「dom」の検索結果

Secrets management, XSS prevention, CSRF protection, dependency scanning, DOMPurify sanitization, CSP headers, CODEOWNERS, HttpOnly cookies

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。

Resend email setup, domain verification

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 algorithmic art using p5.js with seeded randomness and interactive parameter exploration. Use this when users request creating art using code, generative art, algorithmic art, flow fields, or particle systems. Create original algorithmic art rather than copying existing artists' work to avoid copyright violations.

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 for a whole-corpus, read-only audit of tracked stablecoin static metadata and domain sidecars, especially after catalog changes or when factual or internal-consistency drift needs review.

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 hosting, deploying, or operating an app on the Prisma Platform: projects, branches, preview environments, services and their versions, Prisma Postgres databases, object-store buckets, environment variables, custom domains, logs, or the GitHub integration. Triggers on "Prisma Platform", "Prisma Compute", "Prisma Postgres", "Prisma Storage", "preview environment", `prisma deploy`, `prisma dev`, `prisma auth`, `prisma project`, `prisma branch`, `prisma service`, `prisma postgres`, `prisma bucket`, `prisma git`, `prisma.config.ts`, promote, rollback.

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。

Read a Prisma Next structured error envelope and route to the right recovery — code, domain, severity, why, fix, meta. Use for error, exception, my emit failed, my query won't typecheck, my query crashed, my migration won't apply, MIGRATION.HASH_MISMATCH, BUDGET.ROWS_EXCEEDED, BUDGET.TIME_EXCEEDED, RUNTIME.ABORTED, PLAN.HASH_MISMATCH, CONTRACT.MARKER_MISSING, PN-RUN-3001, PN-RUN-3002, PN-RUN-3030, PN-MIG-2001, PN-CLI-4011, PN-SCHEMA-0001, drift, capability missing, planner conflict, prisma studio, EXPLAIN, query log, db.end, db.close, script won't exit, hangs, close connection, pool.end, client is closed.

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。

Extracts indicators of compromise from raw analysis artifacts: parsing strings dumps, sandbox reports, PCAP summaries, and logs for URLs, domains, IPs, hashes, mutexes, and file paths, then deduplicating and typing them. Activates for requests to extract IOCs from analysis output, pull indicators from a report, or harvest atomic indicators.

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。

Prepares indicators of compromise for safe sharing: defanging URLs, domains, IPs, and emails so they cannot be accidentally clicked or resolved, and formatting them into STIX/CSV/MISP-friendly output. Activates for requests to defang IOCs, sanitize indicators for a report, or export indicators for sharing.

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。

Pulp musical/media time primitives, exact beat divisions, tempo and meter maps, transport-range grid projection, inline and order-preserving groove projection, coordinate randomness, streaming cursors, and quantization arithmetic.

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。

Designing psychology experiments — variables, controls, randomization, counterbalancing, and preregistration.

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。

Fine-tune large language models — data curation, SFT, parameter-efficient methods (LoRA), evaluation, and avoiding common training failures. Use when a base model needs task-specific behavior or domain knowledge.

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 rigorous experiments — hypotheses, controls, randomization, power analysis, and preregistration. Use when planning any study where you need trustworthy causal conclusions.

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。

Designing clinical trials — phases, randomization, endpoints, sample size, and CONSORT-compliant reporting.

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。

Marketing analytics and data-driven optimization for B2B SaaS campaigns. Use this skill when analyzing marketing performance, optimizing conversion rates, running A/B tests, conducting customer research, creating marketing dashboards, building attribution models, analyzing funnel metrics, or storytelling with data. Also triggers on: marketing ops, performance analytics, CRO, A/B testing, data storytelling, customer insights, tracking plan, event taxonomy, UTM taxonomy, GA4 audit, consent mode, instrumentation contract, sample size calculation, minimum detectable effect, test duration, not enough traffic to A/B test, statistical significance, Bayesian vs frequentist, sample ratio mismatch, experiment feasibility, martech stack, marketing tech stack audit, tool consolidation, build vs buy, vendor evaluation, shadow IT tools, ESP/CRM/platform migration, switching cost, cutover plan, suppression list migration, data portability, customer interviews, survey design, sampling frame, non-response bias, how many people should I interview, thematic saturation, voice of customer, jobs-to-be-done, buyer persona research, is this finding real, data enrichment, which enrichment provider should we buy, waterfall enrichment, contact data quality, match rate, our list data is bad, email verification, catch-all domain, accept-all, bounce rate from bad data, B2B data decay, how often should we re-verify contacts, ZoomInfo vs Apollo vs Clearbit evaluation, cost per usable record, where did this list come from, GDPR Article 14 notice, purchased list compliance, suppression screening, TAM list building, demand planning, marketing plan, how many leads do we need, what pipeline target should marketing carry, funnel model, top-down vs bottom-up plan, plan of record, annual marketing planning, quarterly planning, pipeline coverage we have to create, sales cycle lag, our leads cannot close this quarter, carry-in pipeline, scenario planning, conservative moderate aggressive forecast, stretch target, reforecast, re-forecast triggers, assumption register, capacity ceiling, addressable audience ceiling, marginal cost per lead, blended CPL is wrong, we missed plan and nobody knows why, AI policy for the marketing team, AI governance, AI acceptable use policy, which AI tools are we allowed to use, can I paste customer data into ChatGPT, shadow AI, AI use register, human review of AI output, when can we stop reviewing every AI draft, can the agent send on its own, the AI made a mistake now what, pull back AI autonomy, AI evals, our AI vendor changed the model, AI output went out wrong, prompt injection, can our AI read prospect replies safely, lethal trifecta, the AI leaked internal notes, turn off auto-generated ad assets, auto-applied recommendations, AI literacy training, EU AI Act Article 4, duplicate records in the CRM, dedupe before import, which value wins in a merge, CRM cleanup.

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。

Before designing, training, or auditing ANY model that replicates human-annotated labels, audit the annotation protocol's INPUT — the exact document/evidence the human labelers consulted — and give the model that same input. Use when: (1) designing a classifier/LLM extractor whose target is a hand-coded label set, (2) a label-replication model shows low recall concentrated in a label subset and the diagnosis on offer is "the label's information is not in the features", (3) reviewers propose construct splits (e.g. "designation vs record-evident"), adjudication sittings, or per-domain stop rules to explain residual disagreement with gold, (4) validating an extraction pipeline against labels transcribed from a source document. Symptom of the underlying failure: elaborate theory accumulates to explain why gold is "partially unpredictable" when the model was simply never shown the document the annotators read.

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。

One-shot module map — go up a layer of abstraction and get a domain-vocabulary module map of the codebase. Use when lost in unfamiliar code, after a long deep-dive session, or when you need to re-orient before planning.

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。

Elite UX/UI & Advanced GSAP Motion Engineer. Enforces Python-driven true randomization for layout variance, strict AIDA page structure, wide editorial typography (bans 6-line wraps), gapless bento grids, strict GSAP ScrollTriggers (pinning, stacking, scrubbing), inline micro-images, and massive section spacing.

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。

Holistic cross-GDD consistency and game design review. Reads all system GDDs simultaneously and checks for contradictions between them, stale references, ownership conflicts, formula incompatibilities, and game design theory violations (dominant strategies, economic imbalance, cognitive overload, pillar drift). Run after all MVP GDDs are written, before architecture begins.

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。

用于将昂贵的浏览器工作移出主线程，涉及 Web Workers、SharedWorker、worker 池、Comlink、transferable objects、Vite/Webpack worker 集成或 UI 响应性修复。不要用于轻量同步工作或 DOM 操作；中文触发词包括 Web Worker、后台线程、主线程阻塞。

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。

用于构建或审查 Canvas 2D、Three.js/WebGL、React Three Fiber、GLSL shader、ShaderToy 到 WebGL 适配、2D/3D 可视化、游戏渲染、动画循环、GPU 资源清理或渲染性能。不要用于标准 DOM UI、已有图表库覆盖的图表或非图形类性能工作；中文触发词包括 Canvas、Three.js、WebGL、GLSL、Shader、3D、数据可视化、游戏。

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。

用于审查前端安全风险，如 XSS、CSRF、敏感数据暴露、不安全 DOM API、不可信用户输入、认证/token 处理、支付流程、文件上传、CSP、依赖风险或第三方脚本；中文触发词包括 安全审查、安全检查。

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 moving expensive browser work off the main thread with Web Workers, SharedWorker, worker pools, Comlink, transferable objects, Vite/Webpack worker integration, or UI responsiveness fixes. Do not use for lightweight synchronous work or DOM manipulation; Chinese triggers include Web Worker, background thread, main thread blocking.

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 building or reviewing Canvas 2D, Three.js/WebGL, React Three Fiber, GLSL shaders, ShaderToy-to-WebGL adaptation, 2D/3D visualization, game rendering, animation loops, GPU resource cleanup, or rendering performance. Do not use for standard DOM UI, charts already covered by a chart library, or non-graphical performance work; Chinese triggers include Canvas, Three.js, WebGL, GLSL, Shader, 3D, data visualization, games.

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 reviewing frontend security risks such as XSS, CSRF, sensitive data exposure, unsafe DOM APIs, untrusted user input, authentication/token handling, payment flows, file upload, CSP, dependency risk, or third-party scripts; Chinese triggers include security review, security check.

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。