“efficiency”的搜索结果

Calculate financial ratios from company statements to assess profitability, ability to cover obligations, efficiency, and valuation. Results include explanations, comparisons across periods, available industry benchmarks, and a formatted Excel report.

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• Comparing profitability across reporting periods
• Assessing ability to cover short-term obligations
• Calculating valuation ratios for investment analysis

Review an AI workflow across five areas: prompts, use of context, tools, system structure, and safety. Receive scores, specific findings, and prioritized actions to address quality and reliability problems.

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• Check an AI workflow before production use
• Audit an existing agent for quality and reliability
• Prioritize fixes for workflow problems

Compress LLM weights using HQQ quantization, which stores weights with fewer bits without calibration datasets. Follow workflows for precision settings, model saving, inference integration, and LoRA fine-tuning of quantized models.

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• Compressing LLMs without calibration datasets
• Serving quantized models with vLLM or HuggingFace Transformers
• Comparing bit widths and inference backends

Integrate Flash Attention into transformer models to reduce processing time and GPU memory use for long sequences. Covers PyTorch integration, advanced attention options, and benchmarks that compare speed, memory use, and output accuracy.

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• Speeding up transformer training on long sequences
• Reducing GPU memory use during attention
• Adding Flash Attention to a PyTorch model

Review recent code changes for duplication, maintainability, and wasted work, then apply straightforward improvements. The workflow combines three parallel reviews, prioritizes small, low-risk fixes, and checks the edited code with relevant tests or project checks.

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• Cleaning up code after implementation
• Polishing changes before opening a pull request
• Reducing duplication in recent changes

Route customer success, sales, revenue operations, and contract requests to one of four specialist skills. Use it when a business request is unclear, so the agent selects the relevant workflow before starting the work.

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• Choose a skill for identifying accounts at risk of churn
• Route RFP participation and proposal requests
• Find the workflow for pipeline and revenue forecasts

Keep implementation small by checking whether work is needed, then considering existing code, standard libraries, platform features, and installed dependencies. Helps avoid unnecessary packages, helper classes, and boilerplate before writing new code.

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• Implementing a feature with minimal code
• Avoiding unnecessary package installations
• Reusing existing helpers and platform features

Assess Google Cloud workloads for environmental sustainability using the Well-Architected Framework. Get recommendations on lower-carbon regions, efficient computing and storage, AI energy use, and ongoing emissions measurement with Carbon Footprint.

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• Reviewing cloud architecture for environmental impact
• Comparing deployment regions by carbon-free energy use
• Reducing energy waste in AI and computing workloads

Reviews Google Cloud spending and resource usage to identify waste, recommend suitable resource sizes, compare discount options, and establish ongoing cost management using the cost optimization guidance in the Google Cloud Well-Architected Framework.

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• Reviewing Google Cloud spending by team or project
• Finding idle resources and oversized instances
• Comparing CUDs, SUDs, and Spot VMs

Review BigQuery capacity, table layouts, storage retention, and SQL to improve cost efficiency. Compare billing options, plan partitioned tables, and evaluate search indexes, stored aggregation results, and BI Engine for suitable workloads.

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• Compare On-Demand pricing with Editions
• Review storage billing models and retention periods
• Design table partitioning and clustering

Prepare for engineering interviews with structured practice in algorithms, system design, and questions about past experience. Develop explanations of your reasoning, analyze solution efficiency, discuss design tradeoffs, and organize concrete stories about your work.

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• Practicing algorithm problems and explaining solutions
• Rehearsing system design interviews
• Structuring stories about past work experience

Design evaluation systems that measure agent results across accuracy, completeness, citations, and tool efficiency. Build repeatable tests, compare changes against a baseline, and define quality checks that catch regressions before deployment and monitor production performance.

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• Compare agent configurations against a baseline
• Catch quality regressions before deployment
• Measure the effects of context strategies

Design persistent agent memory that retains knowledge across sessions and retrieves relevant facts when needed. Compare storage approaches, track people and changing information, and plan memory consolidation and evaluation based on the kinds of questions the agent must answer.

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• Retain user preferences across conversations
• Track people and entities consistently across sessions
• Retrieve facts valid at a specific point in time

Learn how instructions, documents, tool results, and conversation history form an AI model’s context. Understand attention limits, why information in the middle can be missed, and how relevance and placement inform decisions about what to include.

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• Learning the basics of context engineering
• Understanding why more input does not always help
• Explaining how instruction placement affects attention

Plan how an AI agent uses limited input space through reusable prompt prefixes, shorter tool outputs, summaries, and task splitting. Measure token use, response quality, and latency to choose optimizations that provide a net benefit.

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• Reducing token use from lengthy tool outputs
• Improving prompt cache reuse
• Planning context budgets for long conversations

Optimize transformer attention, the calculation that relates tokens to one another, using Flash Attention. The skill guides PyTorch integration, GPU memory reduction, performance measurement, and output comparisons for training or inference with long sequences.

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• Reducing GPU memory use with long sequences
• Integrating Flash Attention into PyTorch models
• Comparing runtime and outputs before and after changes

Configure large language model training across multiple NVIDIA GPUs with Megatron-Core. The skill helps choose how to split models and workloads, set training parameters, monitor performance, and address memory shortages or low GPU utilization.

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• Planning distributed training for a LLaMA-style model
• Distributing MoE expert networks across GPUs
• Tuning GPU utilization and training throughput

Apply HQQ quantization, which stores language model weights using fewer bits without calibration examples. Covers precision settings, inference backends, saving compressed models, deployment with HuggingFace Transformers or vLLM, and further training with LoRA.

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• Compressing a model without calibration data
• Comparing 4-bit and 2-bit quantization settings
• Running quantized models with HuggingFace Transformers or vLLM

Design and improve prompts for language models, then test their accuracy and consistency. Create reusable templates, JSON output schemas that define response structure, evaluation criteria, and test cases while considering token use and model differences.

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• Designing prompts for a new AI application
• Improving inconsistent model responses
• Defining JSON response formats

Investigate observed slowdowns or increased CPU, memory allocation, and IO costs in a process you control. Use controlled timing and runtime-specific profiling tools to test a cause, then check performance and behavior after a fix.

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• Investigate a command that has become slower
• Locate CPU or memory allocation hotspots
• Separate transport waits from backend processing costs

Review Apache Pinot changes affecting frequently executed code or making performance claims in a pull request. Relevant areas include operators, segment readers, aggregation functions, and locking inside loops that process individual rows.

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• Review changes to frequently executed processing code
• Review performance improvement claims in a pull request
• Review locking inside per-row loops

Rewrite long prompts into shorter, clearer instructions for language models. Preserve essential requirements while removing repetition, defining scope, and making output formats and success criteria explicit to reduce ambiguity and unnecessary tokens.

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• Shortening a long prompt while preserving requirements
• Clarifying vague instructions and success criteria
• Making output formats and length limits explicit

Comprehensive Google Ads account health analysis detecting wasted spend, search term leaks, negative keyword gaps, bid strategy issues, and Quality Score problems. Use when analyzing campaign data, pasting Google Ads exports, reviewing account performance, or requesting a full diagnostic of advertising spend efficiency. Platform: Google.

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Multi-channel budget optimization using MER, marginal ROAS, and diminishing returns analysis. Use when pasting multi-channel spend and results data, requesting reallocation recommendations, analyzing budget shift priorities, or optimizing marketing efficiency across Google, Meta, TikTok, and other channels. Platform: Google and Meta.

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