「batch」の検索結果

Plan, create, and configure production-ready Google Kubernetes Engine (GKE) clusters using the golden path Autopilot configuration. Covers Day-0 checklist, Autopilot vs Standard, networking (private clusters, VPC-native, Gateway API), security (Workload Identity, Secret Manager, RBAC hardening), observability, scaling, cost optimization, and AI/ML inference. WHEN: create GKE cluster, provision GKE environment, design GKE networking, secure GKE, optimize GKE cost, GKE autoscaling, GKE inference, GKE upgrade, GKE observability, GKE multi-tenancy, GKE batch, GKE HPC, GKE compute class.

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Add a text watermark to one or multiple PDFs by uploading them to the Solutions API, polling until completion, then returning download URL(s) for the watermarked PDF(s) (or a ZIP if multiple).

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Render Aavegotchi assets by deriving renderer hashes from Goldsky Base core data and calling POST /api/renderer/batch on www.aavegotchi.com. Use when the user gives a tokenId or inventory URL, or when deterministic hash plus image artifacts are required.

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Render Aavegotchi assets by deriving renderer hashes from Goldsky Base core data and calling POST /api/renderer/batch on www.aavegotchi.com. Use when the user gives a tokenId or inventory URL, or when deterministic hash plus image artifacts are required.

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Operate Aavegotchi Gotchiverse player workflows on Base mainnet (8453): alchemica channeling, surveying and harvesting, crafting installations/tiles, building on parcels (equip/unequip/move/batch equip), installation upgrades, craft/upgrade queue management, and parcel access-right management. Use when interacting with Realm/Installation/Tile diamonds via subgraph-first discovery and onchain verification/execution with Foundry cast.

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Automate SEO research with Ahrefs -- analyze backlink profiles, research keywords, track domain metrics history, audit organic rankings, and perform batch URL analysis through the Composio Ahrefs integration.

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Coordinate large-scale codebase migrations and multi-file refactors across hundreds of files. Manage framework upgrades, API renames, and config rewrites using batched PRs, issue tracking, and CI verification.

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Execute Jupyter notebooks using local scripts or MCP Jupyter. Arguments: <notebook_path> [--mode] [--cells] [--timeout] [--batch] [--save]

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Reliable progress watching for long-running background commands (training runs, live audits, batch jobs, CI) without losing output to stdout buffering. Prefer post-completion cat-and-grep over tail -F.

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NPU 图批量下发能力（aclgraph / aclgraph_ex 家族）的开发与调优。覆盖 mindiesd 的 aclgraph_backend：NPUGraph 静态 capture、全局 graph pool、 lazy capture、专用 copy stream + event 管线、shape/dtype 校验、max_entries 驱逐。 当用户需要减少 host launch 开销、静态 shape 大 batch 场景加速、 或排查 NPUGraph replay 输入不匹配问题时使用此 skill。 即使用户只提"批量下发""graph capture""图捕获"而未说 aclgraph，也应触发； pattern/Inductor 融合（default 后端）见 pattern-dev，算子本体见 operator-dev， 本技能只覆盖图批量下发。由 dev-workflow 的编译开发阶段与 model-auto-optimization 的 图下发场景指引加载。

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融合单元的范围判定与收益前置评估：判定「哪些计算该被融进同一个融合单元、边界画在哪、 融了值不值」。结构侧按融合单元构造规则切边界——函数边界即候选组；FA（含 BSA 等变种）为 禁融锚点；以 norm / rope / FA 为分界分区，全 Vec 区域优先试融；区域内有 Cube 时以 Cube 为 首算子向后包裹 Vec（Σvec 小于 cube 或遇下一 Cube 封口）；一轮之后纯 Vector 单元之间再迭代； MLP / MoE 以矩阵乘族（MatMul / BatchMatMul / GroupedMatmul）为界；若该段计算已由单个 融合算子承载，按规则 6 先确认载体是否已生效（不重复建单元）。 收益侧按计算单元利用率判型（memory_bound、*_vec|mac|mte2|mte3_ratio、cube_utilization）， 叠加带宽下限 / 区域占比 / 天花板法 / 地板先行 / Amdahl 传导校验给出 go/no-go。 当用户问「这几个算子能不能融成一个」「融合边界画在哪」「融多大范围」「融了值不值得」， 或 profiling-analyze 给出融合机会候选需要定范围与收益时使用；即使用户只说「这条链要不要 合成一个 kernel」「这里能不能省一次读写」也应触发。 **已经做完、实测收益却打平或为负时同样触发**——此时的任务不是接受"融合无收益"， 而是按事后复核做**等功忙计数对照**归因，并在缺陷落在融合核自身时**升级 operator-dev**。 边界：只出「融合单元草案 + 收益判定 + 否决理由」。不做采纳与选点（dit-perf-opt 依收益选 top 并派活）、不做采集与瓶颈定位（profiling-collect / profiling-analyze）、不写 kernel、不写 pattern、 不做使能与并行选型、不定义验收标准（引 accuracy-gate / perf-gate）。 由 profiling-analyze（融合机会候选）与 operator-dev（实现前的收益评估）调用。

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Extracts structured data from large sets of legal documents into tabular format for review, analysis, and reporting. Processes contracts, agreements, correspondence, filings, and other legal documents in bulk — extracting key terms, dates, parties, obligations, risks, and custom fields into organized tables. Use when conducting due diligence document review, bulk contract extraction, compliance audits across document sets, lease portfolio analysis, employment agreement review, regulatory filing review, or any task requiring structured extraction from multiple documents. Trigger keywords: tabular review, bulk extraction, document review table, data room review, batch document analysis, contract extraction, portfolio review, structured extraction, document comparison table.

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AI合同风控官 - 智能合同审查与法律风险检测专家级工具。支持多国法律体系（中国/美国/英国/新加坡/德国/日本/澳大利亚/加拿大/欧盟），可批量处理合同文档。 当用户需要：合同审查、合同风险检测、合同条款分析、法律文书审核、采购合同审核、销售合同审查、 劳动合同检查、合作协议评审、违约风险提示、合同比对、法务辅助、合同审计、批量合同审核、 外贸合同审查、国际合同合规、NDA保密协议审核、服务合同审查、租赁合同检查、股权转让协议审核、 借款担保合同审查 时触发此技能。

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Arm proactive 5-hour usage protection for long Claude Code sessions (batch, subagents, high window % at start). Daemon reads account-level 5-hour window usage (not per-session). User types /usage-guard at session start. Re-arm every sitting. If user mentions usage-guard by name, read this file and run arm.sh.

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Rapidly generate up to 25 distinct ad creative hypotheses for testing. Produces a matrix of angles (pain, benefit, social proof, urgency, curiosity, comparison) x formats (static, carousel hook, UGC-style, bold-text overlay) with headline, visual prompt, and rationale for each, then generates the visuals with any image tool. Use when the user wants "ad variations", "ad concepts", "creative testing batch", "give me 25 ads", or paid-social creative ideation. Part of the Creative Production set.

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Translate books, documents, AiNiee projects, Unity localization caches, or flat MTool JSON dictionaries end to end on the coding agent itself without the AiNiee app. Use for requests such as "用 agent 翻译这本书", "把 epub 翻译了", "翻译 Unity CSV/cache", "把这个 MTool JSON 翻译了", or "AiNiee-style translation" that need a locked glossary, resumable batches or bounded subagent groups, terminology consistency, source-aware QA, validated cache writeback, and export. For MTool, preserve the left JSON key as immutable source text and write only the right value as translation.

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Expert data engineer for ETL/ELT pipelines, streaming, data warehousing. Activate on: data pipeline, ETL, ELT, data warehouse, Spark, Kafka, Airflow, dbt, data modeling, star schema, streaming data, batch processing, data quality. NOT for: API design (use api-architect), ML training (use ML skills), dashboards (use design skills).

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Apply recognition-primed decision making to agents operating under time pressure, uncertainty, and ambiguous cues. Use for situation assessment, RPD-style action selection, and decision-support design in operational settings. NOT for batch optimization, static planning with full information, or tasks where exhaustive search is cheap and harmless.

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Expert in background job processing with Bull/BullMQ (Redis), Celery, and cloud queues. Implements retries, scheduling, priority queues, and worker management. Use for async task processing, email campaigns, report generation, batch operations. Activate on "background job", "async task", "queue", "worker", "BullMQ", "Celery". NOT for real-time WebSocket communication, synchronous API calls, or simple setTimeout operations.

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Data structures and serialization formats for agent-to-agent communication. Covers message envelopes, structured output schemas, capability declarations, task handoff payloads, error/retry signaling, and context windows as data structures. Deep comparison of A2A protocol, MCP, OpenAI function calling, and LangChain message types. Teaches when to use rigid schemas vs free-form with validation, typed vs untyped, streaming vs batch. Activate on: "agent message format", "agent communication schema", "agent-to-agent protocol", "A2A protocol", "MCP message format", "structured output for agents", "agent interop", "interchange format", "agent serialization", "task handoff format", "capability declaration". NOT for: what agents say to each other (use agent-conversation-protocols), orchestration topology (use multi-agent-coordination), building agent infrastructure (use agentic-infrastructure-2026).

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Differential abundance (DA) and differential state (DS) analysis for flow and mass cytometry - tests which cell populations change in frequency or marker expression between conditions using diffcyt (edgeR/voom/GLMM for DA, limma/LMM for DS), with cydar, CITRUS, and compositional methods (sccomp, scCODA, DCATS) as alternatives. Covers the sample-is-the-experimental-unit principle, design/contrast and mixed-model formulas, compositionality of cluster proportions, and FDR across clusters. Use when comparing populations between groups, choosing a DA method, handling paired/batch designs, or deciding whether compositional correction is needed.

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Aligns and QCs methylated-RNA-immunoprecipitation (MeRIP / m6A-seq) IP and input libraries using STAR or HISAT2 splice-aware mapping, samtools sort/index, IP/input matched-pair tracking, antibody-lot metadata recording, replicate concordance via deepTools multiBamSummary + plotCorrelation, IP enrichment QC via plotFingerprint and per-transcript IP/input ratio distributions, library-complexity saturation curves via PreSeq, and the explicit do-NOT-deduplicate convention for standard non-UMI MeRIP. Use when preparing paired IP and input BAM files for exomePeak2 / MeTPeak / MACS3 peak calling, evaluating MeRIP replicate concordance and IP enrichment, deciding whether to deduplicate (standard MeRIP typically NOT), choosing genome-vs-transcriptome alignment for downstream peak vs m6Anet workflows, recording antibody clone and lot metadata for cross-batch reconciliation, detecting failed IPs via saturation curves and IP/input distribution shape, or generating IP-over-Input bigWig tracks for visualisation.

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Quality control for flow, spectral, and mass cytometry - time-based anomaly cleaning (flowAI, flowCut, PeacoQC, flowClean), margin/boundary event removal, signal-drift detection, dead-cell exclusion, CyTOF Gaussian/DNA/event-length checks, instrument calibration/standardization (MESF, CS&T, peak-2), and batch-level outlier flagging. Use when assessing acquisition quality, choosing a cleaning tool, ordering QC relative to compensation, deciding margin removal before density-based steps, or flagging problematic samples before clustering or differential analysis.

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Performs differential expression on bulk RNA-seq count data with DESeq2's negative-binomial GLM, Wald and LRT testing, apeglm/ashr/normal LFC shrinkage, independent filtering, Cook's outlier handling, VST/rlog transforms, and design formulas including paired, batch, and interaction terms. Use when running bulk DE, choosing DESeq2 over edgeR or limma-voom, building a paired or interaction design, applying LFC shrinkage for ranking or GSEA, choosing Wald vs LRT, troubleshooting padj=NA, picking VST vs rlog, importing salmon/kallisto via tximport, or analyzing prokaryotic RNA-seq.

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