「batch」の検索結果

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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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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作業の依存関係を整理し、独立した調査・実装・検証を並行して進めるためのスキルです。変更先の競合を避け、各作業の完了状況と確認結果を表にまとめます。

• 独立したファイル調査の一括実行
• 複数エージェントでの分担実装
• Git worktreeで変更先を分けたいとき

Submit and monitor batch inference jobs from the CLI. Upload and manage files for batch processing, retrieve results, and integrate batch pipelines with CI/CD workflows.

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Expert-level Windows batch file (.bat/.cmd) skill for writing, debugging, and maintaining CMD scripts. Use when asked to "create a batch file", "write a .bat script", "automate a Windows task", "CMD scripting", "batch automation", "scheduled task script", "Windows shell script", or when working with .bat/.cmd files in the workspace. Covers cmd.exe syntax, environment variables, control flow, string processing, error handling, and integration with system tools.

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Offensive methodology for attacking GraphQL APIs during penetration tests and bug bounty engagements. Covers the full attack lifecycle: endpoint discovery, introspection abuse and blind schema reconstruction when introspection is disabled, authentication and authorization bypass through Relay node IDs and nested object traversal, injection via variables and directives, query batching for brute force and OTP bypass, denial of service through depth bombs and alias amplification, WebSocket subscription hijacking, information disclosure through verbose errors and field suggestion oracles, and file upload abuse via the multipart GraphQL specification. Includes tool-specific guidance for InQL, graphql-cop, CrackQL, BatchQL, Altair, GraphQL Voyager, and clairvoyance. Trigger on: GraphQL, graphql, introspection query, batching attack, query depth, GraphQL injection, GraphQL IDOR, field suggestion, GraphQL auth bypass, GraphQL DoS, GraphQL security, graphql-cop, InQL, CrackQL, BatchQL, Relay node, alias amplification, subscription abuse, multipart upload GraphQL, schema enumeration, __schema, __type.

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Build Claude streaming and Message Batches API workflows. Use when implementing real-time streaming responses, SSE event handling, or processing bulk requests with the 50% cheaper Batches API. Trigger with phrases like "claude streaming", "anthropic batch", "message batches api", "SSE events anthropic", "stream claude response".

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Turn one pool of existing project media into a batch of distinct, publishable montage cuts instead of a single hero edit. Use for batch montage, one-source-many-outputs, 一源多出, 批量混剪, 批量出片, 矩阵号, 多账号分发, variant batches, deduped cuts, 去重变体, 防搬运, or when the user asks for N different versions of the same footage.

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Use when correcting batch effects in merged bulk expression matrices with sample-level batch metadata while preserving biological group structure and generating before-and-after QC plots. NOT for: single-cell integration, raw FASTQ processing, differential expression without batch labels, or datasets without biological groups.

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Structures biological experiments so inference is valid by construction, covering Fisher's principles (randomization, replication, local control), the experimental-vs-observational unit distinction and pseudoreplication (Hurlbert 1984; Lazic 2018), randomization mechanics (complete, restricted, stratified, rerandomization, run-order), blocking layouts (randomized complete block, Latin square, incomplete block), factorial designs and interactions, and the split-plot/nested error strata hidden inside multi-batch genomics. Use when deciding the experimental unit and what counts as a replicate, planning randomization and run order, choosing a blocked/factorial/split-plot/nested layout, avoiding pseudoreplication in cell-culture or animal studies, or specifying the random-effects structure of the analysis model. For assigning samples to sequencing batches/lanes/plates and batch-effect correction see experimental-design/batch-design; for regulated clinical-trial randomization see clinical-biostatistics.

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BUSseq R package fits an interpretable Bayesian hierarchical model---the Batch Effects Correction with Unknown Subtypes for scRNA seq Data (BUSseq)---to correct batch effects in the presence of unknown cell types. BUSseq is able to simultaneously correct batch effects, clusters cell types, and takes care of the count data nature, the overdispersion, the dropout events, and the cell-specific sequencing depth of scRNA-seq data. After correcting the batch effects with BUSseq, the corrected value ca

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Expert knowledge for Azure Batch development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when configuring Batch pools, autoscale, private networking, CMK encryption, or CI/CD for HPC/render jobs, and other Azure Batch related development tasks. Not for Azure Container Instances (use azure-container-instances), Azure Kubernetes Service (AKS) (use azure-kubernetes-service), Azure Functions (use azure-functions), Azure Virtual Machines (use azure-virtual-machines).

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覆盖批量任务完整生命周期：参数叉乘创建、猴子补丁钩子追踪队列提交与执行、状态聚合状态机、队列中断取消、文件清理删除。 导航时机：理解批量任务从创建到完成的全流程、调试端到端任务生命周期问题、修改任务状态转换、排查任务卡住、新增生命周期阶段或状态。 排除：结果处理与输出媒体处理（见 ../result-processing/）、资源上传管理、前端UI逻辑。 关键词: 批量任务生命周期, createBatchTask, cancelTask, deleteTask, post_prompt, task_start, task_done, BatchToolsHandler, SubTaskStatus, CommonTaskStatus, prompt_queue, 猴子补丁, 参数叉乘, 状态聚合, 队列执行, 文件清理, SQLite WAL。

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Use when building batch jobs, ETL pipelines, scheduled imports/exports, or any chunk-oriented bulk processing with Spring Batch. Covers the Spring Batch 5 / Boot 3 builder API, restartability and idempotent job parameters, reader/writer thread-safety, fault tolerance, and chunk transaction boundaries.

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Use when building batch jobs, ETL pipelines, scheduled imports/exports, or any chunk-oriented bulk processing with Spring Batch. Covers the Spring Batch 6 / Boot 4 builder API, resourceless vs JDBC job repositories, restartability and idempotent job parameters, reader/writer thread-safety, fault tolerance, and chunk transaction boundaries.

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Triage the Gaia issue backlog and skill-batch intake queue. Use this skill when someone asks to: "triage issues", "clean up the issue tracker", "review the backlog", "close stale issues", "process the intake queue", "review skill-batches", "evaluate draft skill proposals", "approve or reject pending skills", "is this issue still valid?", "what's clogging the backlog?", "run a triage pass", or /gaia-triage. Covers two workstreams: (1) GitHub issue lifecycle — identifying resolved, stale, or need-more-info issues and acting on them via gh CLI; (2) skill-batch intake — evaluating draft proposals in registry-for-review/skill-batches/ and routing them toward promotion or rejection. This is the gatekeeping step before new skills enter the canonical registry; and (3) a cross-repo triage sweep — sizing, P0-P4 prioritization, umbrella/epic synthesis, and assignment across every gaia-research org repo, on the one headquarters tracker.

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Analyze pharmaceutical batch production records for yield optimization, process parameter tuning, deviation trending, and cycle time reduction under cGMP compliance. Triggers: phrases: "optimize batch yield", "analyze batch records", "pharma manufacturing analysis".

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。

Structures biological experiments so inference is valid by construction, covering Fisher's principles (randomization, replication, local control), the experimental-vs-observational unit distinction and pseudoreplication (Hurlbert 1984; Lazic 2018), randomization mechanics (complete, restricted, stratified, rerandomization, run-order), blocking layouts (randomized complete block, Latin square, incomplete block), factorial designs and interactions, and the split-plot/nested error strata hidden inside multi-batch genomics. Use when deciding the experimental unit and what counts as a replicate, planning randomization and run order, choosing a blocked/factorial/split-plot/nested layout, avoiding pseudoreplication in cell-culture or animal studies, or specifying the random-effects structure of the analysis model. For assigning samples to sequencing batches/lanes/plates and batch-effect correction see experimental-design/batch-design; for regulated clinical-trial randomization see clinical-biostatistics.

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Spark, pandas, polars, DuckDB optimization for batch data processing. Activate on: batch processing, Spark optimization, polars, DuckDB, pandas performance, data frame, shuffle, partition, memory optimization. NOT for: streaming pipelines (use streaming-pipeline-architect), warehouse queries (use data-warehouse-optimizer).

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。

Structures biological experiments so inference is valid by construction, covering Fisher's principles (randomization, replication, local control), the experimental-vs-observational unit distinction and pseudoreplication (Hurlbert 1984; Lazic 2018), randomization mechanics (complete, restricted, stratified, rerandomization, run-order), blocking layouts (randomized complete block, Latin square, incomplete block), factorial designs and interactions, and the split-plot/nested error strata hidden inside multi-batch genomics. Use when deciding the experimental unit and what counts as a replicate, planning randomization and run order, choosing a blocked/factorial/split-plot/nested layout, avoiding pseudoreplication in cell-culture or animal studies, or specifying the random-effects structure of the analysis model. For assigning samples to sequencing batches/lanes/plates and batch-effect correction see experimental-design/batch-design; for regulated clinical-trial randomization see clinical-biostatistics.

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。