609 件 ・ 関連度順
概要と使いどころ
Recover source-bound buyer jobs, struggling moments, desired progress, forces, workarounds, information acts, journey states, criteria, constraints, roles, locales, and authentic language. Use on approved ICP hypotheses plus customer, search, review, forum, procurement, support, or public-market evidence before designing an AI-visibility prompt architecture.
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Research any company, product, or service from a URL plus description and build a comprehensive, evidence-bound AEO/GEO/AI-visibility prompt panel across buyer jobs, information acts, journey states, B0-B5 proximity, aided status, roles, locales, variants, partitions, surfaces, and measurement lanes. Use when a user wants prompts to track in ChatGPT, Claude, Gemini, Perplexity, AI search, or answer engines; wants an AI-visibility measurement design; or needs a versioned panel rather than an SEO keyword list.
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。
Turn an approved measurement charter, ICPs, and buyer jobs into a budget-aware prompt coverage blueprint across proximity bands, aided status, information acts, journey states, roles, locales, evidence grades, partitions, and measurement lanes. Use before prompt wording to define required, optional, and prohibited canonical intent cells.
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。
Drive Unity 6.3 LTS character animation with Animator Controllers: states, transitions, parameters, blend trees, animation layers, and humanoid Avatar IK. Use when wiring an Animator, setting parameters from script (SetFloat/SetBool/SetTrigger), building blend trees, or when the user mentions Animator, Mecanim, state machine, blend tree, or .controller.
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。
Animate in Godot 4.7 three ways: AnimationPlayer for keyframed clips (incl. call and signal tracks), AnimationTree with state machines and blend spaces for character animation, and Tween for short procedural/UI tweens via create_tween(). Use when working with AnimationPlayer/AnimationTree nodes in a .tscn, blending character states, sprite-sheet animation, or code-driven Tweens.
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。
Structure and debug a LÖVE (Love2D) game in Lua: the love.load/update/draw loop, delta-time movement, input, and screen states. Use when building a LÖVE 11.x game (main.lua, conf.lua, .love).
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 /test to write a test suite for code you just built or changed, after implementing a feature, route, or fix. Targets uncommitted changes automatically, reads test preferences.json for your framework (asks and saves it if absent), and picks the right strategy per file: happy path, edge cases, error states, accessibility.
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Use when asked to check whether a figure is audited, which headline numbers sit outside the audited accounts, what 'adjusted' or 'underlying' means in this report, or who is vouching for the numbers in the highlights. For every headline figure in the highlights, chairman's statement and CEO review of an uploaded annual report, states whether it sits inside or outside the audited statements and what was added or removed versus the nearest audited number. Produces a boundary table, the list of figures outside the boundary, and a plain-English summary of who is vouching for what. Educational, not financial advice.
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。
Read entity states and call services on your Home Assistant instance. Service calls are confirmed first. Trigger phrases: home assistant, smart home, turn on the light.
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 designing, changing, or reviewing user-facing interfaces or interaction flows, including API changes that alter visible states or recovery. Backend-only work without user-facing effects stays on its existing route.
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。
Saves user instructions as persistent learnings for future sessions. Use when the user says 'remember this', 'always do X', 'from now on', 'never do Y', or gives any instruction they want persisted across sessions. Proactively suggest when the user states a preference, convention, or rule they clearly want followed in the future.
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。
Anti-slop skill for PRODUCT UI - dashboards, data tables, forms, multi-step flows, settings, list/detail, app shells. The agent reads the surface, budgets the frame first, and ships dense interfaces that are correct at every edge case (overflow, long labels, empty/error/loading states, i18n expansion, keyboard/a11y). House-system-first; maps to Carbon/Polaris/Atlaskit/Fluent/Primer/Material3/Radix-shadcn/Ant. The counterpart to taste-skill, which owns landing/portfolio/marketing.
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 an AEJ: Economic Policy manuscript needs a framework that maps reduced-form estimates into a welfare, cost-benefit, or distributional policy object — sufficient statistics, MVPF, optimal-policy, or a small applied model. Builds the estimate-to-welfare bridge and states its assumptions; it does not run the empirical estimation or write the prose.
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。
The intake stage of a Meta ad harness. Researches the brand first, states what it assumes, and asks only what it cannot infer and what would change money, claims or legal exposure if guessed wrong — at most four questions a turn, in plain business language. Writes the brand's stable facts (ads/brand.md), the campaign brief, and a log of what was asked vs assumed. Use it when someone wants to run Meta ads and the brand's ad facts are not yet recorded.
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Build Sankey, alluvial, river, and CONSORT-style flow diagrams to visualize cohort transitions, cell-state changes, or pipeline filtering using ggalluvial, networkD3, plotly, and consort. Use when showing how entities move between categories across timepoints (cell states, drug response classes, patient flow through a trial) or filtering pipelines (variants filtered through QC stages).
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。
Reconstruct ancestral states at internal phylogenetic nodes for sequences (PAML codeml, IQ-TREE --ancestral, GRASP, FastML), discrete traits (corHMM hidden-rate Markov, ape::ace, phytools::make.simmap stochastic mapping, BayesTraits), and continuous traits (phytools::fastAnc, geiger Brownian/OU, RPANDA). Use when designing constructs for ancestral protein resurrection, tracing trait evolution along a tree, performing stochastic character mapping, testing models of trait evolution (BM vs OU vs EB), inferring ancestral genome content via Dollo or DTL reconciliation, or quantifying ancestral-state uncertainty for downstream comparative analyses.
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Detect somatic and germline copy number variants from targeted, exome, and whole-genome sequencing with CNVkit, a read-depth caller that combines on-target and off-target (antitarget) coverage. Covers panel-of-normals construction, flat-reference tumor-only calling, hybrid/amplicon/WGS modes, CBS vs HMM segmentation selection, purity-aware integer calling, and reconciliation against GATK and allele-specific callers. Use when calling CNVs from hybrid-capture panels or exomes, deciding whether CNVkit (depth-only) is the right tool versus an allele-specific caller, building a panel of normals, diagnosing flat-reference false positives, or interpreting log2 ratios into copy-number states.
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。
Resolve subclonal copy number, whole-genome doubling, and copy-number tumor evolution from bulk sequencing with Battenberg, TITAN, and MEDICC2. Covers clonal versus subclonal copy-number states, haplotype phasing for subclonal resolution, cancer cell fraction, whole-genome-doubling detection and timing relative to mutations, mirrored subclonal allelic imbalance, and copy-number phylogenies. Use when a tumor is heterogeneous and bulk data shows non-integer copy number, when calling subclonal CNAs, detecting or timing whole-genome doubling, reconstructing copy-number evolution, or deciding between Battenberg and TITAN.
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Detects differential alternative splicing between conditions using rMATS-turbo (binomial LRT on junction counts), leafcutter (Dirichlet-multinomial GLM on intron clusters), MAJIQ V3 deltapsi/HET (Bayesian posterior on LSVs), SUPPA2 (empirical-null on TPM-derived PSI), or Shiba (junction-imbalance-corrected, 2025 SOTA at low coverage). Reports FDR-corrected significance and delta PSI effect sizes. Tools differ in statistical model, annotation dependence, calibration regime, and replicate-count requirements. Use when comparing splicing patterns between treatment groups, tissues, or disease states.
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Sequence-based deep learning for ATAC-seq using chromBPNet, BPNet, scBasset, or Enformer. Use when correcting Tn5 bias with neural networks beyond k-mer models, predicting per-base accessibility profiles, scoring in silico variant effects at GWAS or rare-variant SNPs, discovering motifs via DeepLIFT/TF-MoDISco from a trained model, or generating cell-type-specific accessibility predictions for unobserved cell states.
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 mentions UX flow issues, dead-end views, dismiss traps, missing empty states, broken user journeys, or wants a UX audit of their iOS app.
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 and reviews SwiftUI views with modern MV architecture, state, composition, isolated previews, and migration guidance. Covers @Observable ownership, @State/@Bindable/@Environment wiring, view decomposition, ViewModifiers, environment values, .task loading, iOS 26+ handoffs, Writing Tools, clipboard availability, and performance. Use when structuring SwiftUI state, managing @Observable, composing views, previewing meaningful UI states, or correcting SwiftUI patterns.
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。
Guide for implementing smooth, native-feeling animations using React's View Transition API (`<ViewTransition>` component, `addTransitionType`, and CSS view transition pseudo-elements). Use this skill whenever the user wants to add page transitions, animate route changes, create shared element animations, animate enter/exit of components, animate list reorder, implement directional (forward/back) navigation animations, or integrate view transitions in Next.js. Also use when the user mentions view transitions, `startViewTransition`, `ViewTransition`, transition types, or asks about animating between UI states in React without third-party animation libraries.
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。
Exhaustively extract UX patterns from a reference web app. Walks every screen, captures screenshots of every state, records interaction patterns, copy verbatim, keyboard shortcuts, responsive treatments, motion, and empty/error/loading states. Produces a reusable pattern library that other audits can compare against. The inverse of ux-audit — asks 'what is the bar?' rather than 'does this match the bar?'. Trigger with 'learn from X', 'extract patterns from X', 'study X's UX', 'reverse engineer the UX of X', 'build a pattern library from X'.
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。