4,668 件 ・ 関連度順
概要と使いどころ
Refine an already working product frontend in a dedicated finishing pass, preserving its identity while improving hierarchy, components, copy and responsive behavior. Use after the core flow works or when the user asks for frontend polish; not for product ideation or a new visual direction.
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。
Security-focused code review checklist and automated scanning patterns. Use when reviewing pull requests for security issues, auditing authentication/authorization code, checking for OWASP Top 10 vulnerabilities, or validating input sanitization. Covers SQL injection prevention, XSS protection, CSRF tokens, authentication flow review, secrets detection, dependency vulnerability scanning, and secure coding patterns for Python (FastAPI) and React. Does NOT cover deployment security (use docker-best-practices) or incident handling (use incident-response).
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。
Microsoft 365 Agents SDK for Python. Build multichannel agents for Teams/M365/Copilot Studio with aiohttp hosting, AgentApplication routing, streaming responses, and MSAL-based auth. Triggers: "Microsoft 365 Agents SDK", "microsoft_agents", "AgentApplication", "start_agent_process", "TurnContext", "Copilot Studio client", "CloudAdapter".
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。
Create FastAPI routers with CRUD operations, authentication dependencies, and proper response models. Use when building REST API endpoints, creating new routes, implementing CRUD operations, or adding authenticated endpoints in FastAPI applications.
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 Data Lake Storage Gen2 SDK for Python. Use for hierarchical file systems, big data analytics, and file/directory operations. Triggers: "data lake", "DataLakeServiceClient", "FileSystemClient", "ADLS Gen2", "hierarchical namespace".
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 Azure AI Foundry agents using the Microsoft Agent Framework Python SDK (agent-framework-azure-ai). Use when creating persistent agents with AzureAIAgentsProvider, using hosted tools (code interpreter, file search, web search), integrating MCP servers, managing conversation threads, or implementing streaming responses. Covers function tools, structured outputs, and multi-tool agents.
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 actual Azure spend, bill changes, and AKS or AI costs. WHEN: "Azure cost breakdown", "most expensive resources", "why did my bill increase", "cost spike", "unexpected charge", "Cosmos DB charges", "Foundry model costs", "AKS cluster cost", "namespace cost", "enable AKS cost analysis", "idle AKS capacity". DO NOT USE FOR: forecasts, pricing estimates, rightsizing, commitments, or budgets.
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 AI Agents Persistent SDK for .NET. Low-level SDK for creating and managing AI agents with threads, messages, runs, and tools. Use for agent CRUD, conversation threads, streaming responses, function calling, file search, and code interpreter. Triggers: "PersistentAgentsClient", "persistent agents", "agent threads", "agent runs", "streaming agents", "function calling agents .NET".
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 and create distinctive, production-grade frontend interfaces with high design quality and design system compliance. Evaluates using three pillars: frictionless insight-to-action, quality craft, and trustworthy building. USE FOR: PR reviews, design reviews, accessibility audits, design system compliance checks, creative frontend design, UI code review, component reviews, responsive design checks, theme testing, and creating memorable UI. DO NOT USE FOR: Backend API reviews, database schema reviews, infrastructure or DevOps work, pure business logic without UI, or non-frontend 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。
XCMS3 workflow for LC-MS/MS metabolomics preprocessing. Covers peak detection, retention time alignment, correspondence (grouping), and gap filling. Use when processing raw LC-MS data into a feature table for untargeted metabolomics.
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。
Detect structural variants from long-read alignments using Sniffles, cuteSV, and SVIM. Use when detecting deletions, insertions, inversions, translocations, or complex rearrangements from ONT or PacBio data, especially those missed by short-read methods.
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。
Query PharmGKB and CPIC for drug-gene interactions, pharmacogenomic annotations, and dosing guidelines. Use when predicting drug response from genetic variants or implementing clinical pharmacogenomics.
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。
Apply Robert C. Martin-inspired clean code rules when improving readability, naming, function design, responsibilities, or testable everyday 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。
Use when the user asks to "triage our comments, DMs, and mentions", "draft replies to this thread", "can we repost this fan post", or "set up inbox SLAs and an escalation path"; produces a ranked triage queue with register detection (sincere / ironic / performative / parasocial, sentiment-inversion table included — "this is so bad" under a comedy register is praise), a commenter taxonomy (troll monitor-only / rager / misguided / unhappy-customer / advocate) with a response-tier ladder and per-channel SLAs, an escalation matrix ending at the crisis path, a moderation ladder plus house rules for owned spaces, and a UGC curation-and-rights mode whose dated permission entries route to the channel registry — every reply is a ranked DRAFT a human posts; nothing is ever auto-sent. Not for launch-window feedback triage — use launch-feedback-synthesizer. 评论私信提及分诊/语域识别/回复草稿/UGC授权
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 "set up my founder social-selling routine", "build a daily engagement block for target accounts", or "turn funding / hiring signals into selling plays"; produces the founder/seller daily operating block — a time-boxed engagement-block spec (substantive value-add comments on target-account posts, never a pitch), warm-touch-before-ask cadence rules, trigger-response plays consuming the social-pulse-monitor B2B trigger watchlist (funding / hiring / launch signals), and a quarterly diagnostic that reads LinkedIn SSI as an input diagnostic only (Estimated, vendor-defined — never a target metric). All volume norms are Estimated/community-derived; all execution is human — zero mass-DM, zero connection-request automation, zero engagement automation (LinkedIn UA §8.2 red line); 1:1 pitch and DM mechanics route to outreach-manager. Not for cold email sequences — use cold-outbound-sequencer. 社交销售/创始人日常互动块/热身触达节奏/触发信号剧本
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 "track rankings" or "查排名"; measures keyword and SERP-position deltas over time from provided exports or connected tools, including AI-response checks. Not for multi-metric stakeholder reports or setting alerts — use performance-monitor (report or alert mode). 排名追踪/SERP监控
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 "build battle cards", "prep the sales team for launch", or "write the internal launch FAQ"; produces the internal enablement kit for a sales-led launch — battle cards vs each named alternative (where we win / where they win / trap questions, every fact traceable), a sales talk track derived from the PR-FAQ spine, an objection-handling table (objection → response → evidence), internal FAQ + CS macros, and an internal launch announcement with embargo discipline. Not for the external message house — use message-house-builder; not for competitor tracking itself — use competitor-tracker or competitor-analysis; not for outbound sequences — use cold-outbound-sequencer. 销售赋能/battle card/销售叙事/异议处理/内部FAQ
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 "design an email A/B test", "set up a multivariate subject/CTA test", "run a send-time test", "build a hold-out group", or "is this email result statistically and practically material?"; produces a falsifiable hypothesis, one-variable-per-cell matrix, sample-size/MDE/duration/power plan, and an effect/uncertainty read from own ESP data. Applies only a precommitted owner-approved action rule; the helper never chooses a business action. Not for EQS/vetoes or writing the email. 邮件AB测试设计/多变量测试/发送时间测试/留出组/显著性判定
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 alignment artifacts — responsibility matrices, decision rights, and communication plans. Use when unclear ownership stalls decisions. For persuading in the moment, use `design-negotiation` (designer-toolkit).
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。
Critique a rendered screen's composition — balance, whitespace, rhythm, and gestalt grouping. Use when a layout feels off but hierarchy is fine. For emphasis and eye flow specifically, use `critique-visual-hierarchy`.
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 layouts and interactions that adapt across screen sizes and input methods. Use when one design must serve many viewports. For the underlying column grid use `layout-grid`; for OS-specific patterns use `platform-conventions`.
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 to iOS and Android conventions — what each OS mandates, where they diverge, and when to unify. Use when shipping native apps. For breakpoint adaptation use `responsive-design`; for matching competitor patterns use `jakobs-law` (interaction-design).
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。
Apply Fitts's Law — target acquisition time depends on size and distance. Use when sizing and positioning controls, especially for touch. For how many controls to show at once, use `hicks-law`.
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。
Apply the Doherty Threshold — keep system response under 400ms to preserve user flow. Use when diagnosing perceived slowness or setting a performance budget. For what to show during unavoidable waits, use `loading-states`.
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。