Design database schemas — tables, relationships, indexes, constraints, and ORM setup. Covers relational design, normalization, and common patterns.
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Design database schemas — tables, relationships, indexes, constraints, and ORM setup. Covers relational design, normalization, and common 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。
Detect and auto-install missing ToolUniverse research skills. Checks common Claude Code/Cursor/Codex skill directories for the canary file, and installs any missing skills if none found. Use when the plugin's research skills aren't loading, when migrating between clients, or when verifying a skill installation.
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。
Authors portable, strongly-typed bioinformatics pipelines in the Common Workflow Language (CWL v1.2) as CommandLineTool/Workflow/ExpressionTool documents, validated with cwltool and run at scale on Toil/Arvados/Calrissian. Use when deciding CWL (portability/provenance/regulated) vs Nextflow/WDL/Snakemake; declaring secondaryFiles for indexed companions (.bai/.fai/.dict/.tbi and the caret rule); putting resources/containers under requirements (must-hold) vs hints (advisory) to avoid silent OOM; choosing scatterMethod (dotproduct vs flat_/nested_crossproduct); preferring $(...) parameter refs over ${...} JavaScript for portability; pinning DockerRequirement images; or emitting a CWLProv provenance object for audited/clinical settings.
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End-to-end post-GWAS causal inference pipeline orchestrating heritability partitioning, genetic correlation, Mendelian randomization with CHP-aware sensitivity (CAUSE / LHC-MR), colocalization, fine-mapping with SuSiE / FOCUS, mediation, TWAS triangulation, cis-pQTL drug-target MR, effector-gene prioritization (L2G / PoPS / cS2G), and GenomicSEM common-factor GWAS. Use when triangulating causal inference across multiple complementary methods, prioritizing tissues via stratified LDSC, nominating or de-risking drug targets, mapping a lead SNP to a candidate effector gene, modeling shared genetic architecture across correlated traits, or producing a STROBE-MR-compliant publication-grade evidence battery from GWAS summary statistics.
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。
Therapeutics Data Commons. AI-ready drug discovery datasets (ADME, toxicity, DTI), benchmarks, scaffold splits, molecular oracles, for therapeutic ML and pharmacological prediction.
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Designs covalent inhibitors and warheads targeting cysteine (most common, 98% of covalent drugs), lysine, serine, threonine, tyrosine, and aspartate residues, with explicit handling of warhead reactivity (acrylamide, chloroacetamide, vinyl sulfone, sulfonyl fluoride, fluorosulfate, aldehyde, boronate, nitrile), reversibility (kinact/Ki, t_residence), glutathione (GSH) stability, intrinsic reactivity assays, and covalent docking (DOCKovalent, GOLD, HCovDock). Use when designing covalent inhibitors for targeted covalent inhibition (TCI), KRAS G12C-style approaches, or rationalizing covalent SAR.
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Simulate the SCF team's manual prescreen on a Build Award submission — a completeness and eligibility check applied to every full submission before panel review. Checks completeness, Stellar integration, eligibility, budget issues, and common disqualifiers. Use when reviewing a draft Build Award submission for readiness before it goes to the SCF round.
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 common EntitySystem helpers, prototype APIs, spawning, deletion, audio, popup, and random 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。
Protect your SaaS app from common vulnerabilities. Use when building auth, handling user data, or deploying features. Covers authentication, data protection, API security, and OWASP Top 10 for non-technical founders using AI tools.
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。
SSL/БСП subsystems guidance and search via 1c-mcp_ssl_server MCP. Use when working with standard library subsystems - users, files, print forms, background jobs, email, common utilities.
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Build publication-quality figures with matplotlib using the object-oriented Figure/Axes API, constrained_layout, rcParams customization, TrueType (Type-42) font embedding for journal submission, and CVD-safe palettes. Covers seaborn integration, common chart types, axis formatting, and the small gotchas that distinguish reproducible matplotlib from notebook scratch. Use when producing publication figures in Python — RNA-seq scatter, single-cell embeddings, generic biological plotting.
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 covalent inhibitors and warheads targeting cysteine (most common, 98% of covalent drugs), lysine, serine, threonine, tyrosine, and aspartate residues, with explicit handling of warhead reactivity (acrylamide, chloroacetamide, vinyl sulfone, sulfonyl fluoride, fluorosulfate, aldehyde, boronate, nitrile), reversibility (kinact/Ki, t_residence), glutathione (GSH) stability, intrinsic reactivity assays, and covalent docking (DOCKovalent, GOLD, HCovDock). Use when designing covalent inhibitors for targeted covalent inhibition (TCI), KRAS G12C-style approaches, or rationalizing covalent SAR.
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 @Transactional, multi-step database operations, distributed transactions, or any code that needs atomicity guarantees. Covers propagation rules, isolation levels, read-only optimization, and common pitfalls.
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Build robust, production-grade backtesting systems that avoid common pitfalls and produce reliable strategy performance estimates.
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Implement secure API design patterns including authentication, authorization, input validation, rate limiting, and protection against common API vulnerabilities
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。
Behavioral guidelines to reduce common LLM coding mistakes. Use when writing, reviewing, or refactoring code to avoid overcomplication, make surgical changes, surface assumptions, and define verifiable success criteria.
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。
Single Source of Truth (SSOT) guidelines for SwiftUI state management. Use when designing state architecture, choosing between @State/@Binding/@StateObject/@ObservedObject/@EnvironmentObject, fixing state synchronization issues, refactoring duplicated state, or reviewing state management in PRs. Covers SSOT principles, property wrapper selection, state lifting, and common violations.
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。
Android data layer implementation guide. Provides comprehensive patterns for Room Database (Entity, DAO, Migration), DataStore (Preferences), Repository layer design, and common pitfalls. Use when implementing local data storage, cache strategies, or data source integration in Android projects.
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Build beautiful, accessible UI components using shadcn/ui library with Radix UI primitives and Tailwind CSS styling that you own and can customize. Use when creating accessible component libraries, implementing common UI patterns (dialogs, dropdowns, tooltips), building forms with validation, creating data tables, implementing command palettes, using Radix UI primitives, customizing components with Tailwind, or building design systems with full ownership of code.
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。
Diagnose and fix common AEM 6.5 LTS replication issues including blocked queues, connectivity failures, and content distribution problems
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。
Preview and verify imported content in local AEM Edge Delivery Services dev server. Validates rendering, compares with original page, and troubleshoots common issues.
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。
Teaches the AI to design like a high-end agency. Defines the exact fonts, spacing, shadows, card structures, and animations that make a website feel expensive. Blocks all the common defaults that make AI designs look cheap or generic.
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Identifies and fixes common Rust anti-patterns. Use during code review, when seeing excessive clones/unwraps/index loops, or when code fights the borrow checker instead of working with it.
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。
Behavioral guidelines to reduce common LLM coding mistakes. Use when writing, reviewing, or refactoring code to avoid overcomplication, make surgical changes, surface assumptions, and define verifiable success criteria.
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。