「readiness」の検索結果

Expert guidance for planning and executing cloud adoption using Azure Cloud Adoption Framework. Covers strategy, planning, readiness & landing zones, adoption patterns, governance, security, operations & management, organization & teams, and adoption scenarios. Use when planning AVD, SAP, Oracle, VMware, AI/ML workloads, or designing Azure landing zones and governance, and other Azure Cloud Adoption Framework related development tasks.

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Build a product-specific Amazon FBA prep, labeling, packing, and shipment-readiness plan. Use when a seller asks how to prepare units for FBA, choose barcode and labeling steps, plan poly-bagging, bubble wrap, sets, case packs, cartons, dangerous-goods review, supplier or prep-center instructions, or prevent receiving delays. Do not use as a substitute for current Seller Central requirements.

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。

Start a substantial build with Codex on Crack. Inspect the user's own configured models and readiness, recommend a lead/worker/tool shape with reasons, and let the user decide before work starts. Use before delegating; keep small or solo tasks direct.

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。

First-tier tech company P8 capability advisor for evaluating, coaching, and giving career advice against a P8 senior expert, architect, or domain-owner profile. Use when the user asks for P8-level assessment, promotion readiness, capability gaps, project or resume review, interview preparation, or growth advice based on first-tier tech company P8 expectations.

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。

First-tier tech company P7 capability advisor for evaluating, coaching, and giving career advice against a P7 technical/product/expert profile. Use when the user asks for P7-level assessment, promotion readiness, capability gaps, resume/project review, interview preparation, or concrete growth advice based on first-tier tech company P7 expectations.

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。

First-tier tech company P9 capability advisor for evaluating, coaching, and giving career advice against a P9 senior expert, director, or business/organization-level leader profile. Use when the user asks for P9-level assessment, promotion readiness, executive capability gaps, strategic project review, resume positioning, interview preparation, or growth advice based on first-tier tech company P9 expectations.

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。

Release-readiness umbrella check for a scaffolded harness — runs doctor, witness verify, hardcoded-path scan, MCP server config, and GCP Secret Manager validation in one shot. Exits non-zero if any sub-check fails.

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。

Verify the Ed25519 witness manifest of a scaffolded harness. Fast yes/no signature check that proves the publisher signed this exact file set — separate from the full release-readiness umbrella in validate-harness.

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。

5-dimension scorecard (0-100, grade A/B/C/F) for a scaffolded harness. Dimensions: Repo understanding (25%), Agent usefulness (25%), MCP safety (20%), Test coverage (15%), Publish readiness (15%). Emits a 6-field badges block (score + mcpRisk + 4 booleans) ready for the harness README. Exit 0 A/B, 1 C, 2 F.

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。

Quality gates - 11 commit readiness gates, build/test protocol, validation checkpoints, and quality metrics

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。

Roadmap-specific validation checks for architecture reviews. Load when reviewing roadmaps for implementation 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。

Designs CI/CD pipelines, infrastructure, observability, and deployment strategy. Use when preparing platform readiness for a feature.

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 a WINNING ad has to be scaled without breaking it — readiness checks, vertical scaling at plus 20 to 30 percent per step, horizontal scaling into new audiences, creatives, and channels, learning-phase reset risk, how Advantage+ and Performance Max behave when scaled, creative refresh cadence, and the signals that mean stop. Trigger on 'scale ads', 'increase the ad budget', 'scale a winner', 'CPA went up after I scaled', 'how fast can I scale', 'it worked at 50 a day and broke at 200'. Not for — fixing an account that is not working yet, see `03-performance-eval-global`; warm audience plans, see `56-retargeting-plan-global`; proving the winner first, see `19-ab-test-setup-global`.

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。

Detailed completeness audit of all project documents. Use when someone asks "are we ready for production", "audit our docs", "readiness check", "nfr tracker", or "what's missing". Do NOT use for a quick command listing — use /pmstudio instead. Scans every document for section-level completeness, reports staleness, and gives prioritized gap-closure recommendations.

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 structured logging with Pino, Prometheus metrics, and health checks for NestJS services. Use when adding JSON logging, request tracing with correlation IDs, Prometheus metric endpoints, or liveness/readiness health checks.

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。

NIST SP 800-61r3-aligned incident triage that preserves evidence, separates analysis from authorization, and records status. Use for incident intake, severity assessment, containment readiness, or evidence preservation; not generic debugging or unscoped response.

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。

Challenge mode reviews - rigorous questioning before approving changes. Use when you want thorough scrutiny of Ecto changes, LiveView events, OTP designs, or 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。

Challenge mode reviews - rigorous questioning before approving changes. Use when you want thorough scrutiny of Ecto changes, LiveView events, OTP designs, or 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。

Verification loop for Django projects: migrations, linting, tests with coverage, security scans, and deployment readiness checks before release or PR.

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。

Enable nullable reference types in a C# project and systematically resolve all warnings. USE FOR: adopting NRTs in existing codebases, file-by-file or project-wide migration, fixing CS8602/CS8618/CS86xx warnings, annotating APIs for nullability, cleaning up null-forgiving operators, upgrading dependencies with new nullable annotations. DO NOT USE FOR: projects already fully migrated with zero warnings (unless auditing suppressions), fixing a handful of nullable warnings in code that already has NRTs enabled, suppressing warnings without fixing them, C# 7.3 or earlier projects. INVOKES: Get-NullableReadiness.ps1 scanner script.

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。

Audit websites for SEO, technical, content, security, JS rendering, and AI readiness using SEOmator CLI. Returns LLM-optimized reports with health scores across 373 rules and 20 categories, and can diff two audits to show what a deploy changed. Use when analyzing websites, debugging SEO issues, checking site health, or comparing a site before and after a change.

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 reviewing, designing, or modifying Java enterprise products, services, libraries, agents, plugins, connected components, or platform modules that may qualify as products with digital elements and need EU Cyber Resilience Act secure-by-design, vulnerability handling, security update, SBOM, product documentation, or release-readiness controls. Part of Plinth Toolkit

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 reviewing, designing, or modifying Java enterprise systems that expose, exchange, store, process, export, or port data across users, businesses, connected products, cloud providers, APIs, event streams, AI data pipelines, data spaces, or SaaS platforms and need EU Data Act engineering controls. This should trigger for requests such as Review a Java platform for EU Data Act controls; Design data access and portability evidence; Add data-sharing request workflows, export formats, interoperability, metadata, audit logs, cloud-switching support, non-personal data safeguards, or trade-secret handoffs; Assess Data Act engineering readiness before production release. Part of Plinth Toolkit

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。

The compile-readiness gate for prompt auto-optimization. Decide whether you have earned the right to run an optimizer (DSPy MIPROv2 / GEPA / BootstrapFewShot) before spending compute. Two preconditions only — a real metric, and enough examples for the optimizer you picked. Garbage metric in, garbage prompt out. Pick the optimizer by data scale; GEPA inverts the scale assumption (~10 examples + textual feedback).

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。