87 件 ・ 関連度順
概要と使いどころ
UMAP dimensionality reduction. Fast nonlinear manifold learning for 2D/3D visualization, clustering preprocessing (HDBSCAN), supervised/parametric UMAP, for high-dimensional data.
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Query myvariant.info API for aggregated variant annotations from multiple databases (ClinVar, gnomAD, dbSNP, COSMIC, etc.) in a single request. Use when annotating variants with clinical and population data from multiple sources simultaneously.
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。
Define custom distance/similarity metrics for clustering and ML algorithms. Use when working with DBSCAN, sklearn, or scipy distance functions with application-specific metrics.
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。
视频标题生成与落盘工作流。Use when the user asks to title a finished or planned video, especially 抖音/小红书视频标题, 视频标题.md, 成片同级目录, 对标标题共性, 两段式标题, 基于视频主题起标题, 洗稿后标题, or replacing generic dbs-xhs-title for video titles. It derives the real video theme first, studies benchmark title patterns when provided, generates 8-12 theme-first two-part candidates, recommends/selects one, and writes 视频标题.md next to the final MP4 when requested.
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。
Search NCBI databases using Biopython Bio.Entrez (ESearch, EInfo, EGQuery, ESpell). Use when finding records by keyword, building reproducible field-qualified queries, navigating the Entrez Query Translator, exploiting the history server for large result sets, handling retmax caps, or interpreting weekly index lag. Covers PubMed, Nucleotide, Protein, Gene, SRA, GEO, Assembly, Taxonomy, ClinVar, dbSNP.
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。
Extracts and assigns COSMIC v3.4 mutational signatures (86 SBS / 11 DBS / 18 ID / 21 CN / 16 SV) from somatic VCFs using SigProfilerSuite, MutationalPatterns, MuSiCal mvNMF, SigNet, or HRDetect. Use when characterizing DNA-damage etiology (BRCA1/2 HRD, MMR-D, POLE, APOBEC3A, UV, tobacco, aflatoxin, 5-FU/SBS17b, platinum, colibactin SBS88), routing PARP inhibitor decisions, or auditing de novo extraction vs refit choice for cohort size.
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。
Queries myvariant.info BioThings aggregator for ClinVar, gnomAD, dbSNP, dbNSFP, COSMIC, CADD, and CIViC annotations in batched, version-tracked requests. Use when annotating variant lists from multiple databases simultaneously without managing per-source APIs, and when reproducibility-grade analyses require recording source data versions via _meta.
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 knowledge for Azure Static Web Apps development including troubleshooting, decision making, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when wiring SWA APIs to Azure DBs, configuring custom domains/backends, Entra auth, CI/CD, or plan limits, and other Azure Static Web Apps related development tasks. Not for Azure App Service (use azure-app-service), Azure Functions (use azure-functions), Azure Container Apps (use azure-container-apps), Azure Logic Apps (use azure-logic-apps).
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。
Annotate and filter VCF variants with SnpEff and SnpSift. SnpEff predicts functional effects (HIGH/MODERATE/LOW/MODIFIER), genes, transcripts, AA changes, HGVS; SnpSift filters and adds ClinVar/dbSNP. Java CLI with Python subprocess integration. Use ANNOVAR for multi-database annotation; Ensembl VEP for REST API; SnpEff for fast CLI with pre-built genomes.
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。
Fast in-memory DataFrame with lazy evaluation, parallel execution, Arrow backend. Use for tabular data in RAM (1–100 GB) when pandas is too slow. Expression API: select, filter, group_by, joins, pivots, window. Lazy mode enables predicate/projection pushdown. Reads CSV, Parquet, JSON, Excel, DBs, cloud. Larger-than-RAM: Dask; GPU: cuDF.
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。
Auto-annotate plasmids with features (promoters, terminators, resistance, origins, tags, fluorescent proteins) via BLAST against curated DBs (Addgene, fpbase, SnapGene). FASTA or raw sequence in; annotated GenBank, interactive HTML maps, CSV tables out. Handles circular topology. Use to verify synthetic constructs, prep Addgene submissions, share maps, or batch-annotate cloning 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。
Benchling R&D Python SDK: CRUD on registry entities (DNA, RNA, proteins, custom), inventory, ELN, workflow automation. Needs Benchling account and API key. Use biopython for local sequence analysis; pubchem for chemical DBs.
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。
Search NCBI databases using Biopython Bio.Entrez (ESearch, EInfo, EGQuery, ESpell). Use when finding records by keyword, building reproducible field-qualified queries, navigating the Entrez Query Translator, exploiting the history server for large result sets, handling retmax caps, or interpreting weekly index lag. Covers PubMed, Nucleotide, Protein, Gene, SRA, GEO, Assembly, Taxonomy, ClinVar, dbSNP.
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。
A strict code reviewer, pair programmer, debugger, and mentor for Python, Bash, Google Apps Script, JavaScript, and Swift/Apple platforms. Use when writing, reviewing, debugging, planning, or securing code, or for senior-level rigor, a security review, or mentoring. Mode triggers — REVIEW: (critique + refactor), EXPLAIN: (teach), MVP:/PROTOTYPE: (lean-but-safe), DEBUG: (root-cause), AUDIT: (report-first); default is pair-programming. Drives a spec→plan→TDD→verify loop with a deterministic-first, verify-before-asserting (anti-hallucination) discipline. Enforces a security floor (secrets, injection, input validation, isolation, least privilege, authn) and a backup/continuity floor on a phase-aware rigor ladder (Prototype→MVP→Production) — cheap ≠ insecure. Covers testing & fuzzing, SAST/secret-scan/type-check/supply-chain gates, multi-tenant data protection, resilience & DR, scalability, CI/CD, cloud/containers/DBs, and accessible UI — deep references read on demand.
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。
Deterministic deep research via RivalSearchMCP. 9 tools: 5-engine web search (DuckDuckGo/Bing/Yahoo/Mojeek/Wikipedia), 9-platform social search (Reddit/HN/StackOverflow/Dev.to/Medium/ProductHunt/Bluesky/Lobste.rs/Lemmy), 5-source news (Google/Bing/Guardian/GDELT/DDG), 5 academic DBs (OpenAlex/CrossRef/arXiv/PubMed/EuropePMC), GitHub search, website mapping, content extraction with OCR, and research topic synthesis. No API keys required. Use when the user needs web research, competitive analysis, content discovery, or academic paper 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。
对用户、产品等业务对象做分群：用波士顿矩阵法做象限分群，或用分层聚类、K-means、DBSCAN 等聚类技术分群。当需要做客群/产品分群、象限策略、画像或密度型子结构发现时调用。触发条件：当对话中出现“分群”、“分类”、“聚类”、“不同类型”、“不同场景”等体现分群分析词语时触发。
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。
Designing search engines and vector DBs for full-text, vector, and hybrid retrieval, including permission-aware retrieval for multi-tenant or per-role corpora. Use for search design, index optimization, the RAG retrieval layer, or deciding where ACL filtering belongs in the query path.
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。
Updated to be the unified database skill covering ORM, migrations, edge DBs, and Supabase CLI / Keahlian database terpadu untuk ORM, migrasi, edge DB, dan Supabase CLI.
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 running GDB: breakpoints, watchpoints, segfault or hang debugging, reverse debugging, remote gdbserver, core dumps, pretty-printers, Python scripting, or multi-threaded debugging.
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。
JUnit 5, Mockito, AssertJ, and Testcontainers patterns for any JVM project. Covers test structure, parameterised tests, mocking discipline, fluent assertions, and integration testing with real containers. Stack-agnostic — referenced by every Java plugin in the marketplace. Use this skill to: - Write clear, maintainable unit tests with JUnit 5. - Mock dependencies with Mockito without overusing mocks. - Write expressive assertions with AssertJ. - Spin up real infrastructure (DBs, message brokers) with Testcontainers for integration tests. Do NOT use this skill for: - Spring-specific test slices (@SpringBootTest, @WebMvcTest, @DataJpaTest — those are in spring-boot-plugin skills). - Framework-specific mocking utilities (MockMvc, WebTestClient).
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。
How to write a Grafema batch-mode plugin that reads the graph and filesystem, then writes new edges/nodes directly to RFDB. Use when: (1) need to add project-specific edges that generic analyzers can't produce, (2) need to read config files (JSON/YAML) and trace values into code, (3) implementing framework-specific resolvers (Django settings, Express routes, pipeline configs). Covers: grafema.config.yaml plugin config, RFDB field naming (src/dst NOT source/target), addEdges/addNodes API, RFDBServerBackend connection pattern.
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。
Fix node query issues in Grafema tests when nodes have numeric IDs instead of human-readable IDs, or when `type` field is undefined. Use when: (1) queryNodes returns nodes with numeric IDs like "52710336597754872375318185843222727675" instead of semantic IDs like "net:request#__network__", (2) node.type is undefined but node.nodeType has a value, (3) metadata is a JSON string instead of parsed object, (4) tests fail with "Cannot read property 'length' of undefined" after queryNodes. Covers RFDBServerBackend vs RFDBClient distinction and async generator 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。
Auto-activate for pytest_databases, Docker DB fixtures, PostgreSQL/pgvector/ParadeDB, MySQL/MariaDB, Oracle/SQL Server, CockroachDB, MongoDB, Redis/Valkey, BigQuery/Spanner, MinIO, or RustFS. Not for mocked DBs.
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。
Search NCBI databases using Biopython Bio.Entrez (ESearch, EInfo, EGQuery, ESpell). Use when finding records by keyword, building reproducible field-qualified queries, navigating the Entrez Query Translator, exploiting the history server for large result sets, handling retmax caps, or interpreting weekly index lag. Covers PubMed, Nucleotide, Protein, Gene, SRA, GEO, Assembly, Taxonomy, ClinVar, dbSNP.
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。