'uv' 검색 결과

Batch effect correction for CRISPR screens covering ComBat empirical-Bayes, RUV, SVA, control-sgRNA normalization, and the model-based alternative of including batch as a covariate in MAGeCK MLE or Chronos. Covers screen-specific batch sources (passage cohort, library lot, infection day, sequencing run, Cas9 lot, FBS lot), PCA + variance-decomposition diagnostic to decide if correction is needed, when correction harms biology by over-correcting condition into batch, limma removeBatchEffect for visualization-only correction, and relationship to multi-condition design matrices. Use when combining screens for joint analysis, when passage cohort confounds biology, when DepMap-style panels need Chronos with batch covariates, when picking ComBat vs RUV, or when correction harms biology and should be replaced with explicit covariate modeling.

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Use when doing any 3D work in Blender through Python or an MCP bridge, headless or in a live session: modeling, sculpting a character or head, retopology, UV unwrapping, baking, texturing, rigging, animating, previs, geometry nodes, lighting and rendering, grease pencil, or finishing an AI-generated mesh into a production asset. Also before exporting or handing off an asset, to check it is ready for a game engine, a render or a 3D print (floating parts, z-fighting, inside-out normals, unapplied scale, missing UVs). Also when a bpy script fails on Blender 5.x API changes, a brush or sculpt operator fails or crashes headless, or a result has to be judged like a professional 3D artist would.

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Recherche, cartographie et analyse des thèses de doctorat françaises via theses.fr (API ABES) et TEL (HAL). Spécialisée droit avec filtrage par sous-disciplines juridiques. Ouverte sur d'autres domaines universitaires. Capacités : (1) recherche multi-critères — sujet, directeur, jury, établissement, école doctorale, période, statut soutenue/en préparation, discipline ; (2) cartographie académique — qui dirige sur quel sujet, qui siège dans quels jurys, réseaux de co-direction, écoles doctorales productives ; (3) analyses temporelles — évolution d'un sujet, détection de saturation, calendrier des soutenances à venir ; (4) filtrage sous-discipline juridique — droit privé, public, social, affaires, international, etc. Déclencher dès que l'utilisateur mentionne thèse, doctorat, directeur de thèse, jury de thèse, école doctorale, soutenance, doctorant, ou demande une analyse d'écosystème doctoral. Pour articles ou ouvrages de doctrine, utiliser plutôt recherche-doctrine.

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Méthodologie « verification-first » pour la Cour Pénale Spéciale de Centrafrique (cour hybride, siège à Bangui). Toute citation est vérifiée (JusticeInfo, FIDH, HRW, legal-tools.org ; les sites officiels renvoient souvent une erreur 403). Couvre l'affaire Paoua, le « notamment » de l'article 3, et la complémentarité avec la CPI. Aide à la recherche, pas un conseil juridique. Fait partie de la bibliothèque open source « Skills for International Justice » — méthodologie : github.com/jeannesulzer/international-criminal-tribunals-skills

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Rédaction d'assignations en référé devant le tribunal de commerce ou des activités économiques pour le recouvrement de créances commerciales. Utiliser ce skill quand l'utilisateur demande de rédiger, préparer ou créer une assignation en référé pour obtenir le paiement d'une créance impayée (factures, prestations de services, ventes de marchandises, etc.) lorsque le débiteur n'a pas honoré ses obligations de paiement et qu'il n'existe pas de contestation sérieuse.

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模拟 CentOS Linux 7 终端行为（含 Synopsys VCS/UVM 环境），仅输出代码块结果，不进行解释。

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Fast Python environment management with uv (10-100x faster than pip). Triggers on: uv, venv, pip, pyproject, python environment, install package, dependencies.

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Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.

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SPARC (Specification, Pseudocode, Architecture, Refinement, Completion) comprehensive development methodology with multi-agent orchestration

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Implement adaptive learning with ReasoningBank for pattern recognition, strategy optimization, and continuous improvement. Use when building self-learning agents, optimizing workflows, or implementing meta-cognitive systems.

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Achieve aggressive v3 performance targets: 2.49x-7.47x Flash Attention speedup, 150x-12,500x search improvements, 50-75% memory reduction. Comprehensive benchmarking and optimization suite.

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Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.

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Core module implementation for claude-flow v3. Implements DDD domains, clean architecture patterns, dependency injection, and modular TypeScript codebase with comprehensive testing.

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15-agent hierarchical mesh coordination for v3 implementation. Orchestrates parallel execution across security, core, and integration domains following 10 ADRs with 14-week timeline.

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Complete security architecture overhaul for claude-flow v3. Addresses critical CVEs (CVE-1, CVE-2, CVE-3) and implements secure-by-default patterns. Use for security-first v3 implementation.

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CLI modernization and hooks system enhancement for claude-flow v3. Implements interactive prompts, command decomposition, enhanced hooks integration, and intelligent workflow automation.

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Deep agentic-flow@alpha integration implementing ADR-001. Eliminates 10,000+ duplicate lines by building claude-flow as specialized extension rather than parallel implementation.

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Orchestrate multi-agent swarms with agentic-flow for parallel task execution, dynamic topology, and intelligent coordination. Use when scaling beyond single agents, implementing complex workflows, or building distributed AI systems.

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Create new Claude Code Skills with proper YAML frontmatter, progressive disclosure structure, and complete directory organization. Use when you need to build custom skills for specific workflows, generate skill templates, or understand the Claude Skills specification.

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Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG systems, semantic search engines, or intelligent knowledge bases.

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MCP server optimization and transport layer enhancement for claude-flow v3. Implements connection pooling, load balancing, tool registry optimization, and performance monitoring for sub-100ms response times.

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Unify 6+ memory systems into AgentDB with HNSW indexing for 150x-12,500x search improvements. Implements ADR-006 (Unified Memory Service) and ADR-009 (Hybrid Memory Backend).

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Optimize AgentDB performance with quantization (4-32x memory reduction), HNSW indexing (150x faster search), caching, and batch operations. Use when optimizing memory usage, improving search speed, or scaling to millions of vectors.

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Domain-Driven Design architecture for claude-flow v3. Implements modular, bounded context architecture with clean separation of concerns and microkernel pattern.

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