「angle」の検索結果

Reviews a proposal, business case, deck or vendor contract in character as a Head of Procurement archetype and returns a DRAFT review in the chat with a verdict, findings that cite the exact passage or clause, commercial risks, what would change the verdict and five interrogation questions. Use when the user asks to "review this from a procurement angle", "what would procurement say about this proposal", "pressure-test this vendor contract commercially", "find the holes before the sourcing committee" or "check the renewal terms like a Head of Procurement would". Do not use for liability, IP, regulatory or signing-authority questions, use general-counsel-reviewer instead; for whether the spend fits the budget, use cfo-reviewer; for vendor security, use ciso-reviewer. Drafts for human review; never approves, authorises or signs 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。

Research products via AI tools, cross-reference live stock, generate WooCommerce-ready copy, and extract marketing angles. Covers the full research-to-copy pipeline with remote_wp_connection stock checks and paper_store persistence. Use when researching products by SKU/name/category, generating WooCommerce descriptions, preparing product data for campaigns, or cross-referencing catalog files against live inventory.

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 to "write ad copy", "generate RSA headlines", or "build ad creative at volume"; produces ad units — RSA headlines/descriptions, hooks, and an angle matrix — message-matched to the destination landing page. Not for scoring an ad account — use ad-account-auditor; not for the post-click page — use landing-optimizer; not for organic articles — use content-writer. 广告创意/广告文案/RSA标题

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。

Generate and iterate paid-ad creative systems: angles, hooks, copy, concepts, variants, fatigue management and test matrices.

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 for "interrogate", "adversarial review", "multi-model review", "challenge this", "stress test this code", "find blind spots", or "tear this apart". Multiple LLM reviewers challenge changes from independent angles.

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。

Expert in Cloudflare Workers and the Edge Computing ecosystem. Covers Wrangler, KV, D1, Durable Objects, and R2 storage.

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 working with Cloudflare R2 — cloudflare R2 object storage management via the wrangler CLI and Cloudflare API. Covers buckets, objects, usage metrics, CORS policies, and lifecycle rules. Use when managing R2 storage or reviewing bucket configurations.

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。

Expert in Cloudflare Workers and the Edge Computing ecosystem. Covers Wrangler, KV, D1, Durable Objects, and R2 storage.

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。

Animation, morphing, and shader best practices for Liquid Glass and SwiftUI motion on iOS 26+/macOS 26+ through iOS 27. Use when animating glass or building UI motion: glassEffectID, @Namespace morphing, GlassEffectContainer spacing, glassEffectUnion, glassEffectTransition (.matchedGeometry/.materialize), withAnimation, springs, UnitCurve, PhaseAnimator, KeyframeAnimator, @Animatable, matchedTransitionSource, navigationTransition zoom, scrollTransition, visualEffect, Metal shaders (colorEffect, distortionEffect, layerEffect, [[stitchable]]), accessibilityReduceMotion gating, Instruments/xctrace profiling of hangs and animation hitches. Also fires on symptoms: glass morph not animating, view pops in instead of morphing, glass fades instead of materializing, morph flickers with Reduce Motion, menu glass morphs as a rectangle, animation runs on insert but not removal, PhaseAnimator loops forever or fires once, shader shows clipped edges (maxSampleOffset), animation hitches or main-thread hangs while scrolling glass chrome. For static glass, availability gating, toolbars, UIKit/AppKit glass, or accessibility of the material itself use the liquid-glass skill first.

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。

Liquid Glass material best practices for SwiftUI, UIKit, and AppKit on iOS 26+/macOS 26+ (Tahoe) through iOS 27/macOS 27. Use when implementing, reviewing, or migrating to glass: glassEffect, Glass variants (.regular/.clear/.identity), tint, interactive(), GlassEffectContainer, glassEffectUnion, toolbar glass, ToolbarSpacer, scrollEdgeEffectStyle, sharedBackgroundVisibility, tabBarMinimizeBehavior, backgroundExtensionEffect, NSGlassEffectView, UIGlassEffect, Tahoe window chrome, glass accessibility (Reduce Transparency/Increase Contrast), pre-26 Material fallbacks, UIDesignRequiresCompatibility. Also fires on symptoms: glass renders dark or muddy, glass looks like a flat tinted rectangle, glass button doesn't register taps, tap passes through glass, "'glassEffect' is only available in iOS 26.0 or newer", "ambiguous use of 'opacity'", "'cornerRadius' was deprecated", glass looks different in simulator vs device, screenshots of glass UI don't match. For glass animation, morphing, glassEffectID, transitions, Metal shaders, or performance profiling use the liquid-glass-motion skill instead.

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。

Builds personal brand assets using Seth Godin's frameworks: signature symbol, slogan, surprise (Purple Cow), salient idea, and signature story. Use when named (branding), or when the user asks for a brand symbol, slogan, tagline, Purple Cow, counterintuitive angle, core idea, brand story, or personal brand positioning.

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。

Analyze a job description, hiring post, or recruitment page for role requirements, evaluation criteria, keywords, risks, and resume or cover letter angles. Use when the user provides a JD URL/text, asks what a company is looking for, wants JD-resume gap analysis, or needs inputs for a tailored cover letter. 한국어 요청도 포함합니다 - 채용공고 분석, 공고 분석, JD 분석, 직무기술서 분석, 자격요건, 우대사항, 이 공고 뭘 원하는지, 공고랑 내 경력 비교.

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。

Hamiltonian mechanics — symplectic integrators (leapfrog, Yoshida), Hamilton's equations, Poisson brackets, canonical transformations, action-angle variables, and KAM theory analysis. Use for energy-conserving long-time integration of conservative systems.

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 docked / generated protein-ligand poses using PoseBusters physical-validity tests, strain energy quantification, geometric checks (planarity, vdW overlap, bond/angle distortion), and pose-energy reasonableness. Filters AI-docking outputs (DiffDock, EquiBind, NeuralPLexer) where ~50% of poses fail physical-validity tests. Use when QC-ing docking results, comparing classical vs ML docking outputs, or filtering pose lists before SAR 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。

Reads a specific actor's live telemetry from a running CARLA server — location and rotation, velocity and speed, acceleration, angular velocity, and for vehicles the control input (throttle/steer/brake), front-wheel steer angle and mass. All kinematics come from one world snapshot so they are frame-consistent. Use when the user asks "how fast is it going", "where/what heading is the car", "show the ego's telemetry", or "stream a vehicle's state". Resolve ambiguous targets with the world-data skill.

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。

Loads, converts, and manipulates Hi-C contact matrices in cooler format (.cool/.mcool/.scool) and Juicer .hic, using cooler (Python + CLI), hic2cool, and hictk. Covers the single-resolution mcool URI (file.mcool::/resolutions/<bp>), the load-bearing divisive-vs-multiplicative weight-naming rule (KR/VC/VC_SQRT auto-divisive vs cooler's multiplicative weight), what survives .hic<->.cool conversion (FRAG matrices and norm vectors do not), raw-vs-balanced coarsening, the .pairs upper-triangle/chromsize-order contract, and chrom-naming/bin-table provenance. Use when loading a cooler, converting .hic to .mcool, selecting a resolution, building a cooler from pairs or a matrix, coarsening/zoomifying, importing Juicer norm vectors, or debugging all-NaN balanced matrices and chr1-vs-1 empty fetches.

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。

Renders Hi-C contact matrices honestly and reproducibly with matplotlib, cooltools, HiCExplorer, pyGenomeTracks, FAN-C, CoolBox, and plotgardener. Covers the raw/ICE-balanced/observed-over-expected transform choice, LogNorm vs symmetric-diverging colormaps with vmax/percentile clipping, resolution-to-feature matching (compartments 100-500kb, TADs 10-40kb, loops 5-10kb), square vs rotated-triangle track-stacking, NaN/white-stripe handling, virtual 4C, APA/saddle/on-diagonal pileups, two-condition side-by-side and log2-ratio maps, and interactive (HiGlass) vs scripted-static publication figures. Use when plotting a contact matrix, choosing a normalization or color scale, building a multi-track Hi-C figure, making a virtual 4C profile, piling up loops/boundaries, or comparing two conditions.

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。

Defines cell populations in flow and spectral cytometry through manual gates (rectangle, polygon, quadrant, boolean) and reproducible automated gating (openCyto gating templates, flowDensity data-driven thresholds, flowClust model-based gates), organized as a hierarchical GatingSet (flowWorkspace) and round-tripped with FlowJo via CytoML. Covers the canonical gate order (time -> debris -> singlets -> live -> lineage), FMO-vs-isotype boundary setting, gate-order dependence and recompute semantics, rare-event/MRD gating, and per-population statistics. Use when building a gating strategy, automating a manual FlowJo scheme across samples, choosing manual vs data-driven gates, or extracting population frequencies.

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 docked / generated protein-ligand poses using PoseBusters physical-validity tests, strain energy quantification, geometric checks (planarity, vdW overlap, bond/angle distortion), and pose-energy reasonableness. Filters AI-docking outputs (DiffDock, EquiBind, NeuralPLexer) where ~50% of poses fail physical-validity tests. Use when QC-ing docking results, comparing classical vs ML docking outputs, or filtering pose lists before SAR 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。

Schedules reactive frontend states cleanly using entangle directives in PHP and JS.

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 instead of WebSearch for ANY question requiring web research. Trigger on queries like "what is X", "explain X", "compare X and Y", "research X", or before content generation tasks. Provides systematic multi-angle research methodology instead of single superficial searches. Use this proactively when the user's question needs online information.

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。

Loads, converts, and manipulates Hi-C contact matrices in cooler format (.cool/.mcool/.scool) and Juicer .hic, using cooler (Python + CLI), hic2cool, and hictk. Covers the single-resolution mcool URI (file.mcool::/resolutions/<bp>), the load-bearing divisive-vs-multiplicative weight-naming rule (KR/VC/VC_SQRT auto-divisive vs cooler's multiplicative weight), what survives .hic<->.cool conversion (FRAG matrices and norm vectors do not), raw-vs-balanced coarsening, the .pairs upper-triangle/chromsize-order contract, and chrom-naming/bin-table provenance. Use when loading a cooler, converting .hic to .mcool, selecting a resolution, building a cooler from pairs or a matrix, coarsening/zoomifying, importing Juicer norm vectors, or debugging all-NaN balanced matrices and chr1-vs-1 empty fetches.

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。

Renders Hi-C contact matrices honestly and reproducibly with matplotlib, cooltools, HiCExplorer, pyGenomeTracks, FAN-C, CoolBox, and plotgardener. Covers the raw/ICE-balanced/observed-over-expected transform choice, LogNorm vs symmetric-diverging colormaps with vmax/percentile clipping, resolution-to-feature matching (compartments 100-500kb, TADs 10-40kb, loops 5-10kb), square vs rotated-triangle track-stacking, NaN/white-stripe handling, virtual 4C, APA/saddle/on-diagonal pileups, two-condition side-by-side and log2-ratio maps, and interactive (HiGlass) vs scripted-static publication figures. Use when plotting a contact matrix, choosing a normalization or color scale, building a multi-track Hi-C figure, making a virtual 4C profile, piling up loops/boundaries, or comparing two conditions.

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。

Defines cell populations in flow and spectral cytometry through manual gates (rectangle, polygon, quadrant, boolean) and reproducible automated gating (openCyto gating templates, flowDensity data-driven thresholds, flowClust model-based gates), organized as a hierarchical GatingSet (flowWorkspace) and round-tripped with FlowJo via CytoML. Covers the canonical gate order (time -> debris -> singlets -> live -> lineage), FMO-vs-isotype boundary setting, gate-order dependence and recompute semantics, rare-event/MRD gating, and per-population statistics. Use when building a gating strategy, automating a manual FlowJo scheme across samples, choosing manual vs data-driven gates, or extracting population frequencies.

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。