5,237 件 ・ 関連度順
概要と使いどころ
Perform code reviews following Sentry engineering practices. Use when reviewing pull requests, examining code changes, or providing feedback on code quality. Covers security, performance, testing, and design 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。
DAX performance optimization for semantic models. Automatically invoke when the user asks to "optimize DAX", "fix slow DAX", "DAX performance", "tune a measure", "debug a measure", "DAX anti-patterns", or mentions slow queries, server timings, or DAX authoring.
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 new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.
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 a Godot game stutters, drops frames, hitches when entering new areas, overheats a phone, or must ship: profiling and Performance monitors, CPU or GPU bound, draw calls, MultiMesh, occlusion culling, LOD, shader compilation hitches, export to macOS, Windows, Linux, Steam, Android (APK, AAB, keystore), iOS (Xcode), web (itch.io, COOP/COEP headers, SharedArrayBuffer, JavaScript interop), build size budgets, demo and DLC builds, signing and notarization.
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 music video and beat-synced visual content prompts for Seedance 2.0 on Higgsfield. Use whenever the user wants to create a music video, lyric video, beat-synced visuals, performance video, concert visual, album art animation, or music-driven visual content. Triggers on: music video, lyric video, beat sync, music visualization, performance video, concert visual, album visual, song video, music clip, beat drop visual, rhythm sync, or any music-driven video request. Use even for "make visuals for my song" or "video for this track.
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。
Run QA testing on a page, feature, or full site at one of three depth tiers (smoke, standard, full). Use this skill whenever the user asks to QA a page or site, run a smoke test after a deploy, verify a page before launch, or run a regression sweep. Triggers on QA, QA sweep, smoke test, regression test, post-deploy check, pre-launch check, verify the deploy, test this page, does the page render, broken link, 404, image not loading. Also triggers proactively after a deploy or a new page launch where verification matters. Covers accessibility, performance, and SEO at the surface-signal level only: deep audits belong to `accessibility-audit`, `performance-optimization`, and `seo-technical`, and code-level debugging to `code-review-web`.
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。
A discipline for running paid media that does not light money on fire. Hypothesis writing for paid spend, channel selection, budget allocation, audience targeting, bid strategy, campaign types, what NOT to spend on, attribution reality, and the failure modes that produce expensive lessons. Triggers on paid media strategy, ad budget allocation, channel selection, paid media plan, audit my Google Ads, audit my Meta Ads, scale paid media, kill underperforming campaign, paid media hypothesis, ad spend strategy, attribution reality, performance marketing strategy. Also triggers when a team is asking how to scale paid media, or whether to add a new channel, or how to reallocate spend across channels.
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。
How to read paid media dashboards without fooling yourself. Attribution models, platform reporting quirks, multi-platform reconciliation, ROAS vs LTV horizon traps, statistical noise in performance metrics, incrementality testing, and the failure modes that produce expensive lessons. Triggers on read paid media dashboard, attribution analysis, ROAS vs LTV, multi-platform reconciliation, ad incrementality, geo holdout, conversion lift study, ghost bidding, paid media reporting, board-deck paid media metrics, blended CAC, MMM, MTA, last-click attribution. Also triggers when a marketer is about to scale, kill, or rebudget a campaign based on platform metrics, or when reconciling platform reports against warehouse revenue.
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 new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, update or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.
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 pandas DataFrame operations for data analysis, manipulation, transformation, time series analysis, merging, aggregation, and performance optimization.
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。
Performance profiling and analysis
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。
Run sequential performance benchmarks with strict duration rules and baseline management
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。
Synthesize performance findings into evidence-backed recommendations and decisions
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。
Diagnose performance issues on Amazon SageMaker HyperPod clusters — uneven NCCL bandwidth across nodes and poor filesystem throughput. Read-only. Surfaces host-side signals (Xid, ECC, NVLink, EFA reachability, FSx saturation) and routes to the appropriate sibling skill (hyperpod-node-debugger, hyperpod-nccl, hyperpod-version-checker, hyperpod-issue-report) for any remediation. Triggers on uneven NCCL across nodes, straggler node, FSx slow, checkpoint slow, dataloader slow, filesystem bottleneck, FSx throughput, cross-AZ latency, topology mismatch.
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 with Aurora DSQL — manage schemas, execute queries, handle migrations, diagnose query plans, diagnose cluster performance, load data, and develop applications with a serverless, distributed SQL database. Covers IAM auth, multi-tenant patterns, MySQL-to-DSQL and PostgreSQL-to-DSQL schema conversion, foreign key constraints, OCC retry patterns, ORM migration (Django/EF Core/Hibernate/Rails/SQLAlchemy), DDL operations, query plan explainability, system diagnostics via CloudWatch AAS, SQL compatibility validation, and bulk data loading. Load before answering DSQL limit or error-code questions; it has limits docs omit. Triggers on phrases like: DSQL, Aurora DSQL, distributed SQL database, serverless PostgreSQL-compatible database, migrate to DSQL, DSQL query plan, DSQL EXPLAIN ANALYZE, DSQL ENUM, DSQL foreign key, DSQL OCC retry, DSQL multi-region, DSQL JSONB, DSQL GIN index, load into DSQL, load CSV into DSQL, bulk load DSQL, aurora-dsql-loader, DSQL slow, DSQL performance, DSQL wait events, DSQL AAS.
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 python code that evaluates SageMaker models. Supports two evaluation types: LLM-as-Judge and Custom Scorer. Use when the user says "evaluate my model", "run a benchmark", "test model performance", "how did my model perform", "compare models", or other similar requests.
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。
Social media campaign analysis and performance tracking that calculates engagement rates, ROI, and cross-platform benchmarks. Use for analyzing social performance, calculating engagement rate, or measuring campaign ROI.
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。
Frontend development for React, Next.js, TypeScript, and Tailwind CSS. Use when building React components, optimizing Next.js performance, analyzing bundle sizes, scaffolding projects, implementing accessibility, or reviewing 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。
Browser-based QA combining Chrome MCP control with Python analysis tools. Use when performing browser QA testing, visual regression tracking, WCAG accessibility auditing, performance profiling, or health-scoring web 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。
Use when the user asks to "design an experiment", "build a predictive model", "run A/B test analysis", "perform causal inference", "engineer features", "evaluate model performance", "set up MLOps pipeline", "analyze time series", "calculate sample size", or "deploy a model to production". Expert data science covering statistical modeling, experimentation, causal inference, feature engineering, ML deployment, and advanced analytics with Python, R, and SQL.
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。
Performance profiling for Node.js, Python, and Go: CPU flamegraphs, memory leak detection, bundle analysis, query optimization, and k6 load testing. Use when diagnosing slow endpoints, memory growth, large bundles, or traffic spikes.
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。
Designs multi-agent system architectures with orchestration patterns, tool schemas, and performance evaluation. Use when building AI agent systems, designing agent workflows, creating tool schemas, or evaluating agent performance.
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。
People leadership for scaling companies: hiring, compensation, org structure, performance, and retention. Use when building hiring plans, designing comp frameworks, restructuring teams, or managing performance.
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 the GSC AI Performance report by script: AI Overviews and AI Mode impressions. "read the new GSC AI 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。