「readiness」の検索結果

Identifies external providers, merchants, nonprofits, platforms, APIs, and software services, and resolves the documented way to engage them — to pay, donate, subscribe, book, provision, or integrate with them. MUST be used BEFORE web search, model memory, or any other directory/vendor-lookup skill for ANY request that requires selecting, finding, or engaging an external provider or organization — whether a specific one is named ("pay my Twilio bill"), implied ("donate to the Red Cross"), or only a category is stated ("set up a mailing client", "I need a database", "find me a CRM"). Directory supplies verified recipients and their documented payment or engagement path. Results are ranked by agent-readiness (how reliably an agent can complete the interaction), relevance and popularity. Do not treat this as a search-only tool. Examples: "setup a database", "find hosting", "pay X", "use Twilio", "donate $50 to the Red Cross", "book a table at Y", "subscribe to a CRM".

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。

Generates a comprehensive testing plan based on the current branch changes or a specific PR. Use when creating QA checklists, test plans, or verifying PR readiness.

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。

Monitor PR lifecycle from creation through merge including CI monitoring, review comment handling, thread resolution, and merge readiness verification.

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。

Orchestrate a comprehensive git workflow from code review through PR creation, leveraging specialized agents for quality assurance, testing, and deployment readiness. This workflow implements modern g

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。

Assesses whether and how an existing mobile product should migrate to React Native. Use when auditing one or more product repositories for migration readiness, including products whose iOS, Android, and other clients live in separate directories or repositories; choosing brownfield, greenfield, or a checkpoint-based path; defining a representative trial; or preparing a baseline and ROI decision before implementation. When product scope or material evidence is unavailable, grills the stakeholder with exactly one question per turn instead of sending a questionnaire.

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 DevKit · Check local AI DevKit readiness and run setup only when status shows onboarding or harness integration gaps.

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。

dontbesilent 好问题生成器。把模糊问题改写成 Agent 可推理、可批评、可验证的问题说明书，并判断它能被自动化解决到什么程度。 触发方式：/dbs-good-question、/好问题、/问题说明书、/Agent可解性、「这个问题能不能自动化解决」「帮我把问题说清楚」 Turn fuzzy problems into agent-solvable problem briefs and evaluate automation readiness. Trigger: /dbs-good-question, "clarify this problem", "can an agent solve this"

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 someone asks to audit their AIOS, score the Four Cs, find stale paths or unlinked projects, compare AGENTS.md and CLAUDE.md, check Claude/Codex skill compatibility, or assess migration readiness. Automatically saves dated reports and tracks evidence-backed improvements across runs.

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。

Installs and configures ktx, the open-source context layer for data agents — runs ktx setup non-interactively with hidden CLI flags, configures database connections and embeddings, installs agent integration, and verifies readiness. Use when the user asks an agent to add ktx to a project, connect data sources, install agent rules, ingest schema, or troubleshoot a local ktx install.

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 the user asks to use d3k, run/dev/test/debug a web project with d3k, or reproduce a browser issue. Own the runtime: reuse or background-start d3k non-interactively, wait for readiness, use its project-stable managed Chrome profile, and inspect unified browser/server evidence.

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End-to-end orchestrator: from a business idea, app idea, or existing app to running Azure deployment with cost estimates and pre-deploy approval. Analyzes your app, auto-detects the right Azure services, scaffolds infrastructure code, and deploys — tailored to your app, not a template. Handles moving existing apps to Azure without rewriting or with minimal changes. WHEN: bring your app to Azure, plan my app, cost to run, is my code ready to deploy, deploy my app to the cloud, deploy all my services, what Azure services do I need, plan my Azure deployment, deploy my new app to Azure, one-click deploy, I have an app and want it on Azure, migrate my app to Azure, help me get started, build an app, no code yet, starter project. DO NOT USE FOR: use azd for deployment(use azure-deploy), optimizing existing costs (use cost-optimization), code readiness checks only (use azure-app-onboard-prereq).

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 deploying an existing web application or API to an already-running Azure Kubernetes Service cluster. Detects the framework, generates a Dockerfile and Kubernetes manifests, validates against AKS Deployment Safeguards, and deploys with verification. WHEN: deploy app to AKS, deploy to existing AKS cluster, containerize app for Kubernetes, generate K8s manifests for Azure, set up CI/CD for AKS, my AKS deployment is failing safeguard checks, I have a Django/Express/Spring Boot app to run on AKS. DO NOT USE FOR: creating or provisioning an AKS cluster (use azure-kubernetes), assessing migration to AKS Automatic (use azure-kubernetes-automatic-readiness), or deploying to non-AKS targets like Web Apps, Container Apps, or Functions.

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。

Experiment design reference for problem-class framing, Minimum Viable Experiment coaching, hypothesis formation, vetting and red flags, and experiment readiness. Use when translating a stated business outcome into candidate data-science problem classes, or when framing, vetting, scoping, or evaluating an experiment of any kind, including data feasibility, architecture, LLM, performance, use-case, UX, prototyping, and hardware experiments.

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。

Performance, load, and reliability (SLO/SRE) planning for production readiness. Use when defining service level objectives, load characterization, capacity, latency budgets, stress/soak/spike test plans, false-positive baselines, and reliability targets. USE FOR: SLO/SLA definition, load testing plan, performance budget, capacity planning, reliability/SRE backlog, latency targets, error-budget policy. DO NOT USE FOR: executing load tests (use Azure Load Testing tooling), security threat modeling, RAI assessment, privacy/compliance planning, or authoring/restating PRD requirements (cite the PRD's existing NFR/FR ids instead).

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。

Machine learning experimentation reference for model-experimentation conventions, experiment tracking and reproducibility, dataset and model abstractions, ML engagement fundamentals, and model-production readiness. Use when standing up ML experimentation infrastructure or assessing whether a trained model is ready for production.

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。

Combined codebase health dashboard that scores a project across all four quality dimensions — PR quality, architecture, tech debt, and test quality — in a single pass, drawing on twelve classic engineering books. Triggers when: user wants an overall quality assessment, asks "how healthy is this codebase?", "run all the checks", "I need a health score before the release", or wants to onboard a new team with a quality overview. Do NOT trigger for: server health checks, HTTP health endpoints, Kubernetes liveness/readiness probes, database health, or application uptime. Also do not trigger when the user specifically requests only one dimension — use the corresponding focused skill instead (brooks-review / brooks-audit / brooks-debt / brooks-test).

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 PRs against the live base with immutable objects and three-way merges. Use for PR review/merge readiness, main changed, closed-PR reconsideration, open-PR sweeps, review-led repair, or explicitly requested independent review of your own contribution. Not for GitHub CRUD (use github-ops), authoring/maintaining your PR (use github-contributor), or merging without fresh review.

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 a prioritised compliance checklist for GDPR, SOC 2, ISO 27001, FCA, HIPAA, or other frameworks with a gap analysis. Use when asked for a compliance checklist, gap analysis, readiness assessment, or audit preparation for any regulatory framework. Produces a structured checklist with prioritised gaps, quick wins, and evidence requirements. Optimised for Opus 4.7 and newer models. Not a substitute for legal or compliance professional advice.

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。

Reconcile controlled-substance records and prepare for an inspection — the count against the register, the discrepancy investigation, the documentation an inspector asks for, and the reporting obligations a shortfall triggers. Use when asked to reconcile controlled drugs, prepare for a DEA or controlled-drug inspection, investigate a discrepancy or suspected diversion, or audit the CD register. Produces the reconciliation, the discrepancy investigation record, the inspection-readiness checklist, and the escalation and reporting decision tree. A process framework only; reporting thresholds, timelines and obligations are set by law in your jurisdiction and must be verified there.

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Prepare for and run a product requirements review (需求评审 / PRD 评审会) the way Chinese internet teams do it: a pre-read the engineers, testers and designers can challenge, the questions each role will ask, the meeting run sheet and a written conclusion (通过 / 有条件通过 / 不通过). Use when asked 帮我准备需求评审, PRD 评审会怎么开, 评审前检查一下我的 PRD, 研发会问什么问题, or prepare my requirements review in Chinese. Produces a readiness check of the PRD, a role-by-role question bank with answer points, a 60-minute run sheet, and the review minutes template with the conclusion and follow-ups.

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。

Structure AI and ML product decisions with the rigour of any product decision. Use when building AI-powered features, evaluating LLM integrations, designing AI products, or assessing AI readiness. Produces a complete AI product canvas covering problem definition, model approach, data requirements, evaluation framework, UX design, responsible AI checklist, and launch monitoring plan.

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。

Interactive workflow partner for creating, testing, and improving AI agent skills with waza. USE FOR: run my evals, check my skill, compare models, create eval suite, debug failing tests, is my skill ready, ship readiness, interpret results, improve score. DO NOT USE FOR: general coding, non-skill work, writing skill content (use skill-authoring), improving frontmatter only (use sensei).

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。

Analyze KiCad projects and PDF schematics: schematics, PCB layouts, Gerbers, footprints, symbols, netlists, and design rules. Reviews designs for bugs, traces nets, cross-references schematic to PCB, extracts BOM data, checks DRC/ERC, DFM, power trees, and regulator circuits. Every finding carries a confidence label and evidence source with trust_summary rollup. Analyzes PDF schematics from dev boards, reference designs, eval kits, and datasheets. Supports KiCad 5–10. Use whenever the user mentions .kicad_sch, .kicad_pcb, .kicad_pro, PCB design review, schematic analysis, PDF schematics, reference designs, Gerber files, DRC/ERC, netlist issues, BOM extraction, signal tracing, power budget, DFM, or wants to understand, debug, compare, or review any hardware design. Also for "check my board", "review before fab", "what's wrong with my schematic", "is this ready to order", "check my power supply", "verify this circuit", OSHWA certification readiness, or any electronics/PCB design question.

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。

SCM (software configuration management) and Git — branching, merges, conflicts, worktrees, baselines, audit readiness, plus Conventional Commits and safe staging.

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。