「environments」の検索結果

Validate system resilience through controlled fault injection. Covers hypothesis-driven chaos experiments, failure injection types (network, service, infrastructure, dependency), LitmusChaos/Chaos Mesh/AWS FIS/Gremlin/toxiproxy tooling, automated abort gating, game day planning, and progressive chaos adoption. Use when: "chaos engineering," "fault injection," "resilience test," "game day," "failure recovery," "system reliability," "blast radius." Not for: safe rollout flags/canary/dark launch during a release — use testing-in-production; designing new tests from production telemetry — use observability-driven-testing. Related: testing-in-production, observability-driven-testing, performance-testing, release-readiness, test-environments.

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Create and fill .agents/qa-project-context.md with the project's tech stack, test frameworks, CI/CD pipeline, environments, quality goals, risk areas, team structure, and conventions. This is the one file every other QA skill reads first, so they skip redundant discovery and give context-aware advice. Use when: "set up QA context," "configure testing," "initialize project," first use of any QA skill. Not for: bootstrapping a brand-new project's QA end-to-end — use qa-start (which calls this skill as its first step). Related: qa-start, risk-based-testing, test-strategy, qa-metrics, playwright-automation.

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Implement consumer-driven contract testing with Pact-JS (v16). Covers consumer test writing, broker-driven provider verification, Pact Broker setup, can-i-deploy as a deployment gate, webhook-triggered verification, pending pacts, and schema-first vs consumer-first approaches (OpenAPI/Ajv, Schemathesis). Use when: "contract test," "Pact," "consumer-driven," "API contract," "provider verification," "can-i-deploy." Not for: stubbing or mocking a dependency to isolate a test — use service-virtualization; general REST/GraphQL endpoint assertions against your own API — use api-testing. Related: api-testing, service-virtualization, ci-cd-integration, test-environments.

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You are an expert in Drizzle ORM, the lightweight TypeScript ORM that maps directly to SQL. You help developers write type-safe database queries that look like SQL (not a new query language), generate migrations from schema changes, and deploy to serverless environments with zero overhead — supporting Postgres, MySQL, SQLite, Turso, Neon, PlanetScale, and Cloudflare D1.

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Mask, redact, and anonymize sensitive data (PII, PCI, PHI) in databases, logs, and APIs. Use when protecting PII in dev/staging environments, redacting sensitive data from logs, anonymizing data for analytics, or applying k-anonymity and differential privacy for GDPR-compliant data sharing.

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Manage repositories, pipelines, and code review with Bitbucket Cloud. Use when a user asks to set up Bitbucket repositories, configure Bitbucket Pipelines for CI/CD, manage pull requests, set up branch permissions, use Bitbucket REST API 2.0, create webhooks, manage deployment environments, set up code review workflows, integrate with Jira, configure merge checks, or automate repository operations. Covers repository management, CI/CD pipelines, code review, deployments, and Atlassian ecosystem integration.

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Tests and debugs APIs with Bruno, the open-source API client that keeps collections as plain files in a Git repository. Use when a user asks to create API requests, organize collections, write test scripts, use environments and variables, run a collection from the terminal or CI with the bru CLI, or collaborate on API workflows stored in Git.

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AWS Bedrock — fully managed foundation models on AWS infrastructure. Use when deploying AI in AWS-native environments, needing enterprise compliance (SOC2, HIPAA), running Claude, Llama, Titan, or Mistral on AWS, leveraging Knowledge Bases for RAG, or applying Guardrails for content safety.

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Configuration management and automation with Ansible. Use when the user needs to write playbooks, manage inventory, create roles, use Ansible Vault for secrets, or orchestrate multi-server deployments across environments.

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Expert patterns for Neon serverless Postgres, branching, connection pooling, and Prisma/Drizzle integrationUse when "neon database, serverless postgres, database branching, neon postgres, postgres serverless, connection pooling, preview environments, database per preview, neon, postgres, serverless, database, branching, prisma, drizzle, connection-pooling" mentioned.

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Patterns for embedded software development including real-time systems, memory management, hardware abstraction, interrupt handling, and debugging techniques for resource-constrained environments. Use when ", " mentioned.

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The ultimate brand asset: a consistent, explorable universe generated by AI. This skill covers creating coherent brand worlds, consistent characters, architectural spaces, and visual universes that serve as the foundation for all marketing content. Traditional brand building creates guidelines. AI world building creates explorable realities. Characters with consistent personalities across thousands of images. Environments that feel like real places. Visual languages so coherent that any new asset feels like it belongs. This is where AI generation meets universe design. The practitioners of this skill don't just create images—they create the DNA that generates infinite consistent images. They build the source code for visual worlds. Use when "world building, brand world, universe, consistent character, character design, visual language, brand universe, style bible, world bible, coherent assets, character sheet, environment design, world-building, universe, character-design, consistency, visual-language, brand-world, style-guide" mentioned.

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World-class accessibility design expertise combining the inclusive design principles of Microsoft's Inclusive Design team, the technical rigor of the W3C WCAG working group, and the lived experience perspective of disability advocates. Accessibility is not a feature - it's a fundamental quality attribute that makes products usable by everyone. Great accessibility design is invisible to those who don't need it and essential to those who do. It benefits everyone: captions help in noisy environments, keyboard navigation helps power users, high contrast helps in bright sunlight. When you design for the extremes, you improve the experience for all. Accessibility is not a checklist - it's a mindset that asks "who are we excluding?" at every design decision. Use when "accessibility, a11y, wcag, screen reader, keyboard navigation, color contrast, alt text, aria, focus management, skip link, assistive technology, inclusive design, ada compliance, section 508, accessible, voiceover, nvda, jaws, tab order, focus trap, accessibility, a11y, wcag, inclusive-design, screen-reader, keyboard-navigation, color-contrast, aria, focus-management, assistive-technology" mentioned.

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Master AI-powered game asset pipelines using ComfyUI, Stable Diffusion, FLUX, ControlNet, and IP-Adapter. Creates production-ready sprites, textures, UI, and environments with consistency, proper licensing, and game engine integration. Use when "AI game art, generate game assets, ComfyUI game, stable diffusion sprites, AI texture generation, character consistency AI, procedural art generation, SDXL game assets, FLUX textures, train LoRA game, AI tileable texture, spritesheet generation, " mentioned.

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AWS Penetration Testing workflow skill. Use this skill when the user needs Provide comprehensive techniques for penetration testing AWS cloud environments. Covers IAM enumeration, privilege escalation, SSRF to metadata endpoint, S3 bucket exploitation, Lambda code extraction, and persistence techniques for red team operations and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing 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。

AWS Penetration Testing workflow skill. Use this skill when the user needs Provide comprehensive techniques for penetration testing AWS cloud environments. Covers IAM enumeration, privilege escalation, SSRF to metadata endpoint, S3 bucket exploitation, Lambda code extraction, and persistence techniques for red team operations and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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数据工程 — 数据平台从业者的认知操作系统, 覆盖把数据从源系统搬运成可靠 / 可查询 / 可信赖形态供分析 / ML / 数据产品消费的全生命周期 (生成 → 摄取 → 存储 → 转换 → 服务 + 安全/数据管理/DataOps/数据架构/编排/软件工程 六条暗流, Reis & Housley 框架): 摄取与集成 (批 + CDC 变更数据捕获 Debezium + EL 工具 Fivetran/Airbyte/Meltano/dlt + Kafka Connect + schema drift) / 存储与文件表格式 (对象存储数据湖 + 列存 Parquet/ORC/Arrow/Avro + 开放表格式 Apache Iceberg/Delta Lake/Apache Hudi + lakehouse + 分区/compaction) / 转换与建模 (ELT dbt/SQLMesh + Spark + 维度建模 Kimball + Inmon + Data Vault + 大宽表 OBT + 渐变维 SCD + 增量模型 + 语义/指标层) / 编排与工作流 (Apache Airflow/Dagster/Prefect/Mage/Kestra/Apache DolphinScheduler + DAG + 幂等 + 回填 backfill + 数据资产调度) / 批流与实时 (Apache Kafka/Apache Flink/Spark Structured Streaming/Kinesis/Pulsar/Redpanda + Lambda vs Kappa + watermark/窗口/exactly-once + 流式 SQL Materialize/RisingWave + 实时 OLAP ClickHouse/Apache Druid/Apache Pinot/StarRocks/Apache Doris) / 数仓与查询引擎 (Snowflake/BigQuery/Redshift/Databricks SQL/Trino/Presto/DuckDB/Polars + 存算分离 + MPP) / 数据质量测试与可观测性 (dbt tests/Great Expectations/Soda + 数据契约 + Monte Carlo data downtime + 新鲜度/量/schema 异常检测) / 数据治理编目与血缘 (DataHub/Amundsen/OpenMetadata/Unity Catalog + 列级血缘 + PII 分类 + 访问控制 + GDPR) / DataOps 与可靠性 (数据 CI/CD + 转换版本控制 + 环境隔离 + 幂等重处理 + 数据 SLA/SLO + 计算存储 FinOps) / 数据架构范式 (现代数据栈 + lakehouse + data mesh + data fabric + 去中心化 vs 中心化所有权) / 分析工程角色 (dbt 时代连接数据工程与分析的桥) — 不含 数据科学/ML 建模本身 (是下游消费者) / BI 仪表盘制作 (serving 下游) / 数据分析报表为终点 / 'data engineer = 跑 Hadoop 的' 过时窄化 / 通用后端应用开发 (平行学科) (Data Engineering — the cognitive operating system of practitioners who design, build, and operate the data platform: moving data from source systems into reliable, queryable, trustworthy form for analytics / ML / products, covering (a) the data engineering lifecycle (generation → ingestion → storage → transformation → serving, with the undercurrents security / data management / DataOps / data architecture / orchestration / software engineering — Reis & Housley framing), (b) ingestion & integration (batch + CDC change-data-capture with Debezium, EL tools Fivetran / Airbyte / Meltano / dlt, Kafka Connect, API + file + database sources, schema drift handling), (c) storage & file/table formats (object storage data lakes, columnar formats Parquet / ORC / Arrow / Avro, open table formats Apache Iceberg / Delta Lake / Apache Hudi, lakehouse architecture, partitioning / compaction / Z-ordering), (d) transformation & modeling (ELT with dbt / SQLMesh, Spark, dimensional modeling Kimball, Inmon CIF, Data Vault, One Big Table / wide tables, normalization vs denormalization, slowly changing dimensions, incremental models, the semantic / metrics layer), (e) orchestration & workflow (Apache Airflow, Dagster, Prefect, Mage, Kestra, Apache DolphinScheduler, DAGs, idempotency, backfills, data-aware / asset-based scheduling), (f) batch vs streaming & real-time (Apache Kafka, Apache Flink, Spark Structured Streaming, Kinesis / Pulsar / Redpanda, the Lambda vs Kappa debate, watermarks / windowing / exactly-once, streaming SQL Materialize / RisingWave, real-time OLAP ClickHouse / Apache Druid / Apache Pinot / StarRocks / Apache Doris), (g) warehouses & query engines (Snowflake, BigQuery, Redshift, Databricks SQL, Trino / Presto, DuckDB, Polars, decoupled storage & compute, MPP), (h) data quality, testing & observability (dbt tests, Great Expectations, Soda, data contracts, Monte Carlo / data downtime, freshness / volume / schema anomaly detection, unit / integration testing of pipelines), (i) data governance, catalog & lineage (DataHub, Amundsen, OpenMetadata, Unity Catalog, column-level lineage, PII / data classification, access control, GDPR / data privacy), (j) DataOps & reliability (CI/CD for data, version control of transformations, environments, idempotent reprocessing, SLAs / SLOs for data, cost / FinOps for compute & storage), (k) data architecture paradigms (modern data stack, data lakehouse, data mesh, data fabric, decentralized vs centralized ownership), (l) the analytics engineering role (the dbt-era bridge between data engineering and analysis); N

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Enables GitHub repository operations (read/write/commit/PR) for Claude.ai chat environments. Use when users request GitHub commits, repository updates, DEVLOG persistence, or cross-session state management via GitHub branches. Not needed in Claude Code (has native git access).

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Browser automation via webctl CLI in Claude.ai containers with authenticated proxy support. Use when users mention webctl, browser automation, Playwright browsing, web scraping, or headless Chrome in container environments.

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Validates development tool installations across Python, Node.js, Java, Go, Rust, C/C++, Git, and system utilities. Use when verifying environments or troubleshooting dependencies.

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GitHub repository access in containerized environments using REST API and credential detection. Use when git clone fails, or when accessing private repos/writing files via API.

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Universal environment variable loader for AI agent environments. Loads secrets and config from Claude.ai, Claude Code, OpenAI Codex, Jules, and standard .env files.

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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

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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.

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