「performance」の検索結果

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。

Disciplined diagnosis loop for hard bugs and performance regressions. Reproduce → minimise → hypothesise → instrument → fix → regression-test. Use when user says "diagnose this" / "debug this", reports a bug, says something is broken/throwing/failing, or describes a performance regression.

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。

Optimize web images for performance with Next.js Image, responsive srcset, AVIF/WebP, lazy loading, and blur placeholders. Activate on: image optimization, LCP improvement, responsive images, AVIF, blur placeholder, next/image. NOT for: image generation/editing (use image-gen skills), SVG icon systems (use design-system-creator).

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。

PgBouncer, connection optimization, and pooling strategies for database performance. Activate on: connection pool, PgBouncer, database connections, pool size, connection limit, Prisma pool, Drizzle pool. NOT for: query optimization (use data-warehouse-optimizer), database schema design (use dimensional-modeler).

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。

Operations, debugging, and optimization for DAG workflows. Performs root cause analysis on failures, profiles execution performance, aggregates results from parallel branches, bridges context between nodes, and learns patterns from execution history. Activate on "DAG failed", "why did it fail", "root cause", "performance profile", "aggregate results", "merge branches", "execution patterns", "optimize DAG". NOT for planning DAGs (use dag-planner), executing DAGs (use dag-runtime), or validating outputs (use dag-quality).

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 root cause analysis on DAG execution failures. Traces failure propagation, identifies systemic issues, and generates actionable remediation guidance. Activate on 'failure analysis', 'root cause', 'why did it fail', 'debug failure', 'error investigation'. NOT for execution tracing (use dag-execution-tracer) or performance issues (use dag-performance-profiler).

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。

Traces complete execution paths through DAG workflows. Records timing, inputs, outputs, and state transitions for all nodes. Activate on 'execution trace', 'trace execution', 'execution path', 'debug execution', 'execution log'. NOT for performance analysis (use dag-performance-profiler) or failure investigation (use dag-failure-analyzer).

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。

Spark, pandas, polars, DuckDB optimization for batch data processing. Activate on: batch processing, Spark optimization, polars, DuckDB, pandas performance, data frame, shuffle, partition, memory optimization. NOT for: streaming pipelines (use streaming-pipeline-architect), warehouse queries (use data-warehouse-optimizer).

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。

Optimize AgentDB performance with quantization (4-32x memory reduction), HNSW indexing (150x faster search), caching, and batch operations. Use when optimizing memory usage, improving search speed, or scaling to millions of vectors.

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。

Disciplined diagnosis loop for hard bugs and performance regressions. Reproduce → minimise → hypothesise → instrument → fix → regression-test. Use when user says "diagnose this" / "debug this", reports a bug, says something is broken/throwing/failing, or describes a performance regression.

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。

Angular performance optimization and best practices guide. Use when writing, reviewing, or refactoring Angular code for optimal performance, bundle size, and rendering efficiency.

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。

Act as a senior Amazon account growth operator that combines Amazon performance-drop analytics, Amazon growth-opportunity analytics, Amazon Ads data, retail readiness, BSR/rank signals, margin, inventory, and campaign/search-term evidence into a prioritized profitable growth action plan. Use when Codex needs to decide what to protect, fix, scale, reduce, pause, harvest, launch, or monitor for an Amazon brand, agency, marketplace, ASIN set, or account; when building daily, weekly, or monthly Amazon account operating plans; when reallocating ad budget; when deciding which ASINs are safe or risky to push; or when turning performance-drop and growth-opportunity findings into approval-gated actions with success/failure criteria.

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。

Isolate the root cause of ML performance drops, inconsistent evaluations, prediction errors, and training-serving mismatches across data, labels, splits, pipelines, models, metrics, and runtime behavior. Use when investigating a reproducible failure or regression, not routine model selection or general performance validation.

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。

Expert-level flight mechanics covering aircraft equations of motion, stability and control, performance, handling qualities, and flight simulation.

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。

Diagnoses and fixes UI performance across loading speed, rendering, animations, images, and bundle size. Use when the user mentions slow, laggy, janky, performance, bundle size, load time, or wants a faster, smoother experience.

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 technical quality checks across accessibility, performance, theming, responsive design, and anti-patterns. Generates a scored report with P0-P3 severity ratings and actionable plan. Use when the user wants an accessibility check, performance audit, or technical quality 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。

Expert SwiftUI guidance for macOS apps — covers writing and reviewing code with runtime-level understanding of observation, concurrency, performance, and platform integration. Use when user asks to "build a SwiftUI view", "review my SwiftUI code", "fix observation issues", "bridge AppKit", "architect state management", "debug view updates", or works on any macOS SwiftUI project — building features, reviewing existing code, debugging view updates or performance, fixing concurrency issues, architecting state management, bridging AppKit, or making design decisions. Do NOT use for iOS/iPadOS-only SwiftUI, UIKit bridging, or basic Swift language questions. Focuses on non-obvious traps and deep patterns, not basics.

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 optimizing test suite performance — database setup, seeder optimization, parallel testing, CI pipeline efficiency, or RefreshDatabase alternatives.

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 optimizing application performance — caching strategies, eager loading, query optimization, Redis patterns, or background job 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。

Performance audit — bottleneck profiling, N+1 query detection, hot-path analysis; explicit request only, not part of regular feature 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。

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. Triggers: create a skill, new skill, build a skill, skill creation, write a SKILL.md, evaluate skill, test skill, skill eval, benchmark skill, optimize skill description.

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。

[Git] Use when generating developer KPI, performance, contribution value, story point, man-day, or code-quality reports from local git commit history.

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。

Judge performance or resource claims on real hardware: benchmark design or review, latency, throughput, memory or headroom numbers, and accept-or-reject decisions on optimizations. Not for refactors with no resource claim.

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。