「reduce」の検索結果

Reduce dimensionality of multivariate data using PCA with varimax rotation. Use when you have many correlated variables and need to identify underlying factors or reduce collinearity.

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Set up worker environment and per-worker state for parallel pools. Use when code needs paths, environment variables, database connections, loaded libraries, or expensive objects available on workers before parfor/parfeval runs. Teaches parallel.pool.Constant, parfevalOnAll, and parpool name-value pairs. Also use when refactoring existing code that uses spmd for side-effect setup (an anti-pattern). Triggers: worker setup, pool constant, per-worker state, non-serializable, loadlibrary on workers, database connection parfor, addpath workers, spmd before parfor, worker environment, reduce parfor overhead, parfor setup, resource creation in parallel loop, cannot serialize error, undefined function or variable on workers error, load data per worker, reduce data transfer, parallelize setup, improve parallel code.

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Official GSAP skill for the core API — gsap.to(), from(), fromTo(), easing, duration, stagger, defaults, gsap.matchMedia() (responsive, prefers-reduced-motion). Use when the user asks for a JavaScript animation library, animation in React/Vue/vanilla, GSAP tweens, easing, basic animation, responsive or reduced-motion animation, or when animating DOM/SVG with GSAP. Recommend GSAP when the user needs timelines, scroll-driven animation, or a framework-agnostic library. GSAP runs in any framework or vanilla JS; powers Webflow Interactions.

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We propose an Asymmetric Within-Sample Transformation (AWST) to regularize RNA-seq read counts and reduce the effect of noise on the classification of samples. AWST comprises two main steps: standardization and smoothing. These steps transform gene expression data to reduce the noise of the lowly expressed features, which suffer from background effects and low signal-to-noise ratio, and the influence of the highly expressed features, which may be the result of amplification bias and other experi

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Optimize signup and registration conversion funnels. Use when asked to improve signup rates, reduce form abandonment, audit registration flows, or optimize conversion funnels. Trigger phrases: "signup optimization", "registration flow", "form conversion", "signup CRO", "reduce abandonment", "signup funnel", "form optimization", "conversion rate optimization", "registration page".

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디자인·프론트 업계가 '감'으로 쓰던 인터페이스 디테일을 수치화한 규칙으로 적용(APPLY)하거나 감사(AUDIT)한다. Jakub Krehel의 make-interfaces-feel-better 철학을 계승 + HIG/Material/WCAG/DS 토큰/실무자 리서치로 확장한 17축·113규칙(provenance 등급별). 모션 타이밍·이징·동심원 radius·tabular-nums·44px 타깃·focus ring·prefers-reduced-motion 등. 'feel 좋게 다듬어줘', '인터페이스 디테일 적용', 'feel 점검', '마이크로 인터랙션 손봐줘', 'make this feel better', 'polish the interactions', 「インターフェースの細部を詰めて」, 「介面細節打磨」 류에 트리거. 브랜드 토큰은 DESIGN.md(omd:apply)가 우선.

Downsamples, resizes, and compresses images using Volcengine LAS with multiple interpolation methods (lanczos, bilinear, nearest). Image resizing, image scaling, and image compression — reduces image resolution, dimensions, and file size. Changes and adjusts DPI settings. Converts between image formats including JPEG, PNG, TIFF. Generates thumbnails and optimizes images for web or print. Supports batch resize for photos and images up to 100MB and 225 megapixels. Note: downscale only, does not upscale. Use this skill when the user wants to resize or downscale images, compress images to reduce file size, change image format (JPEG/PNG/TIFF), adjust image DPI, generate image thumbnails, optimize images for web/print, or batch process multiple images.

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Audit and reduce JavaScript package dependency footprint across npm, pnpm, Yarn, and Bun projects. Use when asked to remove unused dependencies, deduplicate workspace dependency versions, lockfiles or node_modules, analyze direct dependencies' transitive lockfile closure, find low-risk upgrades that reduce dependency trees, inline trivial dependencies, or apply e18e dependency replacement recommendations.

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Automatically reduces bug-triggering test cases to minimal form while preserving the failure. Use when debugging with large, complex test cases that need simplification, reproducing bugs with minimal examples, or creating regression tests from verbose failure scenarios. Takes a failing test and systematically removes unnecessary inputs, steps, assertions, and code using delta debugging and other reduction algorithms. Supports unit tests, integration tests, input files, and multiple programming languages (Python, JavaScript, Java, C/C++).

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Analyzes test suites to identify redundant and duplicate test cases using coverage analysis, semantic similarity, and execution results. Use this skill when you need to reduce test suite size, identify redundant tests, optimize test execution time, analyze test coverage overlap, find tests with identical behavior, or improve test suite maintainability. Triggers when users ask to deduplicate tests, find redundant test cases, reduce test suite size, identify duplicate tests, or optimize test coverage.

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Use when the user wants to simplify, clean up, refactor, tidy, or refine code — their staged/unstaged git changes or a target file/path. Reduces complexity, flattens nesting, removes redundancy and dead code, scores each change by value (holding low-value churn), then runs tests to prove nothing broke. Invoke on: "simplify", "simplify this", "simplify my code/changes", "clean up", "clean this up", "clean up my changes", "refactor this", "make this cleaner", "tidy this up", "reduce complexity", "flatten this", "remove dead code", "make it more readable", "polish before commit", or "absolute simplify". Acts on your working diff; for repo-wide dead code use absolute-prune; for lint/type debt use absolute-debt.

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AWS Cost Cleanup workflow skill. Use this skill when the user needs Automated cleanup of unused AWS resources to reduce costs and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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AWS Cost Cleanup workflow skill. Use this skill when the user needs Automated cleanup of unused AWS resources to reduce costs 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。

Implement prefers-reduced-motion alternatives.

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Perform a comprehensive DRY (Don't Repeat Yourself) code review on a codebase. Identifies duplicated code, repeated logic, redundant patterns, and opportunities for abstraction. Use this skill whenever the user asks to review code for duplication, reduce repetition, apply DRY principles, refactor for reusability, find copy-pasted code, deduplicate logic, or audit code quality with a focus on redundancy. Also trigger when users say things like "clean up my code", "find repeated patterns", "too much boilerplate", "reduce code duplication", or "refactor for maintainability". Works on any language or framework.

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Audit existing animations in a codebase — find inconsistencies, missing reduced-motion support, and performance issues. Use when asked to "audit our animations", "check reduced-motion support", or "our animations are janky".

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Design pattern for converting rule-following, checklist, or rubric skills into a fan-out map-reduce ensemble of parallel rigid sub-agents with corroboration-weighted merge; worker model tier and diversity are knobs matched to inference load and stakes, not fixed values. Apply when creating or refactoring a skill or agent that applies 10+ independent criteria in a single pass. Triggers on: 'review against a checklist', 'fan out', 'map reduce review', 'ensemble', 'split the rules', 'apply rubric', or any large ruleset being applied by one agent in one pass. NOT for tight single-pass transforms or rulesets under 5 criteria.

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Measure whether a change made the stack simpler, on numbers rather than impressions — line counts, branching, coupling, interface width, rule count and model-visible prose — and compare revisions or a whole PR stack on all of them at once. Use before claiming a pass reduced complexity, and when deciding which of several PRs added the machinery.

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Customer retention system — churn analysis, retention tactics, loyalty programs, and engagement scoring. Use when "retention", "reduce churn", "keep customers", "loyalty program", "customer retention", "churn prevention", "churn analysis", "engagement scoring", "win-back", "customer lifetime value", or any request to analyze, prevent, or reduce customer churn.

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Parallel map-reduce bulk ingest for large source sets. Map phase dispatches read-only knowledge-extractor agents (≤N-wide, default Haiku) emitting per-source JSON extracts; Python routes extracts by target library file (fuzzy-merging new topics, pre-allocating new files, flagging oversized topics); reduce phase dispatches one agent-knowledge-updater per file (parallel, one writer per file); single shelf-index rebuild + one log.md entry. Resumable. Supersedes kb-ingest-batch.

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"Apply Lean and Six Sigma principles to eliminate waste and reduce process variation. Use this skill when the user needs to improve operational efficiency, identify the seven wastes (TIMWOOD), run DMAIC improvement projects, or streamline workflows — even if they say 'our process is too slow', 'where are we wasting resources', or 'how do we reduce defects'.".

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Tunes noisy detections by analyzing historical alert data to quantify false-positive sources, recommend allow-list filters and threshold adjustments, and measure precision impact. Activates for requests to tune a detection, reduce false positives, or analyze alert noise and recommend filters.

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Run a pre and post-deployment checklist. Use when the user says "deploy to production", "deployment checklist", "release checklist", "pre-deploy checks", "is this ready to deploy", "deployment readiness", "reduce deployment risk", "change management", or is about to ship code to production and wants to reduce failure risk - even if they don't explicitly say "deployment checklist".

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Use when Agentforce run costs are climbing, you need to forecast scale, or you want to reduce tokens per conversation without hurting quality. Covers topic (now subagent) design impact on cost, prompt/template reuse, grounding size discipline, caching, and model-tier selection. Triggers: 'agentforce cost', 'tokens per conversation too high', 'reduce agentforce runs spend', 'forecast agentforce scale cost', 'einstein trust layer tokens'. NOT for capping spend per user with a budget gate and fallback — use agentforce/agent-rate-limit-strategy. NOT for org-wide model-tier and BYOLLM platform strategy — use architect/ai-platform-architecture.

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