Results for “optimize”

Optimize a product photo and compose it into a promotional banner with sale text and pricing.

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OpenAI API (developers.openai.com) の Evals / Fine-tuning リファレンス。 Evals API, Datasets, graders（string check / text similarity / model / python / multi）, agent evals, trace grading, external models, Promptfoo（移行先）, red teaming, SFT, DPO, RFT, vision fine-tuning, distillation, prompt optimizer, fine-tuning best practices。 Evals は 2026-11-30 停止予定、セルフサービス fine-tuning は段階的廃止中。

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最適化モデルの運用設計。自動化・監視・フォールバックを定義する

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追加データの依頼書を生成する。何がなぜ必要かを非技術者向けに説明

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実行可能性（feasibility）を確認する。問題の複雑度に応じて一発解きor段階的解法を選ぶ

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受け取ったデータから最適化問題の構造を把握し、仮説を立てる。最も重要なスキル。

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最適化結果を非技術者にも伝わる改善提案書にまとめる

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ボトルネックに合わせた改善策を設計・実行・検証する

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Builds an interactive HTML dashboard visualizing the Agent Skills ecosystem by fetching live data from github.com/agentskills/agentskills. Use this skill whenever the user wants to explore, research, or visualize Skills architecture, the SKILL.md spec, ecosystem adoption, repo stats, or any Skills intelligence dashboard. Trigger when the user mentions "skills dashboard", "skills ecosystem", "skills architecture overview", "agentskills repo", or wants to analyze how different tools implement Agent Skills. Optimized for Perplexity Computer — Computer fetches all data live, no static files required.

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Verifies and injects Google Tag Manager (GTM), GA4 event triggers, and optimized meta tags.

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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.

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Writing style guide for technical content, social media, blog posts, READMEs, git commits, and developer documentation. Optimized to avoid AI detection patterns. Use when writing any content beyond code.

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Optimize Bazel builds for large-scale monorepos. Use when configuring Bazel, implementing remote execution, or optimizing build performance for enterprise codebases.

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Comprehensive AWS cost analysis and optimization recommendations using AWS CLI and Cost Explorer

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Optimize end-to-end application performance with profiling, observability, and backend/frontend tuning. Use when coordinating performance optimization across the stack.

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Optimize content for AI search and LLM citations across AI Overviews, ChatGPT, Perplexity, Claude, Gemini, and similar systems. Use when improving AI visibility, answer engine optimization, or citation readiness.

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Build complex AI systems with declarative programming, optimize prompts automatically, create modular RAG systems and agents with DSPy - Stanford NLP's framework for systematic LM programming

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Web browser automation with AI-optimized snapshots for claude-flow agents

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Track ML experiments with automatic logging, visualize training in real-time, optimize hyperparameters with sweeps, and manage model registry with W&B - collaborative MLOps platform

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Optimizes LLM inference with NVIDIA TensorRT for maximum throughput and lowest latency. Use for production deployment on NVIDIA GPUs (A100/H100), when you need 10-100x faster inference than PyTorch, or for serving models with quantization (FP8/INT4), in-flight batching, and multi-GPU scaling.

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Optimizes transformer attention with Flash Attention for 2-4x speedup and 10-20x memory reduction. Use when training/running transformers with long sequences (>512 tokens), encountering GPU memory issues with attention, or need faster inference. Supports PyTorch native SDPA, flash-attn library, H100 FP8, and sliding window attention.

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Fast tokenizers optimized for research and production. Rust-based implementation tokenizes 1GB in <20 seconds. Supports BPE, WordPiece, and Unigram algorithms. Train custom vocabularies, track alignments, handle padding/truncation. Integrates seamlessly with transformers. Use when you need high-performance tokenization or custom tokenizer training.

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Token-optimized structural code search using tree-sitter AST parsing. Use instead of reading full files when you need to understand code structure, find functions, or explore a codebase efficiently.

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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.

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