126 件 ・ 関連度順
概要と使いどころ
ABP Framework application layer patterns including AppServices, DTOs, Mapperly mapping, Unit of Work, and common patterns like Filter DTOs and ResponseModel. Use when: (1) creating AppServices, (2) mapping DTOs with Mapperly, (3) implementing list filtering, (4) wrapping API responses.
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 the user has finished building a mobile app, started Metro with `npm run dev`, and wants the running app monitored for runtime errors AND silent failures (empty lists, blank screens, swallowed network errors) and fixed autonomously. Accepts a free-text symptom (e.g., `/debug-app "todos not appearing on home screen"`) to drive persisted-log diagnostics — injects temporary console.log statements at data-path boundaries, reads the sanitized `.powernative/metro-logs/` log, and cleans up logs after the root cause is fixed. Supports port/platform filtering, configurable clean-cycle and timeout limits, and watch-only `--no-fix` mode. Otherwise polls with a durable byte cursor, fixes inline or routes appropriately, verifies each fix from new output, and exits after the configured clean checks (default 3). Foreground loop — blocks the conversation while running. Run only after the app is loaded.
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。
CloudBase declarative deployment from a cloudbaserc config (声明式部署, 配置式部署, cloudbaserc 部署) through the deployBuild / deployPlan / deployApply MCP tools. Use when deploying database, functions, app, hosting, or gateway resources described in cloudbaserc.json/yaml as a single desired-state config, when a user wants to build static hosting artifacts locally first (deployBuild), or wants a dry-run plan before applying (deployPlan), or when handling multi-environment deploys via mode / envOverrides. Covers build-plan-apply flow (deployBuild local build → deployPlan dry-run → deployApply confirm=true), hosting build-output neutralization, envId resolution priority, only/skip filtering, concurrency, and continueOnError. Prefer deployBuild (when hosting declares a buildCommand) and deployPlan before deployApply; do not confuse with per-resource tcb CLI deploy or single-function deploy.
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 designing or implementing search, filtering, sorting, query bars, and result states in Apple-platform apps — keep search, filter, and sort as separate jobs with clear no-result 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。
Complete guide for TUnit advanced applications. Use when using TUnit for data-driven testing, dependency injection, or integration testing. Covers MethodDataSource, ClassDataSource, Matrix Tests, Properties filtering. Includes Retry/Timeout control, WebApplicationFactory integration, Testcontainers multi-service orchestration. Keywords: TUnit advanced, TUnit advanced, MethodDataSource, ClassDataSource, Matrix Tests, MatrixDataSource, MicrosoftDependencyInjectionDataSource, Property, Retry, Timeout, data-driven testing, test filtering, WebApplicationFactory TUnit, multi-container orchestration
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 dumb-pipe and traffic-filtering C2 redirectors with nginx (proxy_pass) and Apache (mod_rewrite), deriving filter rules from a Malleable C2 profile, layering Let's Encrypt TLS, and applying OPSEC controls like domain fronting and UA/geo filtering. Use when standing up red-team C2 that must survive blue-team triage or ensuring only profile-matching implant traffic reaches the hidden team 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。
Matched filtering techniques for gravitational wave detection. Use when searching for signals in detector data using template waveforms, including both time-domain and frequency-domain approaches. Works with PyCBC for generating templates and performing matched filtering.
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。
Controls error rates across thousands of simultaneous tests in genomics discovery using false-discovery-rate methods (Benjamini-Hochberg 1995; Benjamini-Yekutieli 2001 for arbitrary dependence; Storey q-value with pi0 estimation; local FDR; independent filtering Bourgon 2010; covariate-weighted FDR via IHW Ignatiadis 2016), plus family-wise error control (Bonferroni, Holm) and the GWAS genome-wide threshold. Covers the FDR-versus-FWER choice as the discovery-versus-confirmatory distinction, the dependence assumptions behind BH (PRDS) versus BY, pi0 estimation, the independent-filtering and false-coverage-rate traps, and reproducibility ranking via IDR (Li 2011). Use when correcting p-values from genome-wide tests, choosing between BH/BY/q-value/Bonferroni, setting an FDR threshold, applying IHW or independent filtering, or interpreting q-values. For confirmatory trials with few pre-specified endpoints (closed testing, graphical/gatekeeping), see clinical-biostatistics/multiplicity-graphical.
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。
Guides MongoDB users through implementing and optimizing Atlas Search (full-text), Vector Search (semantic), and Hybrid Search solutions. Use this skill when users need to build search functionality for text-based queries (autocomplete, fuzzy matching, faceted search), semantic similarity (embeddings, RAG applications), or combined approaches. Also use when users need text containment, substring matching ('contains', 'includes', 'appears in'), case-insensitive or multi-field text search, or filtering across many fields with variable combinations. Provides workflows for selecting the right search type, creating indexes, constructing queries, and optimizing performance using the MongoDB MCP 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。
Reads from, writes to, and manages relational databases using MATLAB Database Toolbox. Use when connecting to databases, reading data with sqlread or fetch, filtering with rowfilter, writing with sqlwrite, updating with sqlupdate, executing SQL statements, managing transactions with commit and rollback, mapping MATLAB classes to tables with ORM (Mappable, ormread, ormwrite, ormupdate), or performing any database operation from MATLAB. Triggers on: database, SQL, sqlread, sqlwrite, sqlupdate, fetch, execute, rowfilter, RowFilter, ORM, Mappable, ormread, ormwrite, ormupdate, orm2sql, transaction, commit, rollback, Database Toolbox, PostgreSQL, MySQL, SQLite, SQL Server, Oracle, database connection, database table, query database, insert data, update rows, delete rows, stored procedure, prepared statement, odbc, databaseConnectionOptions, datasource, data source, DSN, connection string, multithreaded, parallel.
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 optimized read queries using Dapper. Includes multi-mapping for joins, pagination, dynamic filtering, CTEs, and best practices for high-performance data access.
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。
Configure related lists on Salesforce records — column choice on Page Layouts (max 10 fields per related list), sort field selection, the Spring '24 Enhanced Related Lists component (which adds filtering, mass actions, and 10-row inline display), the choice between Related Lists / Related Lists - Single / Related List - Quick Links in Lightning App Builder, per-record-type related-list assignments, and the View All / pagination UX. NOT for FSL WorkOrderMilestone-on-Work-Order layout (use admin/fsl-sla-configuration-requirements), NOT for the Lightning page performance Related List - Single vs Full tradeoff in isolation (use admin/lightning-page-performance-tuning), NOT for Industries Timeline-as-related-list (use admin/health-cloud-timeline), NOT for offline-priming related-list caps (use admin/fsl-mobile-app-setup).
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。
Controls error rates across thousands of simultaneous tests in genomics discovery using false-discovery-rate methods (Benjamini-Hochberg 1995; Benjamini-Yekutieli 2001 for arbitrary dependence; Storey q-value with pi0 estimation; local FDR; independent filtering Bourgon 2010; covariate-weighted FDR via IHW Ignatiadis 2016), plus family-wise error control (Bonferroni, Holm) and the GWAS genome-wide threshold. Covers the FDR-versus-FWER choice as the discovery-versus-confirmatory distinction, the dependence assumptions behind BH (PRDS) versus BY, pi0 estimation, the independent-filtering and false-coverage-rate traps, and reproducibility ranking via IDR (Li 2011). Use when correcting p-values from genome-wide tests, choosing between BH/BY/q-value/Bonferroni, setting an FDR threshold, applying IHW or independent filtering, or interpreting q-values. For confirmatory trials with few pre-specified endpoints (closed testing, graphical/gatekeeping), see clinical-biostatistics/multiplicity-graphical.
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。
Guides MongoDB users through implementing and optimizing Atlas Search (full-text), Vector Search (semantic), and Hybrid Search solutions. Use this skill when users need to build search functionality for text-based queries (autocomplete, fuzzy matching, faceted search), semantic similarity (embeddings, RAG applications), or combined approaches. Also use when users need text containment, substring matching ('contains', 'includes', 'appears in'), case-insensitive or multi-field text search, or filtering across many fields with variable combinations. Provides workflows for selecting the right search type, creating indexes, constructing queries, and optimizing performance using the MongoDB MCP 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。
Controls error rates across thousands of simultaneous tests in genomics discovery using false-discovery-rate methods (Benjamini-Hochberg 1995; Benjamini-Yekutieli 2001 for arbitrary dependence; Storey q-value with pi0 estimation; local FDR; independent filtering Bourgon 2010; covariate-weighted FDR via IHW Ignatiadis 2016), plus family-wise error control (Bonferroni, Holm) and the GWAS genome-wide threshold. Covers the FDR-versus-FWER choice as the discovery-versus-confirmatory distinction, the dependence assumptions behind BH (PRDS) versus BY, pi0 estimation, the independent-filtering and false-coverage-rate traps, and reproducibility ranking via IDR (Li 2011). Use when correcting p-values from genome-wide tests, choosing between BH/BY/q-value/Bonferroni, setting an FDR threshold, applying IHW or independent filtering, or interpreting q-values. For confirmatory trials with few pre-specified endpoints (closed testing, graphical/gatekeeping), see clinical-biostatistics/multiplicity-graphical.
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。
Implement REST APIs in ABP Framework with AppServices, DTOs, pagination, filtering, and authorization. Use when building API endpoints for ABP 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。
Controls error rates across thousands of simultaneous tests in genomics discovery using false-discovery-rate methods (Benjamini-Hochberg 1995; Benjamini-Yekutieli 2001 for arbitrary dependence; Storey q-value with pi0 estimation; local FDR; independent filtering Bourgon 2010; covariate-weighted FDR via IHW Ignatiadis 2016), plus family-wise error control (Bonferroni, Holm) and the GWAS genome-wide threshold. Covers the FDR-versus-FWER choice as the discovery-versus-confirmatory distinction, the dependence assumptions behind BH (PRDS) versus BY, pi0 estimation, the independent-filtering and false-coverage-rate traps, and reproducibility ranking via IDR (Li 2011). Use when correcting p-values from genome-wide tests, choosing between BH/BY/q-value/Bonferroni, setting an FDR threshold, applying IHW or independent filtering, or interpreting q-values. For confirmatory trials with few pre-specified endpoints (closed testing, graphical/gatekeeping), see clinical-biostatistics/multiplicity-graphical.
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。
Implement REST APIs in ABP Framework with AppServices, DTOs, pagination, filtering, and authorization. Use when building API endpoints for ABP 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。
Credit risk data cleaning and variable screening pipeline for pre-loan modeling. Use when working with raw credit data that needs quality assessment, missing value analysis, or variable selection before modeling. it covers data loading and formatting, abnormal period filtering, missing rate calculation, high-missing variable removal,low-IV variable filtering, high-PSI variable removal, Null Importance denoising, high-correlation variable removal, and cleaning report generation. Applicable scenarios arecredit risk data cleaning, variable screening, pre-loan modeling preprocessing.
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。
NVIDIA's runtime safety framework for LLM applications. Features jailbreak detection, input/output validation, fact-checking, hallucination detection, PII filtering, toxicity detection. Uses Colang 2.0 DSL for programmable rails. Production-ready, runs on T4 GPU.
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。
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。
NVIDIA's runtime safety framework for LLM applications. Features jailbreak detection, input/output validation, fact-checking, hallucination detection, PII filtering, toxicity detection. Uses Colang 2.0 DSL for programmable rails. Production-ready, runs on T4 GPU.
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 structured CSV security audit reports from vulnerability data with proper filtering and formatting. This skill covers CSV schema design for security reports, using Python csv.DictWriter, severity-based filtering, and field mapping from JSON to tabular format.
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。