3,131 件 ・ 関連度順
概要と使いどころ
Anti-slop frontend skill for landing pages, portfolios, and redesigns. The agent reads the brief, infers the right design direction, and ships interfaces that do not look templated. Real design systems when applicable, audit-first on redesigns, strict pre-flight check.
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。
ENCODE Portal REST API for regulatory genomics: TF ChIP-seq, ATAC-seq/DNase-seq peaks, histone marks, and RNA-seq across 1000+ cell types. Search experiments by assay/biosample/target; download BED/bigWig; retrieve SCREEN cCREs by region or gene. Use to annotate variants with regulatory tracks, find open chromatin in a cell type, or fetch peak files for ChIP/ATAC analysis. For regulatory variant scoring use regulomedb-database; for GWAS associations use gwas-database.
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。
Unified CLI/Python interface to 20+ genomic databases. Gene lookups (Ensembl search/info/seq), BLAST/BLAT, AlphaFold, Enrichr enrichment, OpenTargets disease/drug, CELLxGENE single-cell, cBioPortal/COSMIC cancer, ARCHS4 expression. Spans genomics, proteomics, disease. For batch/advanced BLAST use biopython; for multi-DB Python SDK use bioservices.
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。
ENA REST API for sequences, reads, assemblies, and annotations. Portal API search, Browser API retrieval (XML/FASTA/EMBL), file reports for FASTQ/BAM URLs, taxonomy, cross-refs. For multi-DB Python use bioservices; for NCBI-only use pubmed-database or Biopython Entrez.
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。
KEGG REST API (academic only). Pathways, genes, compounds, enzymes, diseases, drugs via 7 ops (info/list/find/get/conv/link/ddi). ID conversion (NCBI/UniProt/PubChem). Use bioservices for multi-DB Python.
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。
Query UniProt REST API: search by gene/protein name, fetch FASTA, map IDs (Ensembl, PDB, RefSeq), access Swiss-Prot annotations. Use bioservices for multi-DB access; alphafold-database-access for structures.
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。
Python toolkit for neurophysiological signal processing: ECG (HR, HRV, R-peaks), EEG (complexity, PSD), EMG (activation onset), EDA/GSR (SCR decomposition), PPG, and RSP. Includes synthetic signal simulation. Alternatives: BioSPPy (less maintained), MNE (EEG/MEG specialist), heartpy (ECG only), scipy.signal (raw DSP).
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。
Access USPTO patent data via PatentsView REST API and Google Patents Public Data (BigQuery). Search by inventor, assignee, CPC, or keywords; download metadata and claims; analyze portfolios; track tech trends. For IP landscape analysis, competitor monitoring, prior art search, and tech forecasting in life sciences and biotech.
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。
Molecular biology toolkit: sequence manipulation, FASTA/GenBank/PDB I/O, NCBI Entrez, BLAST automation, pairwise/MSA alignment, Bio.PDB, phylogenetic trees. Use for batch processing, custom pipelines, format conversion, PubMed/GenBank queries. For quick gene lookups use gget; for multi-service REST APIs use bioservices.
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。
Evaluates code against Apple's App Store Review Guidelines. Use this skill when reviewing iOS, macOS, tvOS, watchOS, or visionOS app code (Swift, Objective-C, React Native, or Expo) to identify potential App Store rejection issues before submission. Triggers on tasks involving app review preparation, compliance checking, or App Store submission readiness.
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。
Provision a disposable Jurassic Ninja store for the Maestro smoke suite, connect Jetpack to the developer's own WordPress.com test account, create WooCommerce API keys, and write .maestro/.env.local. Use when Maestro setup is missing, when .env.local points at an expired store, or when a clean store is wanted.
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 swift-snapshot-testing to visually verify SwiftUI views during implementation. Renders views to PNG for comparison against design references. Fast feedback loop (~25s/cycle).
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。
Set up the ContextA8C MCP server for accessing Automattic internal resources (Slack, Linear, P2s, GitHub Enterprise, etc.)
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。
Start or stop the WireMock API mock server for UI testing and E2E verification.
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。
Evaluate WooAIAssistant against a structured scenario suite with hard invariants + LLM-as-judge rubric scoring. Runs live against the demo store + gpt-5.1 via the woo-mobile-ai backend wrapper, writes a JSONL run record, compares against stored baselines, and surfaces regressions. Always delegated to a subagent so the main context only sees the markdown report.
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。
Provides patterns for unit testing Spring Cache annotations (@Cacheable, @CachePut, @CacheEvict). Generates test code that mocks cache managers, verifies cache hit/miss behavior, tests cache key generation with SpEL expressions, validates eviction strategies, and checks conditional caching scenarios. Triggers: caching tests, test Spring cache, mock cache, Spring Boot caching, cache hit/miss verification, @Cacheable 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。
Provides patterns and configurations for running WireMock as a standalone Docker container. Generates mock HTTP endpoints, creates stub mappings for testing, validates integration scenarios, and simulates error conditions. Use when you need to mock APIs, create a mock server, stub external services, simulate third-party APIs, or fake API responses for integration 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。
Provides patterns for unit testing service layer with Mockito. Creates isolated tests that mock repository calls, verify method invocations, test exception scenarios, and stub external API responses. Use when testing service behaviors and business logic without database or external services.
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。
Provides edge case, corner case, boundary condition, and limit testing patterns for Java unit tests. Validates minimum/maximum values, null cases, empty collections, numeric overflow/underflow, floating-point precision, and off-by-one scenarios using JUnit 5 and AssertJ. Use when writing .java test files to ensure code handles limits, corner cases, and special inputs correctly.
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。
What-if analysis for construction projects: model different scenarios and their cost/schedule/resource impacts. Compare alternatives and optimize decisions.
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。
Compare cost estimates across projects, versions, and scenarios. Identify variances, benchmark against standards, and generate comparison reports.
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 investment portfolios for risk, diversification, fees. Asset allocation recommendations, tax-loss harvesting, rebalancing.
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 and roll out a price increase or packaging change without triggering churn. Segments the base by price sensitivity, models revenue and churn scenarios, decides grandfathering and notice strategy, and produces the full comms kit (customer email, in-app notice, sales talk track, support FAQ, objection handling). Use when raising prices, repackaging tiers, or moving customers to new plans.
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。
Calculate comprehensive ROI for AI implementation projects. Takes current costs, manual process time, team size, and hourly rates. Generates detailed roi-analysis.md with executive summary, cost-benefit tables, sensitivity analysis, break-even timeline, and comparison scenarios. Use when evaluating AI investments, building business cases, or justifying automation spend.
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。