インフラ・DevOpsのスキル

Deploy web apps with backend APIs, database, and file storage. Use when the user asks to deploy or publish a website or web app and wants a public URL. Uses HTTP API via curl.

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。

Optimize end-to-end application performance with profiling, observability, and backend/frontend tuning. Use when coordinating performance optimization across the stack.

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。

Integracao completa com Amazon Alexa para criar skills de voz inteligentes, transformar Alexa em assistente com Claude como cerebro (projeto Auri) e integrar com AWS ecosystem (Lambda, DynamoDB, Polly, Transcribe, Lex, Smart Home).

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Use when a coding task should be driven end-to-end from issue intake through implementation, review, deployment, and acceptance verification with minimal human re-intervention.

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。

Email infrastructure for AI agents. Create accounts, send/receive emails, manage webhooks, and check karma balance via the AgentMail API.

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。

Build container-based Foundry Agents with Azure AI Projects SDK (ImageBasedHostedAgentDefinition). Use when creating hosted agents with custom container images in Azure AI Foundry.

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。

Build persistent agents on Azure AI Foundry using the Microsoft Agent Framework Python SDK.

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Creates DE-specific diagnostic and result visualizations using DESeq2/edgeR built-in functions and lightweight ggplot2 wrappers. Covers MA plot (with the shrunken-LFC compression effect), volcano (with the apeglm caveat that p-values are unchanged), PCA on VST/rlog (never raw counts), sample distance heatmaps, top-DE-gene heatmaps with the row-scaling trap, dispersion / BCV plot interpretation, p-value histogram diagnostics, plotCounts for individual genes, blind=TRUE vs FALSE rationale, and the n=3 visualization stake. Use when generating DE diagnostic plots, choosing VST vs rlog for visualization, troubleshooting suspicious plot patterns (shifted MA cloud, batch-dominated PCA, anti-conservative p-value histogram), or building a standard QC figure panel.

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Stores and operates on sparse expression matrices for single-cell and large bulk RNA-seq, covering dgCMatrix/dgRMatrix/dgTMatrix when-each-is-fast, the dgCMatrix (CSC, R) <-> CSR (Python) implicit transpose, AnnData (cells-rows) <-> SingleCellExperiment (cells-cols) orientation flip, HDF5/h5ad vs Zarr cloud-native shift, HDF5SummarizedExperiment + DelayedArray for out-of-memory bulk, scanpy backed mode for large h5ad, the ~10-15% density crossover where dense beats sparse, 10X format proliferation (MTX vs CellRanger H5 vs h5ad), the dense-conversion memory blow-up, and Dask + Zarr for consortium-scale matrices. Use when choosing sparse format, working with single-cell-sized matrices, importing/exporting 10X, debugging R/Python interop transposes, processing matrices too large for RAM, or building cloud-native pipelines.

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Estimate bivariate genetic correlation (rg) between traits from GWAS summary statistics or individual-level genotypes using cross-trait LDSC, HDL, LAVA, rho-HESS, GREML-bivariate, Popcorn, and HDL-L. Use when quantifying shared genetic architecture between two traits, screening MR validity before causal inference, distinguishing global from locus-level rg, estimating trans-ancestry rg, separating partial from full causation via LCV gcp, or producing a STROBE-MR-compliant cross-trait sensitivity battery. Cross-trait LDSC intercept absorbs sample overlap and is NOT a bias; HDL is biased under sample overlap above ~5%. High rg between exposure and outcome motivates CHP-aware MR sensitivity (CAUSE, LHC-MR).

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Use this skill when the user asks about workers compensation for trucking — required by state law, classification of drivers, premium calculation (rate × experience modifier × payroll), Common Claim types in trucking, returning injured drivers to work, claims management, premium reduction strategies, and the difference between WC and Occupational Accident insurance for owner-operators. Reference state WC laws + IFTA / fleet insurance.

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。

Use this skill when an insulin-dependent diabetic driver needs commercial driving certification. Covers the FMCSA insulin variance program, application steps, and continued monitoring.

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Use this skill when the user asks about FMCSA financial assistance + grants — Motor Carrier Safety Assistance Program (MCSAP), High Priority Grants, CMV Operator Apprenticeship Program (CMV-DAP), workforce development funding, hazmat preparedness grants, small carrier financial relief, and state-level grants for trucking infrastructure. Reference fmcsa.dot.gov/grants.

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。

Use this skill when the user asks about Motor Vehicle Records (MVRs), 49 CFR 391.25 annual MVR review, continuous MVR monitoring services (SambaSafety, Foley, etc.), state MVR fees, what to look for on an MVR, CDLIS (Commercial Driver License Information System), or how often MVRs must be pulled. Cite 49 CFR 391.25 and 391.27.

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Use this skill to evaluate whether to switch from annual MVR pulls to continuous monitoring. Covers cost-benefit, intervention timing, and major providers.

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Use when a carrier asks about electric trucks — Tesla Semi, Freightliner eCascadia, Volvo VNR Electric, Peterbilt 579EV, range, charging, total cost of ownership, regulatory pushes (CARB ACT, ACF), incentives, infrastructure, depot vs en-route charging, and which routes/applications are EV-ready today.

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Use this skill when the user asks how to pay CDL drivers — cents per mile (CPM), percentage of revenue, hourly, salary, sleeper-team rate, detention pay, driver-pay laws under FLSA (Fair Labor Standards Act), and how each model affects retention + recruitment. Cite FLSA + state wage laws.

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。

Recommend, create, preview, and attach Coval trace-based metrics from OpenTelemetry spans. Use when a user has Coval traces and wants custom trace metrics, trace-aware LLM judge metrics with include_traces, latency, token usage, provider failures, tool behavior, vertical-specific workflow signals, or production monitoring based on trace attributes.

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Improve Coval trace quality after basic ingestion works. Use when traces are sparse, missing useful STT/LLM/TTS/tool spans, missing attributes needed for Coval built-in metrics, or when a customer wants maximum debugging and observability value from agent traces.

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。

Use when modifying anything under features/files/, app/(core)/files/, app/(public)/share/, or any caller that uploads, downloads, lists, moves, renames, shares, or previews user files.

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The doctrine for turning a vision into a finished, verified, deployed system with near-zero owner involvement. Use when launching, coordinating, resuming, or working any large build, module, campaign, or multi-agent project, or when writing its launch prompt.

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Permanent-delete grooming for leftover Docker builds, agent worktrees, and .next caches. Use when creating or finishing a git worktree, after a local docker build, when the disk is full, or when /tmp or .wt trees were left behind.

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Deploy applications and websites to Vercel using the bundled `scripts/deploy.sh` claimable-preview flow. Use when the user asks to deploy to Vercel, wants a preview URL, or says to push a project live on Vercel.

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。

This skill should be used when the user asks to "sandboxの互換性をチェックして", "sandbox設定を確認して", "このプロジェクトでsandboxが使えるか調べて", "sandboxでコマンドが失敗する", "check sandbox compatibility", "verify sandbox settings", "sandbox is blocking my commands", or wants to ensure their development workflow works within Claude Code's sandbox restrictions — either proactively before starting work or reactively after encountering sandbox-related failures.