「efficiency」の検索結果

[omh] Run efficiency and context usage report: report supplied local run efficiency while provider and host data stay unobserved. Use when the user says: run-efficiency, run efficiency report, local run efficiency, context utilization, tool duration report, 실행 효율 리포트, 컨텍스트 사용량, 도구 지연 시간.

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 sales pipeline health, revenue forecasting accuracy, and go-to-market efficiency metrics for SaaS revenue optimization. Use when analyzing sales pipeline coverage, forecasting revenue, evaluating go-to-market performance, reviewing sales metrics, assessing pipeline analysis, tracking forecast accuracy with MAPE, calculating GTM efficiency, or measuring sales efficiency and unit economics for SaaS teams.

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。

Designs and analyzes pooled prime-editor (PE) screens for installing precise genetic variants without bystander confounding. Covers pegRNA design with PRIDICT and PRIDICT2 for predicting per-pegRNA editing efficiency, pegRNA architecture (spacer + scaffold + PBS + RTT), PE2/PE3/PE3b/PEmax variants, MOSAIC in situ saturation mutagenesis, the PRIME pooled-screen methodology (Ren 2023; ~3,699 ClinVar variant screens), chromatin context as a major locus-level determinant of PE efficiency, scaffold-incorporation and indel byproduct quantification with CRISPResso2, and the cross-modal validation strategy of PE + base-editor screens for variant function. Use when designing a pegRNA library for variant installation, choosing between BE and PE for a specific edit, predicting pegRNA efficiency before library synthesis, analyzing PE screen output, distinguishing intended-edit from scaffold-incorporation, or scaling PE screens to thousands of variants.

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。

Evaluates sales efficiency with CAC payback, sales cycle analysis, channel economics, and pipeline conversion metrics. Use when assessing GTM efficiency, analyzing sales productivity, or evaluating distribution strategy.

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。

Evaluates capital efficiency with burn multiple, net new ARR per dollar burned, and path to profitability analysis. Use when analyzing burn efficiency, assessing capital needs, or modeling runway scenarios.

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。

Designs and analyzes pooled prime-editor (PE) screens for installing precise genetic variants without bystander confounding. Covers pegRNA design with PRIDICT and PRIDICT2 for predicting per-pegRNA editing efficiency, pegRNA architecture (spacer + scaffold + PBS + RTT), PE2/PE3/PE3b/PEmax variants, MOSAIC in situ saturation mutagenesis, the PRIME pooled-screen methodology (Ren 2023; ~3,699 ClinVar variant screens), chromatin context as a major locus-level determinant of PE efficiency, scaffold-incorporation and indel byproduct quantification with CRISPResso2, and the cross-modal validation strategy of PE + base-editor screens for variant function. Use when designing a pegRNA library for variant installation, choosing between BE and PE for a specific edit, predicting pegRNA efficiency before library synthesis, analyzing PE screen output, distinguishing intended-edit from scaffold-incorporation, or scaling PE screens to thousands of variants.

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。

Designs and analyzes pooled prime-editor (PE) screens for installing precise genetic variants without bystander confounding. Covers pegRNA design with PRIDICT and PRIDICT2 (Mathis 2023/2024) for predicting per-pegRNA editing efficiency, pegRNA architecture (spacer + scaffold + PBS + RTT), PE2 / PE3 / PE3b / PEmax / PEAR variants, MOSAIC in situ saturation mutagenesis (Hsu JY et al 2024 bioRxiv), the PRIME pooled-screen methodology (Erwood/Doman 2023 Nat Biotechnol 41:885; ~3,699 ClinVar variant screens), chromatin context as a primary determinant of PE efficiency, scaffold-incorporation and indel byproduct quantification with CRISPResso2, and the cross-modal validation strategy of PE + base-editor screens for variant function. Use when designing a pegRNA library for variant installation, choosing between BE and PE for a specific edit, predicting pegRNA efficiency before library synthesis, analyzing PE screen output, distinguishing intended-edit from scaffold-incorporation, or scaling PE screens to thousands of variants.

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 and improve GitHub Codespaces efficiency. Use this skill when a user wants faster Codespaces startup, lower Codespaces spend, slim devcontainers, right-size machines, tune idle timeout, or scope prebuilds to branches with sustained usage.

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 GitHub Actions workflow efficiency and recommend fixes to reduce CI minutes and costs.

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 SaaS unit economics and capital efficiency. Use when deciding whether the business can scale efficiently or needs correction.

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。

Review a git diff or explicit file scope for reuse, code quality, efficiency, clarity, and standards issues, then optionally apply safe Codex-driven fixes. Use when the user asks to "simplify code", "review changed code", "check for code reuse", "review code quality", "review efficiency", "simplify changes", "clean up code", "refactor changes", or "run simplify".

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 and prime editing outcomes including editing efficiency, bystander edits, and indel frequencies. Use when quantifying CRISPR base editor results, comparing ABE vs CBE efficiency, or assessing prime editing fidelity.

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。

CRISPResso2 for analyzing CRISPR gene editing outcomes. Quantifies indels, HDR efficiency, and generates comprehensive editing reports. Use when analyzing amplicon sequencing data from CRISPR editing experiments to assess editing 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。

Optimizes Simulink models for Embedded Coder generated code. Use when asked to optimize or improve generated code, or reduce code metrics for a Simulink model. Targets: execution time, memory footprint (RAM, ROM, stack, data copies), code size, MISRA compliance, or any semantically similar generated-code metric. Works iteratively — measures baseline, suggests changes, applies, and re-measures to confirm improvement. Triggers can be prompts similar to: optimize generated code runtime, reduce runtime, shrink code size, improve code efficiency, reduce memory usage, speed up generated code, follow MISRA compliance and so on. CAUTION: Do NOT attempt to optimize Simulink models for generated code efficiency without following this skill — the iterative measurement, gating, and rollback workflow is essential for safe optimization.

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 management across process optimization, efficiency, and continuous improvement. Use when designing workflows, building capacity plans, evaluating vendors, running Lean Six Sigma DMAIC projects, or optimizing cost-per-unit.

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 pipeline coverage, tracks forecast accuracy with MAPE, and calculates GTM efficiency metrics for SaaS revenue optimization

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 the user asks about Higgsfield Assist (the built-in GPT-5 copilot), how to use the platform's native AI assistant, credit optimization strategies, plan selection, how to get more from fewer credits, or platform efficiency tips.

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 a multi-agent review of changed files for scope, reuse, quality, efficiency, clarity, and altitude issues followed by fixes. Use when the user asks to "simplify code", "review changed code", "check for code reuse", "review code quality", "review efficiency", "simplify changes", "clean up code", "refactor changes", or "run simplify".

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 and optimizes code for better performance, memory usage, and efficiency. Use when code is slow, memory-intensive, or inefficient. Supports Python and Java optimization including execution speed improvements, memory reduction, database query optimization, and I/O efficiency. Provides before/after examples with detailed explanations of why optimizations work, complexity analysis, and measurable performance improvements.

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 this skill whenever reasoning about manufacturing, scaling production, pricing strategies, operational efficiency, vertical integration, or labor compensation. Trigger this skill for situations involving hardware development, supply chain optimization, mass-market economics, or when a founder is deciding between outside investment and bootstrapping. Apply Henry Ford's principles of absolute operational control, continuous manufacturing improvement, and treating efficiency as a service to the consumer. Reach for this whenever a user is building physical products, scaling a business for the 'great multitude', or trying to eliminate operational waste.

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Thermal engineering for infrastructure: heat transfer (conduction, convection, radiation), cooling load analysis for data centers, heat exchanger sizing via LMTD and e-NTU, PUE/TUE/WUE efficiency metrics, and airflow management patterns. Activates for thermal analysis, data center cooling design, heat exchanger sizing, efficiency calculations, and airflow management.

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Evaluates netting benefits across derivative portfolios with bilateral vs cleared netting, portfolio compression, and capital savings. Use when analyzing netting efficiency, evaluating compression opportunities, or optimizing counterparty exposure.

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Audit Claude Code session logs (~/.claude/projects) for token waste and produce a ranked, attributed efficiency report (habit / skill_file / config). Use when the user asks to audit session efficiency, analyze token waste or cost blowups, review Claude Code usage cost, or asks "where are my tokens going".

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。

Measure CI churn cost for a pull request: how many commits and how many CI-compute seconds were burned on avoidable "fix the CI" push rounds. Use when you want to understand the efficiency cost of a PR's iteration cycle, get actionable pre-push check suggestions, or include a churn summary in a pipeline report. Triggers: "ci churn", "how much CI did we burn", "avoidable commits", "ci efficiency", "wasted CI time", "churn report", "/ci-churn". Called automatically by /fp-drift at pipeline close.

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。