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概要と使いどころ
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。
Sequence-based deep learning for ATAC-seq using chromBPNet, BPNet, scBasset, or EnFormer. Use when correcting Tn5 bias with neural networks beyond k-mer models, predicting per-base accessibility profiles, scoring in silico variant effects at GWAS or rare-variant SNPs, discovering motifs via DeepLIFT/TF-MoDISco from a trained model, or generating cell-type-specific accessibility predictions for unobserved cell states.
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。
Parse and analyze multiple sequence alignments using Biopython. Extract sequences, identify conserved regions, analyze gaps, work with annotations, and manipulate alignment data for downstream analysis. Use when parsing or manipulating multiple sequence alignments.
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。
Harden iOS/Android apps you ship: storage, TLS pinning policy, IPC, and backend trust. You still enforce authz on the server.
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。
Coordinate containment, eradication, and recovery for an authorized incident. Preserve evidence, communicate facts, and avoid destructive actions without approval.
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 reactive, type-safe, production-grade backends. ALWAYS use this skill when the user asks to build, plan, design, or implement backend features, APIs, data models, server logic, database schemas, web apps, full stack apps, or mobile apps. This includes planning and architecture discussions.
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。
Drill-down on the shared MatrxDataTable, local or server, with per-Dimension levels. Use when adding group-by, breakdown or 'by X' analytics to a table, replacing hand-built totals or rollup tables, or when people want counts or sums by status, owner, model or day.
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。
Connect to Azure Database for PostgreSQL Flexible Server from Node.js/TypeScript using the pg (node-postgres) package.
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 real-time web applications with Azure Web PubSub SDK for Java. Use when implementing WebSocket-based messaging, live updates, chat applications, or server-to-client push notifications.
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。
⚠️ DEPRECATED: This SDK has been renamed to Call Automation. For new projects, use azure-communication-callautomation instead. This skill is for maintaining legacy code only.
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 server-side call automation workflows including IVR systems, call routing, recording, and AI-powered interactions.
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。
Goal-integrity skill. Use for backend/API/persistence, preserve/do-not-change, tests/validation, mocks, rework, multi-part requests. Emits Goal Contracts, Deviation Notices, Phase Checks, Final Audits. Skip for Q&A or trivial edits.
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。
Semantic + keyword search and connection-discovery across the user's own Apple Notes via the apple-notes MCP server. Use when the user wants to find, recall, or synthesize something from their notes, or surface non-obvious bridges/related notes. macOS, on-device.
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。
Actorization converts existing software into reusable serverless applications compatible with the Apify platform. Actors are programs packaged as Docker images that accept well-defined JSON input, perform an action, and optionally produce structured JSON output.
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。
Map observed site vegetation, species lists, land-cover descriptions, or plant management PFTs to EcoSIM vegetation codes using templates/ecosim_pftpar_20260303.nc.cdl. Use when choosing or validating EcoSIM pft_type values, pfts entries, pfts_short categories, pfts_long plant type meanings, or the combined short-code plus Koppen climate numerical code used by plant management inputs.
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 radically minimal, blank-canvas interface built as a pure edge-to-edge surface, with almost no color and typography carrying the visual weight. Black serves as the only filled color, the only divider, and the sole surface tone cards.
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。
Execute explicitly authorized tests and record traceable expected, observed, evidence-bound results.
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。
Triage defects with independent severity and priority, reproducible evidence, preserved attempt history, and explicit resolution retesting.
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。
Design traceable Web and browser checks with explicit browser coverage, observed interactions, isolated sessions, and evidence limitations.
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。
Recommend context-appropriate QA tools while preserving observed availability, constraints, and safer alternatives.
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。
Review or refactor an existing SKILL.md and its bundled resources — a smaller always-loaded core, conditional references, preserved behavior, lean/guided consumer profiles, and an A/B protocol to compare a candidate with the previous version. Use when a skill repeats itself, loads references on every run, triggers too broadly or misses legitimate requests, or needs its efficiency measured across models. Do NOT use for writing a new skill from scratch, refactoring application code, translating a skill verbatim, or performing the target skill's own task.
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 an existing skill must be refactored to cut context and ambiguity without losing behavior, safety or fallbacks — 'refatorar a skill', 'reduzir esta SKILL.md', 'revisar skill com métricas' — with before/after metrics, preserved invariants and three review rounds.
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。
Infraestrutura para IA — GPU, servidores MCP, gateway de LLM, pipeline de inferência, custo de token, cache semântico e observabilidade de modelo. Use quando o usuário disser "GPU", "MCP server", "LLM", "inferência", "modelo", "gateway de IA", "custo de token", "rate limit da API de IA", "self-host de modelo".
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。