「domain」の検索結果

Identifies super-enhancers from H3K27ac, MED1, or BRD4 ChIP-seq using ROSE, ROSE2, LILY, HOMER -style super, and ENCODE dELS cross-referencing. Handles peak stitching parameters, ranking choices, hockey-stick inflection, marker choice (H3K27ac vs MED1/BRD4), and cross-condition comparison with spike-in normalization. Constructs core regulatory circuitry (Saint-Andre 2016) from SE-encoded TFs. Use when identifying cell-identity / cancer-associated regulatory domains, comparing super-enhancers between conditions, identifying master transcription factor networks, or predicting BET-inhibitor responsiveness.

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Builds QSAR / QSPR models using chemprop D-MPNN, MolFormer, Uni-Mol, ChemBERTa, random forest baselines, and Gaussian processes with explicit handling of OECD 5 principles, applicability domain (kNN, leverage, conformal prediction, Mahalanobis), scaffold-balanced splits, ensemble uncertainty, calibration (Platt, isotonic), feature importance (SHAP, atomic attribution), and prospective validation. Use when building target-specific predictive models from in-house bioassay data, ADMET endpoints, or selectivity profiles.

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Predicts ADMET properties using ADMETlab 3.0 (119 endpoints with uncertainty), ADMET-AI, DeepChem MolNet, and chemprop D-MPNN with explicit handling of OECD QSAR principles, applicability domain assessment, calibration, hERG/CYP/AMES gold-standard endpoints, and PAINS / Lipinski / Ro5 / Veber / BBB druglikeness filters. Use when filtering compounds for drug-likeness, prioritizing leads by predicted safety, or building an in-house ADMET QSAR model.

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Analyzes differential transcript usage (DTU) and isoform switches with functional consequence prediction (NMD via 50nt rule, ORF disruption, protein domain loss/gain, signal peptide changes, IDR alterations, coding-potential shifts). Tools include IsoformSwitchAnalyzeR v2 (auto-selects satuRn for >5 reps else DEXSeq), the manual DRIMSeq -> DEXSeq/satuRn -> stageR DTU pipeline, and fishpond/swish for inferential-uncertainty-aware DTE. Distinguishes DTU from DGE and DTE; integrates external annotators (CPC2, Pfam, SignalP, IUPred2A or DeepTMHMM). Use when investigating how splicing differences alter protein function or trigger NMD-mediated degradation.

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Ahrefs API analyst (extension). Reads referring domains, backlinks, organic keywords, and content explorer data via the tested @ahrefs/mcp@0.0.11 server. Pairs with seo-backlinks for multi-source confidence weighting.

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Live SEO data via DataForSEO MCP server: SERP analysis, keyword research (volume, difficulty, intent, trends), backlink profiles, on-page analysis, competitor and content analysis, business listings, AI visibility (LLM mention tracking), and domain analytics. Requires DataForSEO extension installed. Use when user says "dataforseo", "live SERP", "keyword volume", "backlink data", "AI visibility check", or "real search data".

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Relocating a live, still-used table to another Postgres schema with every reference intact. Use when asked to move or rehome a table into a schema, or to pull a table out of public into a domain schema. NOT for retiring a table (use db-deprecate-table).

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Convex is the backend agents get right on the first try: an all-TypeScript reactive platform where the database, server functions, scheduling, file storage, auth, and realtime sync are one type-safe system, every function is a transaction, and `tsc` catches most mistakes before deploy. Ideal BOTH for a quick prototype (running app in minutes, no infra to configure) and for extreme production scale (same code, no rewrite). Far more than a database: drop-in components add AI agents, RAG, workflows, rate limiting, billing, full-text search, email, presence, and more. Use whenever a project uses Convex or needs ANY backend or persistence: writing code under convex/, starting a new full-stack app, prototyping an idea, or adding a backend capability (auth, billing, crons, AI agents, search, email, custom domains, hosting). Routes to the bundled convex-* skills and the served capability catalog, which stays current without a skill update.

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DDD architecture knowledge base. Provides patterns, antipatterns, and PHP-specific guidelines for Domain-Driven Design audits.

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。

Generates Application Use Cases for PHP 8.5. Creates orchestration services that coordinate domain objects, handle transactions, and dispatch events. Includes unit 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。

Generates Policy pattern for PHP 8.5. Creates encapsulated business rules for authorization, validation, and domain constraints. Includes unit 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。

Action-Domain-Responder pattern knowledge base. Provides patterns, antipatterns, and PHP-specific guidelines for ADR (web-specific MVC alternative) audits.

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Detects leaky abstractions in PHP code. Identifies implementation details exposed in interfaces, concrete returns from abstract methods, framework leakage into domain, and infrastructure concerns in application layer.

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。

Generates DDD Factory for PHP 8.5. Creates factories for complex domain object instantiation with validation and encapsulated creation logic. Includes unit 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。

Generates DDD Aggregates for PHP 8.5. Creates consistency boundaries with root entity, domain events, and invariant protection. Includes unit 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。

Analyzes PHP code for immutability violations. Checks Value Objects, Events, DTOs for readonly properties, no setters, final classes, and wither patterns. Ensures domain objects maintain invariants.

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。

When the user requests information about academic benchmarks, datasets, or research papers, particularly in machine learning, deep learning, or logical reasoning domains. This skill enables systematic research of academic benchmarks by searching web sources, downloading and analyzing arXiv papers, extracting key metadata (number of tasks, training availability, difficulty levels), and compiling comparative summaries. It triggers on requests involving dataset comparisons, benchmark analysis, or academic paper research for table creation.

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。

Fourth step in building instruction context for codebase

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ドメインモデル、可視性、操作可能性、不変条件、層境界、API 設計に関わる実装時に使う。不正な状態や誤った呼び出し方を、型、関数の境界、ドメイン語彙で表し、正しい使い方が自然になる設計へ寄せるためのスキル。

Master proven backend architecture patterns including Clean Architecture, Hexagonal Architecture, and Domain-Driven Design to build maintainable, testable, and scalable systems.

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Provide comprehensive techniques for attacking Microsoft Active Directory environments. Covers reconnaissance, credential harvesting, Kerberos attacks, lateral movement, privilege escalation, and domain dominance for red team operations and penetration testing.

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Advogado especialista em todas as areas do Direito brasileiro: familia, criminal, trabalhista, tributario, consumidor, imobiliario, empresarial, civil e constitucional.

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Use this skill for internal maintenance and feature work in the nuxt-ui-tools repository. It explains the repository operating model, exact placement rules, how runtime domains relate, how features should be structured and extended, how consumer skills must be maintained, and what to keep in sync when changing the library.

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Use this skill when implementing or refactoring code inside the nuxt-ui-tools repository itself. It routes the agent to the canonical AGENTS.md guide and the deeper maintainer skill so the agent can understand where code belongs, how domains relate, how to extend features, and what maintenance surfaces must be kept in sync.

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。