphic, feature-card artwork, docs or developer-portal illustration, or a consistent illustration set/family for a product. Also use to turn a feature description or a product screenshot into an abstract UI illustration, to add a new illustration to an existing family, or to retheme an existing set.

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 mode operational spec for the team lead. Returns lead identity, facilitator identity, mode-specific rules, suggest-members guidance, and phase arc for code-mode teams.

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 whenever the user mentions Zenodo, depositing or publishing research artifacts (datasets, software, papers, posters) to Zenodo, minting a DOI for a dataset/code release, uploading files to a Zenodo record, creating a new version of a Zenodo deposit, or searching Zenodo records. Covers the full Zenodo REST API workflow — create deposition, upload files via the bucket API, set metadata, publish, version, and search — for both production (zenodo.org) and sandbox (sandbox.zenodo.org).

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。

Software architecture guidelines to maintain code readability (Clean Code, SOLID, DRY) and application scalability / Panduan arsitektur perangkat lunak untuk menjaga keterbacaan kode (Clean Code, SOLID, DRY) dan kemampuan skalabilitas aplikasi.

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Expert guide for Knowledge Graphs, GraphRAG, Microsoft GraphRAG, Neo4j Text2Cypher, multi-hop relational retrieval, and hybrid vector-graph search / Panduan ahli Knowledge Graph, GraphRAG, dan pencarian relasional multi-hop.

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Configures structured logging with ILogger<T> and ILoggerFactory following Microsoft best practices. Includes Serilog setup, log enrichment, and logging source generators for high-performance logging.

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。

SQL Server database design best practices, naming conventions, indexing strategies, and performance optimization for .NET applications using Microsoft.Data.SqlClient and EF Core.

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。

Generates Minimal API endpoints following Microsoft's recommended approach. Creates fast, testable HTTP APIs with minimal code using MapGet/MapPost/MapPut/MapDelete. Preferred over controller-based APIs for new projects.

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。

Comprehensive markdown planning methodology for software projects. Use when starting a new project, creating implementation plans, or refining architecture before coding.

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。

The philosophy and practical benefits of agent fungibility in multi-agent software development. Why homogeneous, interchangeable agents outperform specialized role-based systems at scale.

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。

Systems programming from memory management through networking. Covers memory models (stack vs heap, manual allocation, garbage collection, ownership/borrowing), concurrency (threads, mutexes, channels, async/await, actor model, data races vs race conditions), operating system concepts (processes, virtual memory, page tables, system calls, file descriptors, signals), compilation (lexing, parsing, code generation, linking, static vs dynamic libraries), networking fundamentals (TCP/IP, sockets, HTTP, DNS, TLS), and the hardware-software boundary (caches, cache lines, false sharing, memory-mapped I/O). Use when working with low-level code, diagnosing system-level bugs, understanding performance characteristics, or bridging between high-level languages and machine behavior.

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。

Understanding how algorithmic systems shape what users see, know, and do -- from recommendation feeds to search ranking to credit scoring to hiring software. Covers the mechanics of recommendation systems, algorithmic bias and its sources, personalization's effects on information diets, opacity and accountability, AI limitations (hallucination, confident wrongness), and the human-in-the-loop question. Use when a learner needs to think critically about why particular content reached them.

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。

OpenStack Neutron software-defined networking service. Provides network abstraction for cloud instances including security groups, floating IPs, DHCP, L3 routing, ML2 plugin architecture with OVN/OVS backends, network namespaces, provider and tenant networks, VXLAN/VLAN/flat network types, and port management. Use for deploying, configuring, operating, and troubleshooting OpenStack networking.

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。

Creating and integrating out-of-tree Zephyr modules. Covers module.yml configuration, Kconfig and CMake integration within a module, and West manifest inclusion. Trigger when developing reusable libraries, driver packages, or external software components for Zephyr.

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。

Guided workflow for building custom AI agents using the OpenHands Software Agent SDK. Use when you want to create a new agent through an interactive interview process that gathers requirements and generates implementation plans.

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。

Bridge layer connecting the Agentic AI Foundation (AAIF) governance structure — Linux Foundation stewardship of MCP, goose, AGENTS.md — to the plurigrid/asi skill graph. Formalizes AAIF-compatible skill registration, IPSIE identity profiles, and the cross-protocol interoperability surface. Maps the AAIF platinum member ecosystem (AWS, Anthropic, Block, Bloomberg, Cloudflare, Google, Microsoft, OpenAI) to specific ASI skill integration points.

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 debugging software, investigating incidents, diagnosing flaky tests, or analyzing performance regressions — enforces structured observation recording with evidence IDs, causality validation, and verification gates to prevent correlation-causation pollution. Use when an agent might otherwise summarize or speculate instead of reporting observed evidence.

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。

Verify one falsifiable factual claim against evidence appropriate to the claim. Use when a concrete assertion about software, documentation, configuration, releases, or repository behavior must be classified as VERIFIED, REFUTED, or INCONCLUSIVE.

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。

Review existing tests against the software and contracts they actually exercise. Use to assess whether a test has justified unique protection, detects consequential faults, tolerates valid refactors, earns its lifecycle cost, or deserves strengthening, replacement, consolidation, or removal consideration. Reviews are read-only and evidence-backed, including TDD and regression tests.

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。

Enforce anti-AI UI design rules based on the Uncodixfy methodology. Use when generating HTML, CSS, React, Vue, Svelte, or any frontend UI code. Prevents generic AI aesthetic — soft gradients, glassmorphism, hero sections in dashboards, oversized rounded corners, and decorative copy. Applies constraints from Linear/Raycast/Stripe/GitHub design philosophy for functional, honest, human-designed interfaces. Triggers on UI generation, dashboard building, component creation, CSS styling, landing page design, or any task producing visual interface code.

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。

Verifies claims in backlog items, skill documentation, or plugin content against primary sources using web lookups. Spawns parallel verification agents that must use WebFetch/WebSearch/gh — training data recall is explicitly rejected as evidence. Produces VERIFIED/REFUTED/INCONCLUSIVE verdicts with citations. Use when items are marked UNVERIFIED or when verifying tool API claims, CLI flags, or documented software behavior.

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。

Interviews a researcher in rounds until an idea, design, or draft has no unexamined decision — numbered questions, each with why it matters and a recommended answer, facts looked up rather than asked, and settled decisions written to a file. Stages are idea (topic or hunch to a falsifiable question and contribution claim), design (estimand, identification, sample and power, measurement, analysis plan, pre-registration, venue), and defend (reviewer objections to a finished design or draft). Use when the user says "grill me", asks to stress-test or poke holes in an idea, design, or plan, asks what they are assuming, or brings an idea that is not yet a design. --plan grills software or process plans.

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。

Audits fielded survey-response data for registered elements, data quality, bot and AI-automation screening, and sample integrity, and produces an appendix-ready quality report. Use at soft launch before full field, after fielding before outcomes are analyzed, or when asked what a study did about bots and AI respondents.

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。