Results for “env”

Interactive Claude Code environment setup wizard. Detects existing configuration, guides through best-practice setup for Global CLAUDE.md, project scaffolding, MCP servers, hooks, custom agents, keybindings, and settings. Use when user runs /devtools:code-env-setup, mentions "setup claude code", "configure claude", "claude code setup", "environment setup", or "initialize claude code".

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Use when authoring, reviewing, or maintaining Salesforce DevOps operational documents — runbooks, environment matrices, deployment guides, and post-deploy validation checklists. Triggers: 'runbook', 'environment matrix', 'deployment guide', 'pre-deploy checklist', 'post-deploy validation', 'how do I document a deployment', 'rollback procedure template'. NOT for what actually belongs on the pre-deploy gate — use devops/pre-deployment-checklist. NOT for release calendar, versioning, or go/no-go governance — use devops/release-management. Also covers: the devops-process YAML artefact (environment ladder, change-request state machine, deploy contract, deploy order, RACI), the deployOptions the runbook must record (checkOnly, testLevel, rollbackOnError, ignoreWarnings, purgeOnDelete, singlePackage, runTests), destructiveChangesPre/Post ordering, and the change-request emergency path.

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Expert-level environmental engineering covering water treatment, wastewater treatment, air quality, solid waste, site remediation, and environmental regulations.

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Recreate the nuxt-crouton dev environment from scratch and survive its install/build traps — the pnpm toolchain, the cold-start sequence (why packages must build before apps prepare), the version catalog and workaround pins, the per-app typecheck rule, required env vars, and the known environment traps. Use when setting up a fresh clone/worktree, when "pnpm install" or "nuxt prepare" fails, when an app won't boot after install, when typecheck output looks insane, or when a tool "isn't available". Trigger phrases: "set up the repo from scratch", "why does typecheck show thousands of errors", "playwright install fails", "what pnpm/node version". (Exact error strings — "Could not load '@fyit/crouton-*'", "Cannot find module '@vue/compiler-sfc'" — route via crouton-diagnostics-index first; it points back here for depth.)

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Use for environment validation: .env, config.yaml, paths, Python, Git — check + auto-fix + cron every 60min.

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Google Workspace CLI (gws) リファレンス。Rust 製。 `gws <service> <resource> <method>` 形式で Gmail / Drive / Calendar / Sheets / Docs / Chat 等 19 サービスを統一操作。Google Discovery Service 経由の動的コマンド生成。 `+` プレフィックスのヘルパーコマンド（+send, +agenda, +upload, +triage 等）、 ワークフロー合成（+standup-report, +meeting-prep, +weekly-digest）、 OAuth / サービスアカウント / CI ヘッドレス認証、env vars / .env / keyring 設定、 グローバルフラグ（--dry-run, --page-all, --sanitize）、exit codes、Model Armor 統合。

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Sentry の Issue 一覧 URL（例 `https://<org>.sentry.io/issues/?project=<id>&environment=<env>`）を受け取り、未解決（unresolved）の Issue を列挙して1件ずつサブエージェントで並列調査する。コード上で既に解消済みと確認できたものは Sentry 側を resolved にし、未解消のものは `create-issue` スキルで GitHub Issue を作成する。「Sentryのissueをトリアージして」「Sentryの未解決エラーをチケット化して」といった依頼で使用する。

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Build, test, and debug Hermes Agent RL environments for Atropos training. Covers the HermesAgentBaseEnv interface, reward functions, agent loop integration, evaluation with tools, wandb logging, and the three CLI modes (serve/process/evaluate). Use when creating, reviewing, or fixing RL environments in the hermes-agent repo.

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Standardizes development environment setup across machines by generating tool version configs (Node, Python, Ruby), package manager configs (pnpm, Volta, asdf, mise), environment variable templates, and setup scripts with onboarding documentation. Use when users need to "setup dev environment", "standardize tooling", "configure version managers", or "create onboarding scripts".

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Run installation, integration, and end-to-end tests that follow real user actions in remote environments or Docker. Choose an appropriate isolated environment, inspect failures, record execution details, and clean up resources after testing.

• Testing package installation in a clean environment
• Verifying an integration with a real service
• Checking behavior across macOS, Windows, and WSL2

Create rules that detect command patterns, file edits, prompts, or completion events and issue warnings or block matching actions. Configure checks for dangerous commands, sensitive files, and debug code using text patterns or multiple conditions.

• Blocking dangerous commands
• Warning about edits to sensitive files
• Detecting debug code during file edits

Generate an interactive bash script that guides people through manual service setup. It opens relevant pages, explains what to click, collects credentials, and saves values to .env files or GitHub secrets with stage confirmations.

• Guide manual setup of external services
• Collect credentials for local settings and CI
• Prepare a setup procedure for teammates

Set up Firecrawl in an application, from browser authentication and API key configuration to choosing an SDK, a library for calling the service. Add the first request and verify that the integration works.

• Adding Firecrawl to a new application
• Saving an API key in project configuration
• Choosing an SDK for the project language

Train and evaluate RuView models using Wi-Fi measurements for pose estimation, retrieval, and adaptation to different environments. Follow laptop or GCloud GPU workflows, then package and publish trained models to Hugging Face.

• Train pose estimation without cameras or labels
• Train pose estimation with paired camera and Wi-Fi data
• Adapt models to different rooms and environments

Create and improve GitHub Actions workflows that automate building, testing, and deployment. The skill covers testing across multiple environments, dependency caching, build output management, and security settings such as limited permissions and protected deployment environments.

• Create a build, test, and deployment workflow
• Run automated tests on pull requests
• Configure tests across multiple environments

Compare models available through the HuggingFace router with chat-ui configuration, research missing models, and write short English descriptions of their architecture and capabilities. Add entries to both production and development configuration files and commit the changes.

• Adding newly available router models to chat-ui
• Writing concise descriptions of model capabilities
• Updating production and development model entries together

Build editable Three.js worlds from text or structured briefs, with terrain, distinct regions, landmarks, vegetation, and camera paths. Review the environment from multiple viewpoints and refine selected areas while preserving the overall layout and reproducible settings.

• Build a continuous 3D landscape from a written brief
• Plan a camera fly-through across distinct regions
• Refine one area's terrain or vegetation placement

Set up lighting and shadows in Three.js scenes. Choose light types, create outdoor or studio lighting, use environment images to illuminate materials, and tune shadow settings to balance appearance with rendering performance.

• Create sunlight and sky lighting outdoors
• Set up studio or three-point lighting
• Use HDR images for environment lighting

Configure images and videos as surface textures in Three.js, and use environment images for backgrounds and reflections. Adjust placement, color settings, repetition, and filtering, then manage texture memory through compression, reuse, and disposal.

• Adjust image placement and tiling on 3D surfaces
• Use HDR images for backgrounds and reflections
• Apply video or Canvas content to a 3D surface

Understand how Codex Rules adds project instructions and file-specific rules to agent context. Check supported rule locations, when rules are applied, and environment settings for modes, sources, and text limits.

• Checking supported locations for rule files
• Understanding when file-specific rules are applied
• Configuring rule modes and text limits

Build and evaluate single-agent reinforcement learning experiments, where an agent learns actions from rewards. Use Stable Baselines3 to choose algorithms, create environments, monitor training, and save models with the normalization data needed for consistent evaluation.

• Set up a reproducible reinforcement learning experiment
• Create and validate a custom Gymnasium environment
• Monitor training and save checkpoints

Review the journey from discovering an API to receiving the first successful response. Identify delays in signup, authentication, setup, and documentation, then plan improvements using test environments, interactive examples, clearer errors, and measurements of progress.

• Auditing the path to a first successful API call
• Simplifying signup and access to test API keys
• Planning sandbox environments and interactive documentation

Define security controls for AI coding agents used by a team. Covers command and file permissions, secret scanning, isolated execution environments, code review gates, and audit trails, with configuration examples to adapt.

• Set team rules before introducing AI coding agents
• Restrict access to credentials and infrastructure files
• Design an isolated environment for agent execution

Support authorized security assessments of Microsoft Active Directory with guidance on environment discovery, credential risks, authentication weaknesses, and privilege escalation. The material organizes tools and workflows for penetration testing and controlled training environments.

• Planning an authorized Active Directory assessment
• Exploring attack paths with BloodHound
• Reviewing authentication and privilege weaknesses