「configuration」の検索結果

Designs QTL colocalization studies that connect eQTL, pQTL, sQTL, or related molecular QTL signals with GWAS loci. Always use this skill whenever a user wants to plan, scope, or structure a locus-level study asking whether a GWAS association and a molecular QTL association may reflect the same underlying causal signal. Covers locus definition, QTL/GWAS source architecture, ancestry and LD alignment, single-locus vs multi-locus strategy, candidate-gene prioritization, optional fine-mapping, linked MR/SMR follow-up, and functional annotation. Always output four workload configurations (Lite / Standard / Advanced / Publication+) with a recommended primary plan, stepwise workflow, method rationale, evidence hierarchy, figure plan, minimal executable version, and strictly verified literature guidance with no fabricated references. Never equate colocalization with causality proof, mediation proof, or automatic target validation. Always include the mandatory Dataset Disclaimer immediately before any workflow section that mentions datasets, repositories, consortia, or public resources.

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。

Designs complete research plans that integrate clinical variables with multi-omics data from a user-provided biomedical direction. Always use this skill whenever a user wants to design, scope, or structure a study that combines clinical variables with transcriptomics, proteomics, metabolomics, epigenomics, or related omics layers for mechanism interpretation, biomarker development, risk stratification, treatment-response analysis, or translational use. It should define the clinical use case, alignment across data layers, feature-reduction and fusion logic, modeling route, mechanism-interpretation layer, validation ladder, and four workload configurations (Lite / Standard / Advanced / Publication+). Never fabricate datasets, accession numbers, sample counts, metadata completeness, platform coverage, literature references, PMIDs, DOIs, or validation status. Always include the mandatory Dataset Disclaimer immediately before any workflow section that mentions datasets or public resources.

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。

Designs complete integrated research plans for bulk transcriptomics, proteomics, metabolomics, and related omics from a user-provided biomedical direction. Always use this skill whenever a user wants to design, scope, or structure a bulk multi-omics or single-omics-plus-clinical study — including disease-focused, mechanism-focused, biomarker-focused, stratification-oriented, or translational projects. It should define the research question, choose the best-fit study pattern, recommend example datasets as reference candidates only, specify the core analysis modules and method choices, propose a validation ladder, and output four workload configurations (Lite / Standard / Advanced / Publication+). Never fabricate datasets, accession numbers, sample counts, metadata completeness, cohort availability, assay coverage, literature references, PMIDs, DOIs, or validation status. Always include the mandatory Dataset Disclaimer immediately before any workflow section that mentions datasets or public resources.

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。

Designs cell-based and animal-based validation plans that translate computational, omics, biomarker, genetic, or clinical findings into experimentally testable validation routes. Always use this skill whenever a user wants to move from an in silico, statistical, or clinical association finding toward wet-lab validation using cell systems, organoid-like systems, xenograft or genetically relevant animal models. It should define the exact claim to test, separate mechanism-testing from association-support and translational-support goals, choose the best-fit model family, specify perturbation strategy, readouts, controls, sequencing of experiments, and four workload configurations (Lite / Standard / Advanced / Publication+) with one recommended primary plan. Never fabricate model availability, reagent availability, species relevance, assay feasibility, phenotype penetrance, expected effect sizes, validation success, or literature references.

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 this skill to migrate identified PostgreSQL tables to Timescale/TimescaleDB hypertables with optimal configuration and validation. **Trigger when user asks to:** - Migrate or convert PostgreSQL tables to hypertables - Execute hypertable migration with minimal downtime - Plan blue-green migration for large tables - Validate hypertable migration success - Configure compression after migration **Prerequisites:** Tables already identified as candidates (use find-hypertable-candidates first if needed) **Keywords:** migrate to hypertable, convert table, Timescale, TimescaleDB, blue-green migration, in-place conversion, create_hypertable, migration validation, compression setup Step-by-step migration planning including: partition column selection, chunk interval calculation, PK/constraint handling, migration execution (in-place vs blue-green), and performance validation queries.

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 this skill for setting up vector similarity search with pgvector for AI/ML embeddings, RAG applications, or semantic search. **Trigger when user asks to:** - Store or search vector embeddings in PostgreSQL - Set up semantic search, similarity search, or nearest neighbor search - Create HNSW or IVFFlat indexes for vectors - Implement RAG (Retrieval Augmented Generation) with PostgreSQL - Optimize pgvector performance, recall, or memory usage - Use binary quantization for large vector datasets **Keywords:** pgvector, embeddings, semantic search, vector similarity, HNSW, IVFFlat, halfvec, cosine distance, nearest neighbor, RAG, LLM, AI search Covers: halfvec storage, HNSW index configuration (m, ef_construction, ef_search), quantization strategies, filtered search, bulk loading, and performance tuning.

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。

Replay an LLM inference request trace (Mooncake / vLLM / SGLang hash_ids format) against a block-level KV prefix cache and compute hit statistics. Use when given a request trace plus cache configuration and asked for hit rate, hit tokens, or final cache contents. Covers the longest-contiguous-prefix semantics that distinguishes KV prefix caching from full-prompt prompt caching, the policy-specific residency and eviction rules (LRU, LFU, S3FIFO), and the partial-last-block accounting rule.

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。

Basic usage of the General Lake Model (GLM) for lake temperature simulation. Use when you need to run GLM, understand input files, or modify configuration parameters.

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 configuring Maven plugins, setting up common plugins like compiler, surefire, jar, or creating custom plugin executions.

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。

This skill should be used when the user asks to "pentest SSH services", "enumerate SSH configurations", "brute force SSH credentials", "exploit SSH vulnerabilities", "perform SSH tunneling", or "audit SSH security". It provides comprehensive SSH penetration testing methodologies and techniques.

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 for the TRL (Transformer Reinforcement Learning) library codebase. Use proactively before reading or editing any file under `trl/` so you have the intended contracts and invariants in mind, not just what the current code says. Covers trainer hierarchy (SFT, DPO, GRPO, KTO), shared utility functions (selective_log_softmax, decode_and_strip_padding, padding helpers), configuration system, model wrappers, and how data flows through any TRL trainer.

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 this skill when reading or writing YAML configuration files, loading vehicle parameters, or handling config file parsing with proper error 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。

Normalize line endings for cross-platform file handling with CRLF/LF conversion and git configuration.

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 environment variable to CLI argument mapping with prefix support, type conversion, and fallback chains for configuration.

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。

Bootstrap MCP server with Python SDK, transport configuration, tool/resource handlers, and proper project structure.

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 Zod/JSON Schema configuration validators with defaults and error messages.

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 Cobra/Viper-based Go CLI applications with persistent flags, subcommands, and configuration management. Creates production-ready Go CLI with modern patterns.

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 cosmiconfig for hierarchical configuration loading from multiple sources and formats.

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 configuration file migration utilities for version upgrades.

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。

Integrate database drivers and connectors — connection pooling, query builders, ORM configuration, and multi-database abstraction layers.

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。

Pinecone vector database setup, configuration, and operations for RAG 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。

Integrates MCP App UI with host theming system. Applies host CSS variables, handles onhostcontextchanged, safe area insets, display mode detection, and fullscreen configuration.

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 NeMo Guardrails configuration for conversational safety and control

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。

Comprehensive Content Security Policy audit for MCP Apps in sandboxed iframes. Discovers all network origins, traces them to source, and generates CSP configuration for registerAppResource.

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。