「variants」の検索結果

Class Variance Authority - type-safe component variant styling with cva(), compound variants, and VariantProps

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。

Motion (formerly Framer Motion) patterns - motion components, variants, AnimatePresence, gestures, layout animations, scroll-linked motion, reduced-motion handling

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。

MSW handlers, browser/server workers, test data. Use when setting up API mocking for development or testing, creating mock handlers with variants, or sharing mocks between browser and Node environments.

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。

Unistyles 3.0 styling - C++ powered StyleSheet superset with zero re-renders, theming, breakpoints, variants, dynamic functions, runtime values

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Computes cryptographic and fuzzy fingerprints for malware samples: MD5/SHA-1/SHA-256 for exact identity, plus import hashing (imphash), section hashing, and fuzzy hashes (ssdeep, TLSH) to cluster related variants. Activates for requests to hash a sample, compute imphash, or fingerprint files for similarity clustering.

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Diagnose and fix slow pyfixest regression GRIDS (many feols/fepois calls run sequentially) that stay slow despite demeaner_backend="cupy64" and an idle GPU. Use when: (1) a script looping dozens of pf.feols models on a 100k+ row panel takes ~1 min/model, (2) process inspection shows ~1-1.5 cores busy and nvidia-smi shows ~0% GPU utilization with a resident cupy context, (3) planning any worker prompt that will run a model grid (robustness variants x FE structures x domains). Root cause: per-model CPU-side single-threaded fixed costs (formulaic model-matrix build, interaction construction, singleton detection, cluster vcov) dominate wall time; GPU demeaning is a small slice. Fix: shard the model grid across OS processes and/or use pyfixest multiple-estimation syntax; mandate this IN THE WORKER PROMPT.

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Use when behavior-preserving frontend refactoring focuses on conditional complexity, repeated branches, guards, special cases, or internal invariants, and the selected technique needs family-specific mechanics, risks, and JavaScript/TypeScript/React/Vue adaptation.

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OpenUI generative UI with Python FastAPI backend. OpenAI and Anthropic SDK variants.

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Smart contract and Web3 security audit skill for Solidity, Vyper, Move, and Cairo. Covers reentrancy, access control, oracle manipulation, MEV/sandwich exposure, signature replay, upgrade safety, ERC token edge cases, bridge risk, and DeFi invariants. Tooling includes Slither, Mythril, Echidna, Foundry, Halmo, Certora, and custom invariant tests. Use for design review, code audit, and incident analysis on contracts you own or are authorized to assess.

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Use when a task needs the judgment of a Bioinformatics Scientist — designing a differential-expression or GWAS analysis with correct multiple-testing correction, calling and triaging variants from NGS data using genotype-likelihood confidence rather than raw allele counts, interpreting a BLAST/DIAMOND homology search, choosing or building a genomics pipeline (aligner, caller, normalization method) for a specific data-generating process, or judging whether a computational-biology analysis's statistical design actually supports the biological claim being made. Distinct from a bioinformatics-technician (executes and QC-gates an established pipeline someone else designed) and a data-scientist (general product/experimentation judgment, not genomics-specific statistics) — this role owns which method and statistical model a genomics question requires, and why.

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Use ENCODE functional genomics data for disease mechanism research. Use when the user wants to connect GWAS variants to regulatory elements, annotate disease-associated loci with functional data, identify therapeutic targets from epigenomic data, build disease regulatory models, cross-reference with clinical trials and drug databases, or conduct any disease-focused, pathology-driven, or clinical variant interpretation workflow. Covers the full pipeline from disease-tissue mapping through GWAS variant annotation, heritability enrichment, cancer epigenomics, drug target identification, and clinical trial cross-referencing. Integrates ENCODE with Open Targets, PubMed, ClinicalTrials.gov, and bioRxiv.

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Query the Ensembl REST API for regulatory feature annotations, variant effect prediction (VEP), coordinate liftover, gene lookups, and cross-references. Use when the user needs to annotate variants with VEP (consequence, CADD, REVEL, SpliceAI), check Ensembl Regulatory Build overlap for ENCODE regions, convert coordinates between GRCh37 and GRCh38, resolve gene IDs (Ensembl ↔ symbol ↔ RefSeq), look up gene phenotype associations, or cross-reference ENCODE targets with Ensembl annotations. Also use when the user mentions Ensembl, VEP, variant effect predictor, liftover, assembly conversion, regulatory build, gene lookup, or cross-references between databases.

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Animate React with Framer Motion (Motion): gestures, layout animations, variants, scroll-linked motion, and exit transitions. Use when adding production-grade UI animation.

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。

Encode and decode Base64 correctly with URL-safe variants, padding rules, and binary handling.

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Inspect Figma designs and generate implementation context for screens and interfaces. Use to read component variants, spacing and design tokens, inspect layouts and design-system libraries, and extract image assets for implementing a design through Figma's official MCP server.

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Generate MLS-compliant property listing copy in five length variants (tagline, MLS-short ≤250 char, MLS-long ≤4000 char, marketing description ~600-800 words, social caption) PLUS schema.org RealEstateListing JSON-LD for SEO/AEO syndication..

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Design and document a marketing growth experiment. Use when the user says "run a growth experiment", "test this channel", "growth test", "I want to experiment with [channel]", "traction experiment", "acquisition experiment", "let's test if [channel] works for us", "bullseye framework", "which channels should we try", "run a traction test", or wants to systematically test a marketing or acquisition channel before committing budget. For page-level conversion hypotheses, see page-cro. For copy variants to test, see ab-copy-writer. For recording the result, see retro.

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Repurpose blog posts for social media, email, video, podcast, and community channels. Generates Twitter/X threads, LinkedIn posts and articles, Threads, Bluesky, TikTok, Instagram, YouTube Shorts and long-form scripts, Reddit, newsletter, podcast, Discord, and Slack variants. Adapts tone for each platform. Use when user says "repurpose", "blog repurpose", "share blog", "social media", "twitter thread", "linkedin post", "youtube script", "reddit post".

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Generate rights-cleared paid-ad product photography variants from an authorized source image and validated brand profile. Triggers on: product photo, product photography, virtual photoshoot, photoshoot, enhance product image, studio shot, lifestyle shot, lifestyle product image, floating product image, ingredient shot.

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Run a Meta-Harness-style optimization loop NATIVELY — automatically search over the scaffolding around a FIXED base model (memory, retrieval, context construction, prompt templates, summarization, tool-selection logic) by proposing candidate variants, scoring each on a cheap deterministic eval, and keeping a Pareto frontier of quality vs cost — using native Agent / Workflow / loop tools instead of a standalone Python harness. Use this whenever the user wants to optimize, evolve, tune, distill, or search over a harness, scaffold, prompt system, memory or retrieval policy, context-assembly code, or summarizer while keeping the model fixed; whenever they mention Meta-Harness, harness optimization, scaffold evolution, automatic prompt/memory optimization, an evolutionary or Pareto search over candidate implementations, or "make the harness/agent better without retraining"; and whenever the gain must come from the code AROUND the model rather than the model weights. Reproduces the Meta-Harness paper's method natively, with no claude_wrapper.py and no metered solver API.

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Use when setting up CMS workspaces, creating or publishing content types for Experience Cloud sites, managing content scheduling, or configuring audience targeting on content variants. Triggers: 'set up CMS workspace', 'publish content to Experience Cloud site', 'content scheduling community', 'audience targeting content', 'managed content vs unmanaged', 'custom content type metadata', 'CMS channel configuration'. NOT for moderating member-posted content — use admin/experience-cloud-moderation. NOT for Knowledge vs an external CMS or CMS Connect — use architect/knowledge-vs-external-cms.

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Use this skill to configure a Salesforce B2B Commerce storefront: creating the WebStore, linking BuyerGroups, assigning CommerceEntitlementPolicies, and granting buyer contacts transactional access. Trigger keywords: B2B Commerce, WebStore, BuyerGroup, entitlement policy, buyer account, storefront access. NOT for building the product catalog, categories, or variants — use admin/commerce-product-catalog. NOT for a B2C Commerce (SFCC) storefront — use admin/b2c-commerce-store-setup.

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Understand what SKMTC is, how its engine works, and the architectural invariants — for agents building or extending infrastructure *around* SKMTC rather than authoring generators or running the CLI. Covers the three-phase pipeline, the host/Worker boundary, cross-generator coordination, the manifest, the attribution / gen-maps (provenance) subsystem, the package graph, the dependency substrate, and the design decisions that make SKMTC behave unlike a typical codegen tool. Use this skill when the user asks "what is SKMTC", "how does the SKMTC engine work", "explain the SKMTC architecture", or is building platform infrastructure around SKMTC — a hosted generate API, a schema or generator registry, tracing or provenance tooling, a web app or SaaS that wraps the engine, or platform-level CI integration. This skill is the system mental model. It does NOT cover authoring generators (→ skmtc-generator), running CLI commands (→ skmtc-cli), or diagnosing broken runs (→ skmtc-debug).

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 make something faster, try many variants, run recursive optimization, benchmark latency/throughput/cost, or choose the best implementation by repeated measured tests.

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。