「env」の検索結果

ALWAYS activate when the user's query involves Docker in any way — even if it also matches other skills. If the words docker, Dockerfile, docker-compose, compose.yml, container, or image appear in the query, this skill MUST be used. Covers: writing or editing Dockerfiles and compose files, adding services (postgres, redis, etc.) to compose, volume mounts and data persistence, docker build failures (layer caching, npm install issues), healthchecks and service startup ordering (depends_on), environment variables in containers, port mapping, container crashes and exit codes (OOM/137), non-root users, multi-stage builds, image optimization, .dockerignore, and deploying to container runtimes. Takes priority over general implementation or debugging skills when Docker infrastructure is the subject.

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。

Investigate unexpected behavior and mysterious bugs. Use when the cause of a problem is unknown and the user needs to understand WHY something is happening — symptoms like: sudden unexplained changes in metrics or behavior, works locally but not in staging/production, inconsistent or intermittent failures, correct code producing wrong results, operations succeeding but having no effect, environment-specific failures, duplicate executions, stale data, or any "why did this change?" or "why is this happening?" situation. Covers infrastructure anomalies (cache hit rates dropping, latency spikes, queue behavior shifts) as well as code bugs. The key signal is confusion about root cause, not a request to implement a known fix. Do NOT use for feature requests, known fixes, planning, or documentation tasks.

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。

Align Python version and repo-declared dependencies (requirements.txt / environment.yml) before installing packages for NLP research code reproduction.

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。

CRE Due Diligence analysis suite — 7 specialist skills for multifamily property analysis including rent roll validation, expense benchmarking, market study, physical inspection, environmental review, title review, and tenant credit assessment.

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。

Neo4j Visualization Library (NVL) — framework-agnostic graph rendering for the browser. Covers @neo4j-nvl/base (NVL class, nodes/relationships, Canvas vs WebGL renderer), @neo4j-nvl/interaction-handlers (ZoomInteraction, PanInteraction, DragNodeInteraction, ClickInteraction, HoverInteraction, BoxSelectInteraction, LassoInteraction, KeyboardInteraction), and @neo4j-nvl/react (InteractiveNvlWrapper, BasicNvlWrapper, StaticPictureWrapper). Use when rendering a Neo4j graph in a browser, feeding driver results through nvlResultTransformer, choosing Canvas vs WebGL, wiring node/relationship click/hover/drag handlers, or embedding NVL in React, Vite, or vanilla JS apps. Does NOT handle Cypher query authoring — use neo4j-cypher-skill. Does NOT handle driver lifecycle, sessions, or executeQuery setup — use neo4j-driver-javascript-skill. Does NOT handle GraphVisualization/Needle default embed — use @neo4j-ndl/react.

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 a change trains a model, reports a metric, or touches seeds, data or model versions, the lockfile, MLflow or DVC — what every run must log, seeding, environment pinning, data versioning and keeping tests off the shared tracking server

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 a case needs data, a user/role, or a third-party dependency - seeding through the product first, test credentials, namespacing on shared environments, and stubbing an external HTTP service by hand

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 before booting anything - choosing local (start_task_preview or a workspace boot), the task's preview or stage, confirming the build is the PR head, and what to do when nothing can 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。

Built-in deploy recipes and deploy targets. Use when a task asks you to set up or change a stage/preprod/prod deploy workflow, including the system's "Set up <env> deploy" tasks.

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 a web/frontend app needs a cloud deploy - GitHub Actions to Cloud Run/ECS for SSR or GCS+CDN/Firebase and S3+CloudFront for static, with build-time env and cache invalidation

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。

Phase C of a WinForms-to-Avalonia migration: implement platform and infrastructure adapters for dependencies discovered in Phase A/B, such as persistence, process execution, dialogs, notifications, or environment capabilities. Use only in an explicit migration when such dependencies leak into Forms/core or when the orchestrator routes here. Do not invent adapters for capabilities the product does not have, and do not implement main-window XAML.

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。

Phase E of a WinForms-to-Avalonia migration: verify behavioral parity against Phase A contracts, record intentional differences, validate supported environments and packaging, and make evidence-based legacy cutover decisions. Use only for an explicit migration when comparing the legacy and Avalonia implementations or when the orchestrator routes here. Do not delete legacy without the cutover checklist and explicit authorization.

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。

Windows 11 system administration with PowerShell — system info reporting, hardware monitoring, package management (winget/scoop), disk cleanup, performance optimization, environment configuration, PowerShell profile setup with aliases and autocomplete, and Task Scheduler automation. Use when user asks to check system info, install tools on Windows, clean up disk space, set up PowerShell profile, or automate Windows tasks. Do NOT use for Linux administration, cross-platform scripting, or network infrastructure 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。

Design CI/CD pipelines for GitHub Actions, GitLab CI, and CircleCI with matrix builds, test sharding, caching, Docker layer caching, OIDC auth, deployment strategies (rolling, blue-green, canary), auto-rollback, self-hosted runners, and environment protection with manual approvals. Use when user asks to set up CI/CD, write a pipeline, configure GitHub Actions/GitLab CI/CircleCI, automate deployments, or set up build/test/deploy workflows. Do NOT use for Dockerfile authoring (use docker), K8s manifests (use kubernetes), or Terraform config (use terraform).

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 complete service-level Terraform infrastructure with modules, environments, and CI/CD. Use when adding Terraform to a new service or bootstrapping infrastructure from scratch.

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 using UV (Python package manager), needs to set up Python virtual environments, install/manage Python CLI tools, migrate from pip/pipx to UV, Python version management, or troubleshoot UV-related 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。

Scaffold-time observability so a shipped product is not blind in prod from day one — error capture (Sentry), request-id structured logging, and /healthz + /readyz endpoints. stack-baseline pins Sentry but nothing wires it; this is the wiring. Loaded by app-scaffolder (bake into the scaffold), infra-provisioner (prod env + probes), and consumed by l3-support (traces) and devops (deploy gate).

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。

Prove a deploy landed — the served revision is the commit you meant, its env is present, and the product does its job — instead of trusting the deploy command's exit code. Also catches server drift before a pull or deploy. Use after any deploy, rollout, restart or server-side fix ("задеплой", "выкати", "проверь на проде", "почему не обновилось"). Loaded by devops, l3-support, infra-provisioner.

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。

What to do the moment a secret is exposed — pasted into chat, committed, logged, shown in a transcript or a screenshot. The secret is compromised; only revoking and rotating it helps. Turns it into one tracked task with an escalation, not a reminder repeated every session. Use when you see a key, token, password or private key anywhere it should not be ("ключ в чате", "токен в логах", "закоммитил .env"). Loaded by security-officer, l3-support, devops.

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。

Run and test directly on the server. No local dev environment needed. What you build is what goes live.

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。

Prepare a meeting-ready alternative-investments brief for a client, household, IRA, trust, or named account before a review — pulls the alts book from iCapital or Addepar (positions, NAV vs. commitment, called and unfunded amounts, contributions/distributions/subscriptions/redemptions, lockups, and the platform's own IRR/TVPI/DPI/RVPI) and sits it beside the liquid book and IPS target from the connected portfolio system (Orion Connect, Addepar, or Envestnet), then flags cash needed for capital calls, alts % vs. target, concentration, unfunded concentration, and stale as-of data. Cites the source platform on every figure and keeps disclosures on the page. Does not recommend funds, forecast NAVs/IRRs, or simulate allocation changes. Triggers on "alts brief", "/alts-brief", "prep the [client]'s alts review", "what's going on with [client]'s PE/private investments", "how's [client] doing on their alts", or any request to review a household's alternative investment positions before a meeting.

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。

Prepare a complete client review meeting prep document for a financial advisor, for whatever timeframe the review covers (quarterly, annual, ad hoc). Given a household name or email and a timeframe, pulls CRM context (Salesforce/Redtail/Wealthbox), portfolio data (Orion/Addepar/Envestnet-Tamarac), estate & tax status (Wealth.com), meeting notes (Zocks or CRM), recent correspondence (Gmail/Outlook), and the household's plan snapshot (MoneyGuide), then assembles a meeting-ready prep doc with agenda, talking points, and action items. Triggers on "pre meeting", "/pre-meeting", "prep for [client]", "quarterly review prep", "get me ready for my meeting with [client]", or "client meeting tomorrow".

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。

Unified image-generation engine for the product-shots ecosystem. Dispatches to the right model family (OpenAI gpt-image-2 / Gemini gemini-3-pro-image-preview Nano Banana Pro) with one parameterised script for text-to-image and image-to-image. Primary backend: OmniMaaS / Cloubic gateway (https://api.omnimaas.com/v1); also supports any OpenAI-SDK-compatible gateway via fallback env vars. Auto-resizes oversized reference images to prevent edge-proxy timeouts. Use when the user says "generate an image", "make a picture", "create a visual", "render this", "edit this image", "做一张图", "生成图片", "改这张图" — or when invoked as the image-generation backend by another product-shots skill (main-image / detail-page / ad-creative / social-post / multi-angle). NOT a creative-direction skill: takes a prompt + optional reference images, returns a file path.

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。

Reviews a change before it is merged, from little or no input. With nothing typed it reviews the branch you are on, uncommitted work included, against its base, its PR and its ticket, so a developer can check their own work before asking for review. With PR links, repo#N or a branch it reviews someone else's change the same way, several repos at once. It judges the failed CI checks and every Bugbot and reviewer comment, gets a fresh breaker review, traces every other piece of code that uses what changed, proves each finding or marks it unproven, says what the merge needs (a build, env vars, migrations, deploy order) and how to check the deploy, never posts to GitHub, and offers fixes only after the report. Runs only when the user types it.

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。