「env」の検索結果

Pre-launch and pre-commit audit for vibe coding projects. Use when asked to check whether a project is ready to ship, deploy, merge, or commit, especially for common AI-built app mistakes: broken project structure, committed secrets or cache files, environment variable hygiene, database migrations, ORM/schema drift, unsafe raw SQL, unused legacy code, dead routes/components, weak auth, missing tests, build failures, and deployment footguns.

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。

Create visual concepts, image-generation prompts, and optional AI-generated images for product hero images, marketing creatives, social posts, ads, ecommerce PDP visuals, and general visual design tasks. Use when the user asks for visual strategy, image prompt writing, product/marketing image direction, or direct text-to-image generation. In Codex environments use the built-in imagegen tool; elsewhere use the bundled script backed by the apiz CLI.

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。

Read and write Railway: list projects and deployments, set environment variables, redeploy. Trigger phrases: railway, deploy.

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 setting up or optimizing developer workflows in a monorepo, managing mise tasks, git hooks, CI/CD pipelines, database migrations, or release automation. Invoke for development environment setup, build automation, testing workflows, and release coordination.

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。

Records visual proof while testing UI behavior — the agent tests the app hands-on via computer use while a screen recording with structured test/assertion annotations captures the session — then posts the video and a results summary to the PR and tracker issue. Use whenever a change needs verifiable evidence that it works, instead of prose claims — including headless environments (scripted screenshots and probes) and non-UI changes (measured numbers, output pairs).

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。

Live SEO data via DataForSEO API credentials. SERP analysis (Google, Bing, Yahoo, YouTube, Google Images), keyword research (volume, difficulty, intent, trends), backlink profiles, on-page analysis (Lighthouse, content parsing), competitor analysis, content analysis, business listings, AI visibility (ChatGPT scraper, LLM mention tracking), and domain analytics. Uses DataForSEO credentials from the selected project `.env`; MCP tools are optional adapters when already available. Use when user says "dataforseo", "live SERP", "keyword volume", "backlink data", "competitor data", "AI visibility check", "LLM mentions", "image SERP", "google images", "image rankings", or "real search data".

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 you add tests for Julia packages

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Writes Vitest tests following project patterns: __tests__/ directories, vi.mock() for module mocking with vi.hoisted() for test-time factories, global LLM mock from src/test/setup.ts, environment variable save/restore in beforeEach/afterEach, vi.clearAllMocks() lifecycle, and test file organization. Use when user says 'write tests', 'add test coverage', 'test this', creates *.test.ts files, or when test failures appear in CI. Do NOT use for non-test code or for debugging without writing tests.

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。

Remote API traps in Kotlin Multiplatform: Ktor clients, defensive parsing, Resource envelopes, OK responses that dropped your data, auth callbacks, retry backoff, downloads, websockets, clock sync. Use when calls succeed but data never lands.

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。

Inspect an agent repository and optional traces, interview the user, write reviewed Task Specs, build and audit Harbor tasks, and bootstrap reusable project World Knowledge Skills. Use for agent evals, benchmark design, Task generation, controlled Environments, synthetic data, Verifiers, Harbor runs, calibration, or continuous benchmark maintenance.

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INVOKE THIS SKILL when setting up a new project or when asked about package versions, installation, or dependency management for LangChain, LangGraph, LangSmith, or Deep Agents. Covers required packages, minimum versions, environment requirements, versioning best practices, and common community tool packages for both Python and TypeScript.

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INVOKE FIRST for any LangChain / LangGraph / Deep Agents agent building project before consulting other skills or writing any agent code. Required starting point for up to date info on framework selection (LangChain vs LangGraph vs Deep Agents vs hybrid composition), agent patterns, install, environment setup, and which skill to load next.

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。

Take an agent-written app from repo to live production on the user's OWN accounts, with providers they choose (hosting, database, auth, payments, email, domain/DNS). The human connects accounts and approves changes; supported wiring operations run through a local CLI and produce verification evidence with explicit limits. Use when the user wants to ship, deploy, go live, launch, publish, or put their app online, or asks to wire up env vars, webhooks, auth settings (signup, email confirmation, password policy), a real signup → confirmation email → login journey, password recovery, account isolation between two users, auth redirects, email DNS or a custom domain.

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 targeting Field Crops Research or deciding whether an agronomy / crop-physiology manuscript fits this venue. Encodes the journal's fit, the field-scale multi-environment and replication bar, data-reporting expectations, house style, official-submission re-check, and desk-reject heuristics.

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Use when positioning an Agricultural Systems (AgSy) manuscript against the literature so it reads as a systems contribution. AgSy readers span agronomy, modelling, livestock, economics, environment, and food-system science, so the paper must engage the systems and modelling literatures they expect — not only one subfield. Stakes the contribution; it does not write the lit review.

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Use when strengthening the reproducibility of an ACM MM (ACM Multimedia) paper or preparing for the ACM MM Reproducibility track and ACM artifact badging — capturing environments, media/data access, seeds, and multimodal pipelines so an independent reviewer can rebuild results and reach Artifacts Evaluated or Results Reproduced badges.

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 strengthening ACM CCS reproducibility evidence, including the artifact-availability posture, threat-model-to-evidence mapping, attack reproduction steps, defense overhead measurement, measurement-dataset provenance, environment and version pinning, and honest justification when artifacts cannot be shared.

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 preparing an accepted AAMAS paper for the IFAAMAS open-access camera-ready, covering author de-anonymization, proceedings metadata, two-column reflow of game figures and learning curves, resolving rebuttal-stage promises, registration, in-person presentation, and the public code and environment release for a multiagent paper.

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 packaging AAMAS multiagent code, environments, opponent and population sets, random seeds, game definitions, and logs as anonymous supplementary evidence or a public post-acceptance release, even without a separate artifact badge, so that game-theory and MARL reviewers can inspect and re-run the interaction claims.

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。

Render a 'brand identity reveal' video from a config — a single poster frame in a real, softly-lit space (real wall, soft-focus plant in the corner, dappled leaf shadow, illuminated poster) whose artwork HARD-CUTS through ~10 on-brand poster mockups (hero product, IG post, hanging banners, sticker sheet, logo lockup, poster, two lifestyle stills, packaging, big icon) then holds on a brand end card. The environment plate is one create-image-fal generation; the mockups are real-DOM HTML frame-stepped via Playwright, perspective-composited into the detected frame quad with the plate's real leaf-shadow multiplied back onto each poster (reads as behind glass), sequenced by FFmpeg. Deterministic assembly, FREE (the plate comes from create-image-fal, the bed from create-music-elevenlabs), music bed only and approved brand copy only. Use for the brand-identity-reveal format.

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。

Shared helper that routes ALL paid media generation (FAL image/video, ElevenLabs music) through the GooseWorks proxies so every call bills the Ads agent — never a provider SDK's default host. Host-swaps the FAL queue URLs, loads the agent token from the sandbox env (GW_MEDIA_PROXY_TOKEN) or ~/.gooseworks/credentials.json, and returns the result CDN URL. Every video-ad media capability imports this; templates never call a provider directly.

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。

Configure Aspire AppHost to emit explicit app config via environment variables; keep app code free of Aspire clients and service discovery.

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。

Managing local .NET tools with dotnet-tools.json for consistent tooling across development environments and CI/CD pipelines.

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。

Download large datasets from NCBI efficiently using EPost, history server, batching, rate limiting, and retry logic. Use when bulk-fetching tens of thousands of sequences, pulling all results of a large ESearch, designing reproducible pipelines, comparing E-utilities to NCBI Datasets v2 CLI, or implementing checksum-validated downloads. Encodes WebEnv TTL (~8h), EPost 200-ID limit, retmax caps, parallelization design, and integrity verification.

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。