'batch' 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 monitoring, diagnosing, or managing Batch Apex, Scheduled Apex, Queueable, and Flow scheduled jobs: Setup > Apex Jobs, AsyncApexJob queries, concurrent limits, failure detection, and notification patterns. NOT for writing the Batch or Schedulable class itself — use apex/batch-apex-patterns or apex/apex-scheduled-jobs. NOT for BatchApexErrorEvent alerting in code — use apex/scheduled-apex-failure-detection-and-monitoring. Trigger keywords: Setup > Scheduled Jobs, Apex Flex Queue, CronTrigger, CronJobDetail, FlowInterview, System.abortJob, System.schedule, CRON expression, Holding status, BLOCKED state, reschedule after sandbox refresh, scheduled job runbook.

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Guide AAS maintainers through pull request review, batch merging, generated-file synchronization, and scripted releases. Follow protected-branch procedures and check evidence tied to the exact revision before accepting changes.

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• Reviewing and merging a batch of pull requests
• Synchronizing generated files after source changes
• Preparing and publishing a protected release

Create, debug, and maintain Windows batch scripts for file operations, backups, and scheduled tasks. Covers command arguments, variables, loops, and error handling, with starter templates for command-line tools and reusable functions.

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• Automating file copies and backups
• Creating scripts for Task Scheduler
• Debugging variable expansion and quoting

Build transcription workflows for uploaded or stored audio and video using Zoom AI Services Scribe. Consult guidance on authentication, single-file requests, batch jobs, completion notifications, and saving transcript results for further processing.

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• Adding transcription for uploaded recordings
• Transcribing stored call archives in batches
• Saving transcripts after job completion notifications

Batch effect correction for CRISPR screens. Covers normalization across batches, technical replicate handling, and batch-aware analysis. Use when combining screens from multiple batches or correcting systematic technical variation.

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Remove batch effects from RNA-seq data using ComBat, ComBat-Seq, limma removeBatchEffect, and SVA for unknown batch variables. Use when correcting batch effects in expression data.

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Bead-based signal normalization and cross-batch harmonization for CyTOF and high-parameter cytometry - EQ four-element bead normalization of instrument sensitivity drift (CATALYST normCytof, premessa), and reference-anchor cross-batch normalization (CytoNorm, per-cluster quantile splines). Covers the distinction between within-run drift correction and between-batch correction, the mandatory anchor/reference sample, why normalization is per-cluster with many quantiles, and the over-correction risk. Use when correcting CyTOF signal drift, harmonizing multi-batch or multi-site studies, or deciding whether to normalize data versus model batch in the design.

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This skill should be used when the user asks to "batch LLM requests", "should I use the batch API", "estimate batch vs realtime cost", "design a bulk LLM job", or "process thousands of prompts cheaply".

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Remove batch effects from RNA-seq data using ComBat, ComBat-Seq, limma removeBatchEffect, and SVA for unknown batch variables. Use when correcting batch effects in expression data.

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Orchestrator skill that takes a warp-apply receipts directory and proposes each batch to its corresponding multisig signer — Safe Transaction Service via `safes/propose-warp-batch.ts` for EVM batches (auto-detects governance context: AW / Foundation / Regular / etc. per filename), Squads via `squads/propose-warp-batch.ts` for SVM batches. Persists a per-ticket proposal summary at `~/.hyperlane/proposals/ticket-id.yaml` so the human can track signing progress in Heimdall or Squads.

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Turn a brand's positioning into a 30-day, multi-platform content batch — 3-7 pillars, subtopic matrix mapped to funnel stage and persona, pillar pieces plus derivative fan-out, proof elements in at least half the slots, all registered in the editorial calendar store and quality-gated. Use when "content batch", "batch my content", "30 days of content", "month of content", "content batching machine", "content pillars", "fill the content calendar", "plan a month of posts", or any request to produce a full month of multi-platform content in one run.

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Parallel SDLC batch runner. Analyzes a list of independent task descriptions, groups them by heuristic file-conflict prediction, dispatches one worktree-isolated background agent per task (each running the full pipeline-orchestrator: BA → Dev → QA → Sec → Docs), and aggregates results into a single batch summary. Use when: - `/sdlc:batch` invokes this skill after parsing its arguments (Step 2 of commands/batch.md) - You have 2+ independent feature/fix descriptions to run through the SDLC pipeline in parallel Do NOT use for: - A single task (use pipeline-orchestrator directly via /sdlc:start) - Tasks that must share a single working tree (this skill isolates each task in its own git worktree)

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Bead-based signal normalization and cross-batch harmonization for CyTOF and high-parameter cytometry - EQ four-element bead normalization of instrument sensitivity drift (CATALYST normCytof, premessa), and reference-anchor cross-batch normalization (CytoNorm, per-cluster quantile splines). Covers the distinction between within-run drift correction and between-batch correction, the mandatory anchor/reference sample, why normalization is per-cluster with many quantiles, and the over-correction risk. Use when correcting CyTOF signal drift, harmonizing multi-batch or multi-site studies, or deciding whether to normalize data versus model batch in the design.

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Bead-based signal normalization and cross-batch harmonization for CyTOF and high-parameter cytometry - EQ four-element bead normalization of instrument sensitivity drift (CATALYST normCytof, premessa), and reference-anchor cross-batch normalization (CytoNorm, per-cluster quantile splines). Covers the distinction between within-run drift correction and between-batch correction, the mandatory anchor/reference sample, why normalization is per-cluster with many quantiles, and the over-correction risk. Use when correcting CyTOF signal drift, harmonizing multi-batch or multi-site studies, or deciding whether to normalize data versus model batch in the design.

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Bead-based signal normalization and cross-batch harmonization for CyTOF and high-parameter cytometry - EQ four-element bead normalization of instrument sensitivity drift (CATALYST normCytof, premessa), and reference-anchor cross-batch normalization (CytoNorm, per-cluster quantile splines). Covers the distinction between within-run drift correction and between-batch correction, the mandatory anchor/reference sample, why normalization is per-cluster with many quantiles, and the over-correction risk. Use when correcting CyTOF signal drift, harmonizing multi-batch or multi-site studies, or deciding whether to normalize data versus model batch in the design.

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Agent fallback for CBaseCombatCharacter_OnKilled (auto-generated, category: func). Locate CBaseCombatCharacter::OnKilled in the CS2 server module via IDA Pro MCP and emit a fresh, minimal-unique artifact. The deterministic preprocessor could not resolve this symbol on the current gamever - your job is the re-sign. Trigger: CBaseCombatCharacter_OnKilled, CBaseCombatCharacter::OnKilled

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Organize a task into parallel work streams with clear dependencies, separate places for changes, and explicit checks. Batch independent reads and tests, coordinate concurrent work, and report completed or blocked tasks with verification results.

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• Batching independent file reads and searches
• Splitting implementation across multiple agents
• Separating changes with Git worktrees

Prepare a selected batch of pull requests, or proposed code changes, by reviewing overlaps, diagnosing conflicts, and preserving contributor credit. Check the exact version and required authorization before each merge, then report completed work and remaining holds.

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• Prepare a selected batch of pull requests
• Investigate conflicts between proposed changes
• Check validation and authorization before merging

Add clear runtime reporting to Python, PowerShell, or Node.js batch scripts. Design start messages, progress counts, item failures, and final summaries while preserving existing result output, error handling, and exit codes, then verify visibility during execution.

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• Add progress reporting to long-running batch scripts
• Report individual failures and final outcome counts
• Keep progress logs separate from machine-readable results

Deploy language models with vLLM through an OpenAI-compatible API or offline batch processing. Tune response speed, throughput, and GPU memory use with quantization, which reduces numerical precision, and execution across multiple GPUs.

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• Deploy an OpenAI-compatible language model API
• Generate text from many prompts in a batch
• Serve quantized models with limited GPU memory

Configure LLM inference and serving on NVIDIA GPUs with TensorRT-LLM. Covers quantization, which reduces numerical precision, batching requests during generation, and distributing models across GPUs to improve processing capacity and response times.

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• Setting up an LLM server on NVIDIA GPUs
• Configuring FP8 or INT4 quantization
• Distributing large models across multiple GPUs

Integrate the Gemini API on Agent Platform using the Google Gen AI SDK. Guidance covers authentication, text and multimedia tasks, structured JSON responses, live voice interactions, context caching, and batch prediction across five programming languages.

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• Adding document and multimedia understanding to an application
• Generating JSON that matches a defined schema
• Building live voice and video interactions

Build data processing pipelines for machine learning, from preprocessing to batch predictions. Uses Ray Data to handle datasets larger than memory, distribute work across CPUs and GPUs, and feed data into training frameworks.

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• Preprocessing datasets larger than memory
• Running batch predictions across multiple GPUs
• Preparing image and tabular data for training