「efficiency」の検索結果

Guides when to ask clarifying questions versus proceed autonomously. Use to reduce unnecessary clarifying questions when intent is clear.

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。

Tracks per-agent token usage and flags waste in parallel dispatch. Use when evaluating parallel agent efficiency or after a multi-agent run.

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。

Architecture guide for GDPR-compliant federated learning systems. Covers horizontal and vertical FL, aggregation strategies (FedAvg, FedProx), communication efficiency, secure aggregation, and differential privacy integration. Includes privacy guarantees analysis and deployment patterns for cross-organizational ML without data sharing.

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。

Validates and optimizes Blender assets for polycount, topology, UV efficiency, material count, naming, collections, and game-ready performance. Use before export on all production assets via MCP.

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。

Route substantial coding or repository-review work through a two-stage Sol commander/verifier and separate Luna Max CLI worker. Use when the user explicitly asks Sol to direct or supervise Luna, explicitly requests this router, needs an isolated auditable Luna-owned implementation with a Sol review loop, or asks to diagnose or measure the router itself. Do not use merely because a task involves coding, verification, progress reporting, or token saving. For small or straightforward tasks, keep work in Sol unless the user explicitly requests Luna; router configuration, usage, and efficiency questions should use read-only inspection without launching a worker.

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。

Plan, diagnose, and verify performance budgets, latency targets, load tests, capacity estimates, bottleneck analysis, caching strategy, query efficiency, queue throughput, and regression gates. Use when a feature may be slow, a system must scale, a performance regression is suspected, or release readiness depends on throughput, cost, memory, CPU, or response time.

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。

Applies the reasoning style of Ilya Sutskever (deep learning pioneer, co-founder of OpenAI and Safe Superintelligence Inc.) to problems involving AI architecture, scaling laws, alignment, and research strategy. Reach for this skill whenever discussing machine learning paradigms, the limits of compute and data, AGI timelines, superintelligence safety, or deciding between hardcoding vs. learning. Trigger this skill for questions about next-word prediction, reinforcement learning efficiency, generalization gaps, and transitioning from brute-force scaling to fundamental research, even if the user doesn't explicitly name him.

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。

Applies the engineering and research philosophies of Jeff Dean, Chief Scientist at Google DeepMind and Google Research. Reach for this skill whenever you are designing large-scale distributed systems, optimizing latency and energy efficiency, or making architectural decisions about machine learning infrastructure. It should trigger automatically for topics involving hardware-ML co-design, model distillation, sparse activation, massively multi-task models, or scaling systems by 5x to 10x. Use this skill to evaluate system bottlenecks, transition from specialized to unified models, and optimize experimental velocity. Apply his mental models to avoid premature 100x scaling and to treat AI models as reasoning engines rather than memorization databases.

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 writing, completing, improving, or adapting SKILL.md and agents/*.md in a bundle-plugin — integrating external skills, filling scaffolded stubs, or rewriting for better triggering and token 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。

Financial ratio analysis, profitability ratios ROE ROA ROIC, liquidity ratios current quick cash, leverage ratios debt-to-equity interest coverage, efficiency ratios asset turnover inventory turnover DSO, valuation ratios PE PB EV/EBITDA, cross-company ratio comparison, DuPont 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。

Operational KPI tracking, headcount trends, utilization rates, backlog analysis, book-to-bill ratio, operational efficiency metrics, capacity utilization, productivity metrics, operational leverage, same-store sales

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。

Calculate read depth and coverage across genomic intervals using bedtools genomecov and coverage. Generate bedGraph files, compute per-base depth, and summarize coverage statistics. Use when assessing sequencing depth, creating coverage tracks, or evaluating target capture 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。

Analyzes base-editing screens for variant function. Covers library design (Sanson 2020 GRACE, Hanna 2021 BRCA1/2 SNV scanning, Cuella-Martin 2021), CBE vs ABE chemistry choice (BE3/BE4 vs ABE7.10/ABE8.20/ABE8e), editing-window math (positions 4-8 from PAM-distal end, wider for ABE8e), bystander-edit quantification and the variant-call ambiguity it creates, sgRNA-efficiency filtering before hit calling, indel byproduct interpretation, the substitution-vs-indel diagnostic, variant annotation against ClinVar / COSMIC, and the Broad be-validation-pipeline. Use when designing a BE variant screen, choosing CBE vs ABE for a specific edit, interpreting bystander-confounded hits, distinguishing functional signal from indel artifact, integrating CRISPResso2 output with screen scoring, or deciding BE vs PE for SNV installation.

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。

Workflow for ribosome profiling, P-site aware preprocessing, periodicity checks, ORF detection, and translation efficiency 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。

Think and work like an expert Mathematical Statistician. Use when a task calls for Mathematical Statistician judgment. Reasons from LAN, empirical processes, and influence functions; proves M/Z-estimator limits, minimax rates (Fano/Le Cam/Assouad), and semiparametric efficiency while validating with ADEMP simulations and treating naive bootstrap, non-Donsker classes, and debiasing sparsity violations as first-class failure modes.

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。

Think and work like an expert Heat Transfer Engineer. Use when a task calls for Heat Transfer Engineer judgment. Reasons from conduction, convection, radiation, and coupled fluid-solid physics through thermal resistance networks, Biot/NTU/film-temperature scaling, LMTD and epsilon-NTU exchanger methods, fin efficiency, and conjugate-heat-transfer CFD while treating contact resistance and TIM pump-out, fouling, boiling CHF, non-condensables, and non-conservative interface flux mapping as first-class failure modes.

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。

Think and work like an expert Electric Machines Engineer. Use when a task calls for Electric Machines Engineer judgment. Reasons from magnetic circuit design, dq-frame machine models, FEM flux paths, and drive efficiency maps while treating saturation, cogging, thermal derating, and inverter harmonics as first-class failure modes.

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。

Think and work like an expert Antenna Engineer. Use when a task calls for Antenna Engineer judgment. Reasons from gain–directivity–efficiency, Chu–Harrington bandwidth limits, and array factor through HFSS/CST/FEKO synthesis, IEEE 149-2021 NF/FF/CATR metrology, CTIA TRP/TIS/ECC OTA, and Friis link budgets while treating ground-plane truncation, active impedance in arrays, range ripple, and S₁₁≠pattern conflation as first-class failure modes.

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。

Internal — for Boundless team members only. Query the Boundless Indexer REST API for on-chain market data, staking, PoVW rewards, delegations, and market efficiency on prod/staging environments. Use when the user asks about proof requests, provers, requestors, staking data, PoVW rewards, delegations, market aggregates, leaderboards, or wants to fetch data from the indexer API on live networks. Also use when the user mentions "indexer" and wants to look up addresses, requests, or market statistics on deployed environments. Do NOT use for debugging local code changes, reviewing PRs, or investigating issues in the codebase 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。

Half-Quadratic Quantization for LLMs without calibration data. Use when quantizing models to 4/3/2-bit precision without needing calibration datasets, for fast quantization workflows, or when deploying with vLLM or HuggingFace Transformers.

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。

World-class expertise in measuring, attributing, and optimizing AI-generated content performance. Combining data science rigor with content strategy intelligence to answer the questions traditional content analytics can't: Is AI content performing? Which AI variations convert? What's the true ROI of AI vs traditional content creation? This isn't vanity metrics for robots. This is the discipline of proving AI content drives business outcomes - and using that data to make AI content systems better, faster, and more profitable. Built on the principles of companies like Jasper, Copy.ai, and Notion who've scaled AI content operations with measurement as the foundation. Use when "ai content performance, ai content analytics, measure ai content, ai content roi, ai vs human content, ai content attribution, ai content testing, ai variation testing, ai content dashboard, ai content metrics, prompt performance, ai content conversion, ai content quality score, content velocity, ai content efficiency, ai-content, analytics, measurement, attribution, roi, ab-testing, performance, optimization, data-driven, content-analytics" mentioned.

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。

Ultra-compressed communication mode. Cuts token usage ~75% by speaking like caveman while keeping full technical accuracy. Supports intensity levels: lite, full (default), ultra, wenyan-lite, wenyan-full, wenyan-ultra. Use when user says "caveman mode", "talk like caveman", "use caveman", "less tokens", "be brief", or invokes /caveman. Also auto-triggers when token efficiency is requested.

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 Best Practices workflow skill. Use this skill when the user needs Angular performance optimization and best practices guide. Use when writing, reviewing, or refactoring Angular code for optimal performance, bundle size, and rendering efficiency and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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。