「performance」の検索結果

Curated few-shot examples for construction AI tasks: classification, extraction, analysis. Domain-specific examples for improved LLM 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。

Automated pipeline for retraining ML models with new construction data. Monitor model drift, trigger retraining, and validate model 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。

Prequalify subcontractors based on safety, financial, and performance 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。

Track actual vs planned productivity using CWICR norms. Calculate productivity rates, identify variances, and generate performance reports.

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。

Building energy simulation and analysis for construction. Calculate heating/cooling loads, evaluate envelope performance, optimize HVAC sizing, and ensure energy code compliance.

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 construction labor productivity using data analytics. Track worker performance, identify inefficiencies, predict resource needs, and optimize crew allocation for maximum 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。

AI-powered contractor matching and selection for construction projects. Analyze contractor capabilities, past performance, certifications, and project requirements to recommend optimal matches.

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。

Multi-project portfolio analytics dashboard. Aggregate KPIs across projects, track portfolio health, compare performance, and support executive decision-making.

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。

Serverless compute for Databricks jobs, Lakeflow pipelines and Declarative Automation Bundles (DABs), with STANDARD or PERFORMANCE_OPTIMIZED and classic only where serverless cannot run the workload. Use when creating, deploying, scheduling or editing a job or pipeline, or deciding its compute. Invoke BEFORE writing a job spec. For migrating existing classic workloads, use databricks-serverless-migration.

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 guide to Spark Structured Streaming for production workloads. Use when building streaming pipelines, working with Kafka ingestion, implementing Real-Time Mode (RTM), configuring triggers (processingTime, availableNow), handling stateful operations with watermarks, optimizing checkpoints, performing stream-stream or stream-static joins, writing to multiple sinks, or tuning streaming cost and 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。

Track workouts, stats, progress over time. Identify improvement areas, plateaus, rest/recovery needs, peak performance timing, injury risk.

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 content for search engines with keyword analysis, readability scoring, meta descriptions, and competitor comparison. Use this when users want to improve SEO, optimize blog posts, or analyze content for search 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。

Multi-dimensional rep evaluation: activity, conversion, velocity, deal size. Peer benchmarking and coaching priority identification.

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。

Writes conversion-focused landing page copy - headlines, subheads, value propositions, feature and benefit blocks, social proof, FAQs, and CTA button text - using PAS, AIDA, or StoryBrand. Use when the user wants words for a landing page, sales page, product or pricing page, waitlist page, homepage hero, or ad-to-page flow, including rewriting weak copy, generating headline or CTA variants for A/B tests, or turning a product description into page sections. Use landing-page-optimizer instead for auditing an existing page's performance, and a frontend skill for building the page itself.

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。

Audit a codebase for LLM calls that are really classifications in disguise, then produce a costed swap plan for a System One model. Use when asked to cut AI inference cost or latency, when scoping a performance engagement for a client, when reviewing an agent loop that feels slow, or when asked "where could we use Jev here". Produces a ranked table of candidates with measured latency and dollar deltas.

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。

Uses Managed Agents' 14.5-hour runtime to audit an entire codebase overnight. Security, performance, accessibility, dependency issues. You wake up to a full 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。

A/B test subject line variations using proven copywriting frameworks. Predict open rates based on historical 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。

Computes DORA delivery-performance metrics from git and GitHub API. Use when assessing deployment frequency, lead time, or change failure rate.

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。

Decides hand-vs-compiler for loop transforms (unrolling, SIMD, fusion, branchless). Use when reviewing/authoring a hot loop or tempted to hand-optimize one.

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。

Establishes CPU/GPU baselines before resource-intensive operations. Use before builds, training runs, or any task that pins cores or GPUs for over a minute.

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。

Evaluate Claude skill quality through auditing. Use when reviewing or auditing skills.

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。

Guide creating Claude Code hooks with security-first design. Use for validation and enforcement.

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。

Implement, review, or improve SwiftUI features using the iOS 26+ Liquid Glass API. Use when asked to adopt Liquid Glass in new SwiftUI UI, refactor an existing feature to Liquid Glass, or review Liquid Glass usage for correctness, performance, and design alignment.

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 face.evoLVe for high-performance face recognition workflows across alignment, data preparation, PyTorch training, feature verification, and Paddle deployment.

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。