「pipeline」の検索結果

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.

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 environment strategy for testing across dev, CI, preview, staging, and production — Docker Compose test infrastructure, multi-stage Dockerfiles, seed-data lifecycle, per-PR preview environments, production parity, and external-dependency stubbing at the HTTP boundary. Use when: "set up test environment," "docker-compose for tests," "per-PR preview environment," "staging parity," "spin up test infra," "environment tiers." Not for: choosing mock-vs-stub-vs-fake per dependency — use service-virtualization; factory and fixture data patterns — use test-data-management; pipeline/Actions config — use ci-cd-integration. Related: test-data-management, ci-cd-integration, contract-testing, service-virtualization.

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。

Hybrid fingerprint + LLM pipeline for bug classification, deduplication, and ticket generation. Normalizes CI logs, creates stable fingerprints, clusters near-duplicates, then uses LLM for severity classification and ticket writing. Includes bug reporting templates and severity/priority matrix. Use when: "bug triage," "classify bugs," "failure analysis," "auto-classify," "CI failures," "bug report," "defect template." Not for: runtime self-healing of one flaky locator — use test-reliability. Not for: designing new tests from production telemetry — use observability-driven-testing. Related: qa-metrics, qa-dashboard, ci-cd-integration, qa-project-context.

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。

Test application security against OWASP Top 10 (2025) with automated CI tooling: OWASP ZAP (DAST), dependency/supply-chain scanning (OSV-Scanner, SBOM, provenance), Semgrep SAST, auth/session tests (JWT, OAuth, RBAC), and XSS/CSRF/SQLi/SSRF Playwright patterns. Use when: "security test," "OWASP," "vulnerability," "ZAP," "XSS," "SSRF," "dependency scan," "auth testing," "OWASP LLM Top 10." Scope is automated scanning + negative-path security tests in CI, not manual penetration testing. Not for: mapping security controls to regulations (SOC 2, HIPAA, PCI, GDPR) — use compliance-testing; pipeline stage wiring and deploy gating mechanics — use ci-cd-integration; purely functional API auth/input tests with no attacker model — see api-testing; testing your product's own LLM features or defending the agent itself (prompt-injection detector, indirect injection, jailbreak red-teaming) — use ai-system-testing. Related: ci-cd-integration, compliance-testing, api-testing, shift-left-testing, ai-system-testing.

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。

Validates gate criteria before PRD lifecycle advancement by delegating to the readiness scoring pipeline (scripts/readiness.py). Returns a graduated PASS / WARN / BLOCK verdict with top blockers and their causal chain. Triggers before advancing from v0.X to v0.Y or explicit `/ghm-gate-check`.

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 dark-themed, animated technical diagrams as self-contained HTML+SVG files — flowcharts whose connectors visibly flow, and architecture diagrams where requests travel as light dots through the system (Diagrid/Temporal landing-page style). Use this skill whenever the user asks for a flowchart, workflow, pipeline, process diagram, state machine, system architecture, infrastructure, cloud, microservices, or network topology diagram — and especially when they mention "animated", "flowing", "dynamic", "alive", "GIF-like", or want a diagram for a landing page, README, docs, or product demo. Also use it to convert Mermaid source (a mermaid code block or .mmd file) into an animated diagram — "animate this mermaid", "make this flowchart move". Prefer this over static diagram output whenever the diagram represents anything that moves: requests, events, data, jobs, messages, or control flow.

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。

This skill should be used when the user says "/ultrawork", "ultrawork", or wants to run the full specify → execute pipeline automatically with a single command. Automated end-to-end workflow that chains specify and execute skills.

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。

Root cause based one-shot bug fix. debugger diagnosis → requirements.md generation → /execute. /bugfix "error description" Full investigation pipeline: debugger + gap-analyzer + standard verify. QA suggestion after successful fix.

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 technical architecture diagrams, pipeline flows, layer/stack diagrams, and system illustrations as standalone HTML files. Uses a dark-mode design system with embedded CSS and inline SVG. Use when the user needs architecture diagrams, system illustrations, flow charts, or technical visualizations.

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。

End-to-end content creation pipeline from research through editorial review to social distribution. Orchestrates a 3-stage workflow: research, draft/edit, and distribute. Supports blog posts, LinkedIn, Twitter threads, newsletters, and essays.

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。

DuckDB is an in-process analytical database that runs embedded inside your application with zero external dependencies. It can query CSV, Parquet, and JSON files directly without loading them into tables first, making it ideal for local data exploration, ETL pipelines, and analytical workloads where spinning up a server is overkill.

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 and parse Excel spreadsheets with ExcelJS — create workbooks with multiple sheets, styled cells, formulas, charts, images, and conditional formatting. Use when tasks involve exporting application data to .xlsx, building financial reports, parsing uploaded spreadsheets, or creating data import/export 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。

Manage secrets and environment variables using the Doppler CLI. Use when the user wants to set up Doppler, inject secrets into processes, manage projects and configs, upload or download secrets, create service tokens, use secret templates, or integrate Doppler with Docker, Kubernetes, or CI/CD pipelines. Trigger words: doppler, secrets manager, env vars, secret injection, doppler run, doppler setup, service token, secret rotation.

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 and use Elasticsearch for full-text search, custom analyzers, aggregations, and index management. Use when a user needs to design search mappings, write complex queries, build aggregation pipelines, tune relevance scoring, or optimize index performance for search workloads.

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。

Parse and generate CSV files with the csv package — stream large files, handle custom delimiters, transform records, validate data, and generate CSV output from objects. Use when tasks involve data import/export, ETL pipelines, processing uploaded CSV files, or generating downloadable reports.

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。

Assists with end-to-end testing of web applications using Cypress. Use when writing E2E tests, setting up component testing, configuring CI pipelines with parallelization, or building custom test commands. Trigger words: cypress, e2e testing, end-to-end, cypress run, cy.get, integration testing, browser testing.

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。

You are an expert in Crawl4AI, the open-source web crawler built for AI applications. You help developers extract clean, structured data from websites for LLM training, RAG pipelines, and content analysis — with automatic markdown conversion, JavaScript rendering, CSS-based extraction, LLM-powered structured extraction, and session management for multi-page crawling.

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。

Expert guidance for DeepEval, the open-source framework for unit testing LLM applications. Helps developers write test cases, define custom metrics, and integrate LLM quality checks into CI/CD pipelines using a pytest-like interface.

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。

Real-time face swap and video deepfake using a single source image. Use when: building face-swap applications, real-time video effects, virtual try-on features, AI video effects 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。

Automate and script DaVinci Resolve workflows. Use when a user asks to script DaVinci Resolve via Python/Lua API, automate color grading, batch render projects, manage timelines programmatically, automate media import, build render queues, create Fusion compositions via script, automate Fairlight audio processing, manage project databases, build custom tool scripts, or integrate Resolve into production pipelines. Covers the Resolve Scripting API (Python/Lua), Fusion scripting, and workflow automation.

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。

Node-based graphical interface for Stable Diffusion workflows. Build complex image generation pipelines by connecting nodes visually. Supports custom nodes, ControlNet, LoRA, upscaling, and advanced workflows with full control over the diffusion process.

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。

Expert guidance for Checkov, the static analysis tool for infrastructure-as-code that scans Terraform, CloudFormation, Kubernetes, Helm, Dockerfile, and ARM templates for security misconfigurations and compliance violations. Helps developers integrate Checkov into CI/CD pipelines and write custom policies.

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。

Cohere API for enterprise NLP — embeddings, reranking, RAG, and text generation. Use when building RAG pipelines, semantic search, document reranking, or enterprise NLP applications. Command R+ excels at tool use and retrieval-augmented generation; Embed v3 and Rerank 3 are best-in-class for search quality.

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。

Serverless vector database at the edge with Cloudflare Vectorize. Use when: building semantic search on Cloudflare Workers, RAG pipelines at the edge, low-latency vector similarity search, or storing and querying embeddings without managing a separate vector database.

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。