「annotation」の検索結果

MyBatisを使うJavaのデータ保存・取得処理を設計、レビューします。SQLと結果の対応付け、動的SQLの安全性、トランザクションや検索性能を扱います。

• マッパーとXMLの対応付けの設計
• 動的SQLと入力値の安全性確認
• 複数更新のトランザクション確認

A lo-fi multi-screen MOBILE flow wireframe — three or four phone frames laid out in a row on a board, showing a connected user flow (Onboarding → Home feed → Item detail → Confirm). Grey-box content inside each device, dashed connector arrows + numbered step labels between screens, and a couple of annotation callouts. Reads like an app flow pinned to a whiteboard before any pixels are committed. Use when the brief asks for "mobile wireframe", "app flow", "user flow wireframe", "lo-fi mobile", "low fidelity", "线框图", "移动端线框", or "App 流程".

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。

An annotated / redline lo-fi wireframe — a desktop landing/marketing page drawn as flat greyboxes inside a browser chrome frame, overlaid with numbered annotation pins (①②③④⑤) in a single accent color, paired with a right-hand SPEC PANEL that lists each numbered pin with a short engineering / UX note. This is the "wireframe + redline spec" style — clean, flat, low-fidelity, NOT hand-drawn. Use when the brief asks for "annotated wireframe", "redline wireframe", "wireframe with spec", "lo-fi landing wireframe", "low fidelity", "线框图", "标注线框", or "redline".

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 crisp greybox / blueprint lo-fi wireframe — neutral grey blocks on a pale page, image placeholders drawn as a rectangle with a diagonal X, text shown as solid "lorem bars" of varying widths, sharp 1.5–2px borders, and a single monospace redline accent for annotations. Reads like a structural blueprint before any visual design is applied. Use when the brief asks for "greybox", "blueprint wireframe", "lo-fi dashboard", "low fidelity", "线框图", or "灰盒原型".

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 hand-drawn wireframe exploration — graph-paper background, marker / pencil tone, multiple tab labels for variants, sticky-note annotations, scribbled chart placeholders, hatched fills. Reads like a designer's whiteboard before any pixels are committed. Use when the brief asks for "wireframe", "sketch wireframe", "hand-drawn", "lo-fi", "whiteboard", "草稿", or "手绘原型".

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。

Command-line interface for Openscreen — a screen recording editor. A stateful CLI for editing screen recordings with zoom, speed ramps, trim, crop, annotations, and polished exports. Built on the Openscreen JSON project format with ffmpeg as the rendering backend. Designed for AI agents and power users who need programmatic video editing.

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。

Fits probabilistic models for single-cell omics, including scVI batch integration, scANVI annotation, totalVI CITE-seq, MultiVI RNA/ATAC integration, and posterior differential expression. Use for generative modeling, reference mapping, multimodal analysis, or model-based uncertainty; use scanpy for standard preprocessing and exploratory analysis.

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。

Performs pathway and gene-set enrichment analysis on gene lists or ranked gene data and interprets the results. Used when the user has a set of genes (differentially expressed genes from PyDESeq2/Scanpy, CRISPR-screen hits, cluster marker genes, proteomics hits) and wants to know which biological pathways, GO terms, or gene sets are over-represented or enriched. Covers over-representation analysis (ORA / Enrichr / Fisher / hypergeometric), ranked Gene Set Enrichment Analysis (GSEA / preranked), single-sample scoring (ssGSEA/GSVA), and functional profiling via gseapy, g:Profiler, Enrichr libraries, MSigDB, GO, KEGG, Reactome, and WikiPathways — plus gene-ID mapping, choosing the right background universe, multiple-testing correction, redundancy reduction, dotplots/enrichment maps, and publication-ready tables. Use this for "pathway analysis", "enrichment analysis", "GO enrichment", "KEGG/Reactome pathways", "GSEA", "over-representation", "functional annotation", or "what pathways are my genes in".

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。

Processes mass spectrometry data with pyOpenMS. Supports proteomics and metabolomics workflows—feature detection, peptide/protein identification, label-free quantification, adduct/accurate-mass annotation, and complex LC-MS/MS pipelines. Supports extensive file formats and algorithms. For simple spectral comparison and small-molecule library matching use matchms.

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 the 1000 Genomes Project dataset (3,202 whole-genome-sequenced individuals, GRCh38) at the level of individual participants. Use when a question is about individuals or variants in the 1000 Genomes Project cohort: which individuals carry variants matching specific criteria in a gene or region, which individuals are homozygous-reference at a position, which variants exist in the dataset or carried by specified individuals in a gene or region, the relatedness between two specified individuals. Variants are returned with 1000 Genomes allele frequencies (AF), gnomAD v4.1 exome and genome AF, AlphaMissense score, and HGVSp annotations.

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。

Manages biological datasets and models with LaminDB, including artifact registration, lineage tracking, schema validation, Bionty ontology annotation, query/search, collections, branches, storage, and workflow integrations. Use for reproducible biological data curation or a LaminDB lakehouse.

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 20+ bioinformatics resources through CLI/Python. Supports quick lookups of gene info, BLAST/BLAT, viral sequence downloads, PDB/mmCIF structures, G2P residue annotations, enrichment analysis, OpenTargets, COSMIC, CELLxGENE, and 8cube mouse specificity/expression data. Best for interactive exploration and simple queries. For batch processing or advanced BLAST use biopython; for multi-database Python workflows 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。

Inspects and automates microscopy data workflows against OMERO.server with omero-py, BlitzGateway, OMERO CLI, tables, annotations, ROIs, rendering, and documented OMERO.web APIs. Use this skill for scoped OMERO inventory, metadata export, import/export planning, or reviewed write 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。

Searches 18 scholarly APIs for papers, preprints, citations, open-access full text, repository records, and journal OA status, and returns results with reproducible provenance. Covers PubMed, PMC, Europe PMC, bioRxiv, medRxiv, arXiv, OpenAlex, Crossref, Semantic Scholar, CORE, Unpaywall, OpenCitations, PubTator3, Zenodo, Figshare, ROR, BioStudies, and DOAJ. Use when searching for papers, citations, DOI/PMID/arXiv lookups, abstracts, full text, open-access PDFs, preprints, citation graphs, author publications, biomedical entity annotations, deposited records (Zenodo, Figshare, BioStudies), institution ROR IDs, or any scholarly literature query. Triggers on mentions of any supported database or requests like "find papers on X", "look up this DOI", "who cites this paper", or "get me the PDF".

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。

Retrieves and analyzes Cancer Dependency Map (DepMap) release data, including CRISPR Chronos gene effects, cancer model annotations, omics biomarkers, and PRISM drug sensitivity. Supports cancer-selective dependency, co-essentiality, and candidate synthetic-lethality analyses with release-aware identifiers and statistical checks.

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 a Python interface to bioinformatics services including UniProt, KEGG, ChEMBL, Reactome, QuickGO, and UniChem. Used for cross-database protein annotation, pathway retrieval, chemical identifier mapping, and integrated biological data workflows with 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。

Predicts regulatory features, gene structure, and expression directly from DNA sequence using Genomic Intelligence's hosted transformer DNA language models — no local GPU or model weights. Six tasks over a REST API and a hosted MCP server (keyless public demo): promoter regions, splice donor/acceptor sites, enhancer activity, chromatin state, sequence-to-expression (log TPM), and de-novo gene annotation, plus a composite find-genes-then-predict-expression workflow. Use when the user has a gene symbol, a genomic region, or a DNA/FASTA sequence and wants any of these predictions, mentions Genomic Intelligence, genomicintelligence.ai, api.genomicintelligence.ai, or mcp.genomicintelligence.ai.

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。

Generate hand-drawn 16:9 article illustrations with the Grav character IP, sparse annotations, and absurd but clear visual metaphors.

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。

Guide for contributing to the Airflow Java SDK (AIP-108). Use this skill whenever a contributor is working in the `java-sdk/` directory or on the Java coordinator in `task-sdk/src/airflow/sdk/coordinators/java/` — whether they want to add a feature, write tests, fix a bug, understand the architecture, or prepare a PR. Trigger on phrases like "Java SDK", "JavaCoordinator", "java-sdk", "annotation processor", "Builder.Task", "BundleBuilder", or anything about running JVM tasks in Airflow.

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 making UI/frontend changes guided by visual context, when the user selects elements visually, draws annotations, or provides screenshots alongside change requests. Also use when editing components where spatial context (element identity, DOM references, layout data) supplements text instructions.

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 type safety with type hints, generics, protocols, and strict type checking. Use when adding type annotations, implementing generic classes, defining structural interfaces, or configuring mypy/pyright.

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。

Debug LLM applications using the Phoenix CLI. Fetch traces, analyze errors, structure trace review with open coding and axial coding, inspect datasets, review experiments, query annotation configs, and use the GraphQL API. Use whenever the user is analyzing traces or spans, investigating LLM/agent failures, deciding what to do after instrumenting an app, building failure taxonomies, choosing what evals to write, or asking "what's going wrong", "what kinds of mistakes", or "where do I focus" — even without naming a technique.

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。

Optimizes, improves, and debugs LLM prompts using production trace data, evaluations, and annotations. Extracts prompts from spans, gathers performance signal, and runs a data-driven optimization loop using the ax CLI. Use when the user mentions optimize prompt, improve prompt, make AI respond better, improve output quality, prompt engineering, prompt tuning, or system prompt improvement.

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。

Reference skill for Zoom Cobrowse SDK. Use after routing to a collaborative-support workflow when implementing browser co-browsing, annotation tools, privacy masking, remote assist, or PIN-based session sharing.

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。