「sem」の検索結果

Transfers gene annotations between genome assemblies via coordinate liftover (UCSC liftOver, CrossMap for same-species version updates) or feature/sequence projection (Liftoff for same/close species, miniprot for protein-level cross-species, TOGA/GeMoMa/CAT for distant clades). Covers the coordinate-vs-projection decision by divergence, why a successful lift is not biological confirmation, reference bias, the silent-dropping of unmapped features, build/PAR/MHC/inversion hazards, and transfer-vs-de-novo validation. Use when annotating a new assembly of a species with an existing reference, harmonizing coordinates across builds, or mapping annotations across related species.

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。

Pull pre-computed ortholog calls from public databases (OrthoDB, Ensembl Compara, OMA browser, eggNOG, PANTHER, KEGG Orthology, HomoloGene) via their REST APIs. Use when orthologs are already curated upstream, when the question is "what is the X ortholog of Y" rather than "how to infer orthology de novo", when batch-mapping gene IDs across species, or when comparing the resources for consensus calls. Encodes confidence-level semantics, 1:1 vs 1:many vs many:many, HomoloGene deprecation, and when to defect to de novo computation.

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。

Build and analyze pangenomes for prokaryotes (Panaroo, PPanGGOLiN, PEPPAN, GET_HOMOLOGUES, anvi'o pangenomics) and eukaryotes (Minigraph-Cactus, PGGB, vg pangenome graphs). Implement Tettelin core/accessory/cloud genome decomposition (Tettelin 2005), Heap's law open/closed pangenome modeling, gene presence/absence GWAS (Scoary, pyseer), pangenome graph variant calling (vg, PanGenie), and structural-variation graph indexing. Use when assembling species- or genus-level pan-gene catalogs, separating core from accessory/shell/cloud genes, testing gene-content associations with phenotypes, building pangenome graphs from haplotype-resolved assemblies, calling SVs from pangenome graphs, or selecting between bacterial-pangenome and eukaryotic-pangenome workflows.

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 when the user asks about passenger-carrying CMVs — buses, motorcoaches, charter operations, school buses, limousines, shuttle services. Covers different HOS rules (10-hour driving / 15-hour on-duty / no 34-restart), Class C CDL with P endorsement, S endorsement for school bus, FMCSA Hours-of-Service for passenger vehicles, $5M insurance minimum, BMC-32 filing, accessibility requirements, and how passenger ops differ from property. Cite 49 CFR 395.5 + 387.33.

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 when the user asks about CDL classes (A, B, C), endorsements (H, N, P, S, T, X), required for hazmat / passenger / school bus / tank / triple-trailer operations, what each class is permitted to operate, the TSA Security Threat Assessment for hazmat, and ELDT (Entry-Level Driver Training) requirements. Cite 49 CFR 383.

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。

SERP-based semantic topic clustering for content architecture planning. Groups keywords by actual Google SERP overlap (not text similarity), designs hub-and-spoke content clusters with internal link matrices, and generates interactive visualizations. Optionally executes content creation if claude-blog is installed. Use when user says "topic cluster", "content cluster", "semantic clustering", "pillar page", "hub and spoke", "content architecture", "keyword grouping", or "cluster plan".

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。

自分のClaude Codeログから3回以上繰り返している作業を洗い出し、実用できるエージェントスキルを生成する。「定期作業洗い出し」「ログから繰り返し作業」「スキル化候補出して」「この作業をスキル化して」と言われたら起動。2モード搭載 (keyword=高速30秒・無料 / semantic=並列サブエージェント・3-5分・$1〜$3)。出力は候補リスト、生成SKILL.md、実インストールはユーザー承認後

リリース単位（単一パッケージか monorepo か）を判定し，SemVer でバージョンを上げ（マニフェストと全参照箇所を整合），bump コミット・タグ・push・About 欄・GitHub Release までの文面を提案．承認後に一気に実行する．「リリースして」「公開して」「バージョンを上げて」など版を公にすることを求められたときに使う．記録するだけなら /capstone:commit を使う．

Substackの編集画面（Tiptap/ProseMirror）で、指摘済みの誤字脱字などの修正リストをスクリーンショット操作なしで高速に反映するスキル。jp-checkの校正結果をSubstack下書きに適用するときなどに使う。

Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG systems, semantic search engines, or intelligent knowledge bases.

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。

Supermemory is a state-of-the-art memory and context infrastructure for AI agents. Use this skill when building applications that need persistent memory, user personalization, long-term context retention, or semantic search across knowledge bases. It provides Memory API for learned user context, User Profiles for static/dynamic facts, and RAG for semantic search. Perfect for chatbots, assistants, and knowledge-intensive applications.

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。

Power BI semantic modeling assistant for building optimized data models. Use when working with Power BI semantic models, creating measures, designing star schemas, configuring relationships, implementing RLS, or optimizing model performance. Triggers on queries about DAX calculations, table relationships, dimension/fact table design, naming conventions, model documentation, cardinality, cross-filter direction, calculation groups, and data model best practices. Always connects to the active model first using power-bi-modeling MCP tools to understand the data structure before providing guidance.

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。

End-to-end skill for building, testing, linting, versioning, and publishing a production-grade Python library to PyPI. Covers all four build backends (setuptools+setuptools_scm, hatchling, flit, poetry), PEP 440 versioning, semantic versioning, dynamic git-tag versioning, OOP/SOLID design, type hints (PEP 484/526/544/561), Trusted Publishing (OIDC), and the full PyPA packaging flow. Use for: creating Python packages, pip-installable SDKs, CLI tools, framework plugins, pyproject.toml setup, py.typed, setuptools_scm, semver, mypy, pre-commit, GitHub Actions CI/CD, or PyPI publishing.

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。

High-performance vector similarity search engine for RAG and semantic search. Use when building production RAG systems requiring fast nearest neighbor search, hybrid search with filtering, or scalable vector storage with Rust-powered performance.

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。

Open-source embedding database for AI applications. Store embeddings and metadata, perform vector and full-text search, filter by metadata. Simple 4-function API. Scales from notebooks to production clusters. Use for semantic search, RAG applications, or document retrieval. Best for local development and open-source projects.

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。

Build or refine UI color systems, warmth, semantic color, and color-based hierarchy in designs that are too gray, monochromatic, or missing color meaning. Use when the user mentions dull colors, gray UI, missing warmth, weak semantic color, or a need for stronger color hierarchy.

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 vector databases, embedding strategies, and semantic search implementation. Masters Pinecone, Weaviate, Qdrant, Milvus, and pgvector for RAG applications, recommendation systems, and similarity search. Use PROACTIVELY for vector search implementation, embedding optimization, or semantic retrieval 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。

Test Data Builder Pattern complete implementation guide. Used when using builder pattern to create maintainable test data or simplify complex object test preparation. Covers fluent interface, semantic methods, default value design, and Builder composition patterns. Keywords: test data builder, builder pattern test, test data builder, object mother, fluent interface, fluent interface, UserBuilder, ProductBuilder, .With(), .Build(), AUser(), test data preparation, complex object creation, semantic testing

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。

Complete guide for upgrading xUnit 2.9.x to 3.x. Use when upgrading xUnit v2 to v3 or understanding xUnit v3 new features and breaking changes. Covers package updates, async void fixes, IAsyncLifetime adjustments. Includes new features: Assert.Skip, Explicit Tests, Matrix Theory, Assembly Fixtures. Keywords: xunit upgrade, xunit v3, xunit 3.x, xunit migration, xunit upgrade, xunit.v3, OutputType Exe, IAsyncLifetime v3, Assert.Skip, SkipUnless, SkipWhen, Explicit attribute, MatrixTheoryData, AssemblyFixture, breaking changes

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。

Integrates AI/LLM via Semantic Kernel. Plugins, prompt templates, memory stores, agents.

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。

Identifies and exploits IPv6-specific vulnerabilities including SLAAC spoofing, Router Advertisement flooding, and IPv6 tunneling during authorized assessments to test dual-stack security controls and IPv6-aware network defenses.

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。

Sets up vector databases for semantic search including Pinecone, Chroma, pgvector, and Qdrant with embedding generation and similarity search. Use when users request "vector database", "semantic search", "embeddings storage", "Pinecone setup", or "similarity search".

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。

Automates releases and package publishing with changesets or semantic-release. Handles versioning, changelog generation, git tags, and release notes. Use for "release automation", "semantic versioning", "package publishing", or "changelog generation".

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 semantic commit messages following the Conventional Commits specification with proper types, scopes, breaking changes, and footers. Use when users request "write commit message", "conventional commit", "semantic commit", or "format commit".

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。