「rag」の検索結果

Phase A of a WinForms-to-Avalonia migration: inventory the legacy application, split responsibilities, capture command and state contracts, build a coverage matrix, and define acceptance evidence without implementing Avalonia UI. Use only in an explicit WinForms-to- Avalonia migration when behavior is undocumented or the orchestrator routes here. Generated artifacts must use the current repository's real paths, terminology, and capabilities.

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 unit, integration, E2E, and visual regression tests following the Testing Trophy methodology (80% integration). Covers Vitest/Jest, Testing Library, Playwright, MSW for API mocking, snapshot strategy, visual regression (Chromatic/Percy/Playwright), test factories with Faker, and CI sharding. Use when user asks to write tests, set up testing framework, mock API/dependencies, improve coverage, or add visual regression. Do NOT use for performance/load testing, production monitoring, or type testing (covered by TypeScript).

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。

Deploy, manage, and debug Kubernetes in production — Deployments, Services, Gateway API, Service Mesh (Istio/Linkerd/Cilium), eBPF observability (Cilium Hubble), security hardening (Pod Security Standards, OPA/Kyverno, seccomp, runtime security with Falco/Tetragon), Helm, HPA, PDB, topology spread, and debugging. Use when user asks to write K8s manifests, deploy to a cluster, debug pods, set up Gateway API, configure autoscaling, or harden cluster security. Do NOT use for Dockerfiles (use docker), CI/CD pipeline design (use ci-cd), or Terraform infrastructure (use terraform).

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 the ChromaDB vector database that stores the ecosystem's persistent memory. Use when user asks to check memory storage, backup memory, search stored entries, delete entries, or reset the vector database. Do NOT use for general question answering about past sessions (use the memory skill for that).

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 only after explicit Codex `$superloopy:i-have-adhd` or Claude Code `/superloopy:i-have-adhd` invocation, or when a qualifying leading `loopy`/`루피` task receives Superloopy's non-diagnostic ADHD-friendly output overlay. Do not activate from typos, fragmented writing, urgency, grammar, language proficiency, or ordinary discussion of ADHD, attention, focus, accessibility, or cognitive load.

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。

Testing patterns for Micronaut/Kotlin backend including repository tests, integration tests, and test data builders. Use when writing tests, setting up test infrastructure, or improving coverage.

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。

Testing patterns for FastAPI with pytest-asyncio, httpx AsyncClient, fixtures, and test data factories. Use when writing tests, setting up test infrastructure, or improving coverage in a FastAPI 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。

This skill should be used when the user asks about "code complexity", "cyclomatic complexity", "cognitive complexity", "code metrics", "maintainability index", "code coverage", or when measuring code quality quantitatively. Provides metrics thresholds and measurement techniques.

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。

用户在寻找低成本杠杆替代方案，或分析"先收钱后提供服务"的商业模式的财务优势时。 语言信号:"有没有低成本的资金来源"/"预付款模式有什么好处"/"保险公司的商业模式"/"如何用别人的钱投资" 不适用于:不需要融资/杠杆分析的纯经营决策。

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 traceability matrix and quality gate decision. Use when the user says "lets create traceability matrix" or "I want to analyze test coverage"

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 tests in red or expand mode and run-and-heal new tests. Use when the user says "lets expand test coverage", "I want to automate tests", "lets write acceptance tests", or "I want to do ATDD"

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-RED visual automation flows for Home Assistant. Covers visual automation, flow JSON, trigger-state, api-call-service, function nodes, context storage, timer patterns, error handling, and node-red-contrib-home-assistant-websocket nodes. Use when user mentions "Node-RED", "flow", "visual automation", or "node-redflöde".

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。

Ensure textures and decals cover all visible surfaces of a closed or extruded Blender asset, especially front/back caps and sidewalls, instead of relying on partial projected planes, curves, or linework overlays. Use when back textures do not fill the model, side textures are missing, spin/turntable views expose plain sidewalls, or a model needs separate UV treatment for caps and side surfaces.

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。

Explain a repository's architecture (modules, routes, storage, services), find risky hotspots, draw a Mermaid diagram of the code graph, or review how far a change reaches, from Cognee's code graph.

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。

审阅或修改剧本、对白、人物与故事结构；按原文证据给出诊断和最小修改方案，支持 Coverage 报告

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。

Designs NUnit-based C# test suites for API, component, and integration coverage. Use when creating fixtures, wiring Testcontainers, or reducing flaky CI 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。

Designs risk-based test strategy for software delivery. Use when defining coverage, setting CI gates, managing flaky tests, choosing test layers, or establishing release criteria.

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。

Mines recent papers, blogs, and newsletters for methods worth stealing, with graded evidence. Use when finding recent papers or techniques for RAG, evals, or agents.

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。

Builds docxtpl templates, DOCX extraction for RAG, and EAA accessibility checks. Use when templating Word or extracting tables; plain edits and redlines: official docx skill.

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。

Chooses among AI/ML approaches: classical ML, LLM, RAG, fine-tuning, agents, embeddings, MoE or SSM. Use when picking or scaling an architecture, or self-host vs API.

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。

Prompt engineering for production LLMs — structured outputs, evals, RAG, tool workflows, multimodal prompting, and safety. Use when designing, debugging, or shipping prompts.

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。

Adapts LLMs: SFT dataset loss masking, LoRA and QLoRA, full FT vs PEFT, distillation, pruning, tokenizer fragmentation. Use when fine-tuning or compressing a model.

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。

AI knowledge engine - a memory system in 6 lines of code. remember/recall/forget/improve loop, vector + graph search, OpenClaw plugin supported.

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。

Full repository maintenance pipeline that orchestrates all project agents in sequence - code review, accessibility, bundle size, security, API stability, test coverage, ecosystem, and release preparation. Acts as a quality gate before publishing or merging major changes. Use when: maintenance, health check, pre-release audit, quality gate, full pipeline, repo health, pre-merge check, release readiness, audit all.

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。