「environment」の検索結果

Analyzes species-environment relationships with constrained ordination (CCA, RDA, db-RDA), variance partitioning, indicator species (indicspecies IndVal.g group-equalized), PERMANOVA paired MANDATORILY with PERMDISP (Anderson & Walsh 2013; dispersion confounds centroid tests), Joint Species Distribution Models (HMSC, sjSDM, gjam) with explicit rejection of "residual covariance equals biotic interaction", phylogenetic community ecology (SES_MPD/MNTD), trait-environment via RLQ + fourth-corner with corrected modeltype=6 (Dray 2014), bipartite network metrics (NODF, modularity) with curveball null (Strona 2014), and Mantel-test replacements (dbRDA, GDM) for spatial data. Use when testing how environmental gradients structure communities, identifying habitat indicator taxa, partitioning variance among predictors, deciding whether PERMANOVA significance is location vs dispersion, picking among HMSC/sjSDM/gjam, or replacing Mantel tests for landscape 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。

Continuous environmental scanning across six PESTEL categories — Political, Economic, Social, Technological, Environmental, Legal. Not a one-time analysis but an ongoing radar. Three modes. Diagnostic mode runs a full scan and prioritises forces by impact, speed, and reversibility. Review mode checks the watch list monthly for movement and new signals. Alert mode defines concrete monitoring triggers for each top force. Use whenever someone wants to understand external forces acting on their business, assess macro risk, prepare for planning, or check whether the world has moved since last time they looked. Connects to the strategy map (forces act on the chain) and Wardley Map (forces drive component evolution). Based on practice by Eterdis (eterdis.com).

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 marketing, packaging, web or sustainability text against the environmental-claim rules the user supplies (house policy, regulator guidance extract, industry code) and returns a DRAFT claims review: every environmental claim quoted verbatim with location and family, the evidence the text or evidence pack offers for it with date and scope, the supplied rule passages that bear on each claim, observations as read, and separate question lists for legal and for marketing. No compliance, greenwashing or substantiation verdict. Use when the user asks to "review these green claims", "check this copy against our environmental claims policy", "what evidence do we have for carbon neutral here", "list the sustainability claims in this brochure" or "prepare questions for legal on this eco campaign". Do not use for mapping general factual claims to evidence, use claims-evidence-map instead. Drafts for human review; never approves, authorises or signs off.

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 working with Aws Proton — aWS Proton service and environment template management. Covers environment templates, service templates, service instances, pipeline management, component inspection, and template sync configuration. Use when managing Proton environments, deploying services from templates, inspecting provisioned resources, or auditing template versions.

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。

Diagnose devcontainer configuration problems and guide development environment setup. This skill should be used when the user asks to 'set up devcontainer', 'fix container startup', 'configure VS Code dev container', 'Codespaces setup', or has Docker development environment issues. Keywords: devcontainer, docker, VS Code, Codespaces, container, development environment, Dockerfile.

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。

Analyzes species-environment relationships with constrained ordination (CCA, RDA, db-RDA), variance partitioning, indicator species (indicspecies IndVal.g group-equalized), PERMANOVA paired MANDATORILY with PERMDISP (Anderson & Walsh 2013; dispersion confounds centroid tests), Joint Species Distribution Models (HMSC, sjSDM, gjam) with explicit rejection of "residual covariance equals biotic interaction", phylogenetic community ecology (SES_MPD/MNTD), trait-environment via RLQ + fourth-corner with corrected modeltype=6 (Dray 2014), bipartite network metrics (NODF, modularity) with curveball null (Strona 2014), and Mantel-test replacements (dbRDA, GDM) for spatial data. Use when testing how environmental gradients structure communities, identifying habitat indicator taxa, partitioning variance among predictors, deciding whether PERMANOVA significance is location vs dispersion, picking among HMSC/sjSDM/gjam, or replacing Mantel tests for landscape 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。

Analyzes species-environment relationships with constrained ordination (CCA, RDA, db-RDA), variance partitioning, indicator species (indicspecies IndVal.g group-equalized), PERMANOVA paired MANDATORILY with PERMDISP (Anderson & Walsh 2013; dispersion confounds centroid tests), Joint Species Distribution Models (HMSC, sjSDM, gjam) with explicit rejection of "residual covariance equals biotic interaction", phylogenetic community ecology (SES_MPD/MNTD), trait-environment via RLQ + fourth-corner with corrected modeltype=6 (Dray 2014), bipartite network metrics (NODF, modularity) with curveball null (Strona 2014), and Mantel-test replacements (dbRDA, GDM) for spatial data. Use when testing how environmental gradients structure communities, identifying habitat indicator taxa, partitioning variance among predictors, deciding whether PERMANOVA significance is location vs dispersion, picking among HMSC/sjSDM/gjam, or replacing Mantel tests for landscape 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。

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.

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。

Creates ephemeral preview deployments for each pull request with automatic deployment, unique URLs, and cleanup on PR close. Use for "preview deployments", "PR environments", "ephemeral environments", or "review apps".

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。

Comprehensive secrets extraction methodology targeting CI/CD environments across all major platforms. Covers environment variable extraction from build contexts, exploitation of vault and secrets-manager misconfigurations (HashiCorp Vault, AWS Secrets Manager, Azure Key Vault, GCP Secret Manager), runner and agent token abuse for lateral movement, OIDC federation attacks exploiting trust relationships between CI/CD providers and cloud platforms, build log leakage analysis for inadvertently exposed credentials, cache poisoning techniques for credential exfiltration, platform-specific credential store exploitation (GitHub Actions secrets, GitLab CI variables, Jenkins credential providers), service connection and service account abuse in Azure DevOps and GCP, and Docker registry credential theft from build environments. Maps to MITRE ATT&CK T1552 (Unsecured Credentials) and its sub-techniques. Each section provides enumeration procedures, extraction techniques, and post-exploitation pivoting guidance for usi...

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。

OpenClawの変更内容に合うテストやチェックを選び、失敗原因を調べるスキル。ローカル検証から環境依存の確認、リリース検証の担当手順への振り分けまで扱います。

• ソース変更に合う検証を選びたいとき
• 不具合の再現と修正確認
• CIの失敗原因を調べたいとき

OpenClawにモデル提供サービスを追加し、実際の応答まで確認するスキル。認証情報は環境変数やファイルへの参照で扱い、設定の検証と結果報告も進めます。

• OpenClawにモデルを追加したいとき
• 認証情報を参照形式で設定したいとき
• モデルの実際の応答を確認したいとき

会話の開始時に使えるスキルを確認し、応答や作業の前に読み込む手順を定めます。作業方法を決めるスキルを優先し、実行環境ごとの案内につなぎます。

• 会話開始時にスキルを確認したいとき
• 機能開発前にスキルを選びたいとき
• 不具合調査前にスキルを選びたいとき

Viteを使う開発で、設定ファイル、環境変数、APIへの接続、プラグイン、ビルド出力を整え、開発サーバーや依存関係の不具合調査を支援するスキル。

• 環境変数と開発用API転送の設定
• 開発サーバーの遅さや更新不良の調査
• 本番用ファイルの最適化と動作確認

NestJSのAPIやサービスを設計・レビューするスキル。機能別のコード構成、入力検証、認証、例外処理、環境設定、テストを整理し、実運用に向けた実装を支援します。

• NestJSのAPI構成を決めたいとき
• 入力検証とアクセス制御の実装
• 例外処理と環境設定のレビュー

Djangoの公開や変更提案の前に、環境・コード品質・データベース変更・テスト・脆弱性・設定を順に検証し、段階別の合否と修正すべき問題をレポートにまとめます。

• Djangoの変更提案前の確認
• マイグレーションを確認したいとき
• テストカバレッジの目標確認

DockerとDocker Composeで開発環境を構成し、通信・データ保存・権限を見直すほか、元のファイルを変更せずにCLIの導入手順を検証するスキル。

• アプリとDBの開発環境を構築
• コンテナの通信とデータ保存の確認
• Docker設定のセキュリティ見直し

Set up, review, debug, or validate an Orca per-workspace environment recipe: the on-demand, disposable runtime (cloud sandbox, VM, SSH host, or local container) Orca creates fresh for each workspace. Use to stand up a new recipe end to end, fix an `environmentRecipes` entry in `orca.yaml`, scaffold provider lifecycle scripts, or resolve an `orca vm recipe doctor` failure. Use `orca-cli` for ordinary worktree and workspace creation with no recipe involved.

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。

Setting up or installing into a Python environment. One rule applies whether or not you read the body - the environment belongs with the project, so create it inside the project directory (.venv) or let uv manage it, and never in a scratch directory like /tmp and never by forcing an install into the system interpreter. Load the body before creating a virtualenv, choosing an installer, or writing run instructions for a Python project.

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 deterministic, editable, free-viewpoint Three.js worlds from text or structured briefs. Use for cinematic 3D terrain, semantic regions, procedural biomes, explicit landmarks, environmental scattering, camera fly-throughs, world diagnostics, or requests for a real 3D environment rather than generated 2D footage. Integrates OpenMontage's threejs_world tool with HyperFrames; do not use for a single isolated 3D object or a flat parallax scene.

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。

Three.js textures - texture types, UV mapping, environment maps, texture settings. Use when working with images, UV coordinates, cubemaps, HDR environments, or texture optimization.

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。

Version-aware guidance for PufferLib reinforcement-learning environments, vectorization, policies, PuffeRL training, evaluation, and safe checkpoint review. Covers the native 5.0 build and environment API, published 3.0.0 Gymnasium/PettingZoo adaptation, and a pinned historical 4.0 profile.

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。

Python configuration management via environment variables and typed settings. Use when externalizing config, setting up pydantic-settings, managing secrets, or implementing environment-specific behavior.

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。

Set up a working ML training/inference environment on NVIDIA DGX Spark (GB10, aarch64, CUDA 13). Use when installing PyTorch/Unsloth/TRL/vLLM on DGX Spark, hitting libcudart or wheel-ABI errors on aarch64, or choosing between NGC containers and bare pip installs.

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。