「arm」の検索結果

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.

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。

Identify recurrent and driver copy number alterations across a tumor cohort with GISTIC2 (G-score, Ziggurat deconstruction, focal vs broad/arm-level analysis, q-values from permutation) and quantify copy-number signatures with the Steele 2022 COSMIC framework and the Drews 2022 CINSignatures framework. Covers driver-gene localization from recurrence peaks, distinguishing focal drivers from arm-level passengers, and the caller-sensitivity caveats of copy-number signatures. Use when finding recurrently amplified or deleted regions in a cohort, localizing driver genes, separating focal from broad events, running GISTIC2, or extracting copy-number mutational signatures.

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Deploys and operates a LiveKit agent in production: shipping a version to LiveKit Cloud and rolling it back, secrets and configuration, the worker process model and prewarming, safe async inside worker processes, provider timeouts and degradation, graceful shutdown, SDK upgrades, and observability. Use when the user says "deploy my agent", "roll back the deployment", "tail the agent logs", "the first call after a restart is slow", "attached to a different loop / event loop is closed", "prewarm the VAD", "shut down without dropping calls", "upgrade livekit-agents safely", "correlate logs by session", or is changing an agent codebase that is already live. Not for designing the agent (building-livekit-agents) or reproducing one bad conversation locally (debugging-livekit-agents).

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AWS CloudWatch monitoring for logs, metrics, alarms, and dashboards. Use when setting up monitoring, creating alarms, querying logs with Insights, configuring metric filters, building dashboards, or troubleshooting application issues.

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。

Deploy Azure resources with ARM templates and Bicep. Create modular deployments and manage dependencies. Use when deploying Azure-native IaC.

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。

Only to be triggered by explicit super-swarm-spark commands.

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。

Assistant juridique expert en droit français. Recherches, consultations, rédaction d'actes, contre-argumentaires, analyses de contrats et de pièces, veille, vérification et harmonisation de références. Produit des documents Word (computer use) ou des réponses conversationnelles structurées. MCP : OpenLegi, Themia, LegalDataHunter. Déclencher pour toute question juridique, tout concept de droit français (contrat, responsabilité, jurisprudence, code civil, tribunal, indemnisation), toute question sur le droit de l'UE, la CJUE ou la CEDH, et toute demande de vérification ou harmonisation de références bibliographiques juridiques.

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Expert blueprint for TPS/hybrid shooters: soft-lock aim assist, cover validation rays, SpringArm TPS camera, and genre routing to FPS sibling for viewmodel/hitscan feel. Use for third-person/cover shooters and hybrids. Keywords: TPS, soft_lock, cover, SpringArm3D, hybrid shooter, aim assist — not FPS-only movement.

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Analyze the current session and propose improvements to skills. **Proactively invoke this skill** when you notice user corrections after skill usage, or at the end of skill-heavy sessions. Also use when user says "reflect", "improve skill", or "learn from this".

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 code review for diffs. Analyzes changed code for security vulnerabilities, anti-patterns, and quality issues. Auto-detects domain (frontend/backend) from file paths.

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。

Server-specific best practices for FastAPI, Celery, and Pydantic. Extends python-skills with framework-specific 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。

Guidelines for creating temporary files in system temp directory. Use when agents need to create reports, logs, or progress files without cluttering the repository.

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。

Shared TypeScript best practices for Designer and Electron subsystems.

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Commit changes, push to GitHub, and open a PR. Includes quality checks (security, patterns, simplification). Use --quick to skip 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。

Generate specialized skills for each subsystem in the monorepo. Creates shared language skills and subsystem-specific checklists for high-quality AI code generation.

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。

Universal Runtime best practices for PyTorch inference, Transformers models, and FastAPI serving. Covers device management, model loading, memory optimization, 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。

Fetch GitHub CI failure information, analyze root causes, reproduce locally, and propose a fix plan. Use `/fix-ci` for current branch or `/fix-ci <run-id>` for a specific run.

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。

RAG-specific best practices for LlamaIndex, ChromaDB, and Celery workers. Covers ingestion, retrieval, embeddings, and performance.

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 GitHub release orchestration with AI swarm coordination for automated versioning, testing, deployment, and rollback management

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。

Advanced GitHub Actions workflow automation with AI swarm coordination, intelligent CI/CD pipelines, and comprehensive repository management

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 GitHub code review with AI-powered swarm coordination

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 GitHub project management with swarm-coordinated issue tracking, project board automation, and sprint planning

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。

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.

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。

Programmatic inbox management for Smartlead. Enable/disable warmup with correct ramp settings, set signatures in bulk, tag inboxes (active vs insurance), and pull inbox health dashboards. Use after creating a new batch of inboxes via /zapmail-domain-setup-public, or when managing an existing Smartlead account at scale. Triggers on "turn on warmup", "set signatures", "tag inboxes", "inbox health", "set up new inboxes".

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。