「validate」の検索結果

Flask web framework conventions: app factory pattern with create_app(), Blueprint registration with url_prefix, MethodView for class-based API views, Marshmallow schema validation, WTForms for HTML forms, Flask-Login for session auth, flask-jwt-extended for stateless API auth, Jinja2 template conventions, error handlers, and extension initialization. Activated automatically by flask-plugin/stack.md. Use this skill to: - Structure Flask applications with the app factory and per-feature Blueprints. - Validate JSON request data with Marshmallow and HTML forms with WTForms. - Implement session-based auth with Flask-Login or token auth with flask-jwt-extended. - Render Jinja2 templates safely or return JSON responses for API mode. - Register global error handlers for consistent error responses. Do NOT use this skill for: - SQLAlchemy ORM model patterns and Flask-Migrate — see flask-plugin:sqlalchemy-patterns. - Python language idioms — see python-foundation:python-conventions. - Testing patterns — see python-foundation:pytest-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。

Use when writing, reviewing, or generating Terraform HCL that must pass fmt and validate. Not for module authoring, state operations, `terraform apply`, or remote state.

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。

Select, run, and report proportional Proto UI validation for a completed change. Use before review, handoff, commit, or pull request updates, and after resolving validation failures. Cover focused evidence first, then affected graph, types, docs, packaging, consumers, release, or repository checks according to risk.

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。

Refine an existing implementation plan with a second iteration. Re-analyzes the codebase for gaps, missing tasks, and wrong dependencies. Use after $aif-plan or to improve an $aif-fix plan. Optional +check flag validates refinements via a fresh-context subagent.

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 professional Agent Skills for AI agents. Creates complete skill packages with SKILL.md, references, scripts, and templates. Use when creating new skills, generating custom slash commands, or building reusable AI capabilities. Validates against Agent Skills specification.

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。

Perform code review on staged changes or a pull request. Checks for bugs, security issues, performance problems, and best practices. Use when user says "review code", "check my code", "review PR", or "is this code okay". Optional +check flag validates findings via a fresh-context subagent.

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。

Cyclical dogfooding loop: test AI-AGENT-STORIES.md against the live graph, discover new stories, analyze gaps, fix root causes, re-test, write report. Use when: (1) user says "/gap-loop", (2) periodic dogfooding session, (3) after a release or major feature to re-validate stories, (4) before sprint planning to prioritize product 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。

Run automated QA checks on Grafema VS Code extension via Playwright + MCP cross-validation. Use when: (1) user says "/qa" to start or resume QA session, (2) user wants to validate extension panels against graph data, (3) user wants to re-check previously found bugs after a fix, (4) user wants to run a custom QA task. Requires Docker code-server running.

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 to "minimize the repro path", "/minimize", "shrink the crash steps", "精简复现路径", "找最小复现", "把 12 步压成 3 步", or wants to compress a recorded crash repro_path into the shortest sequence that still reproduces the same crash signature. Drives live Android replay via mobile-mcp + ui-mcp + log-mcp, validates each candidate subset with analyzer-mcp's signature, and updates the session's crash record with the verified minimal path; iOS sessions use the static fallback because ui-mcp replay is Android-only.

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。

Migrate workloads from Google Cloud Platform to AWS — including AI and agentic workloads regardless of cloud provider. Triggers on: migrate from GCP, GCP to AWS, move off Google Cloud, migrate Terraform to AWS, migrate Cloud SQL to RDS, migrate GKE to EKS, migrate Cloud Run to Fargate, Google Cloud migration, migrate from OpenAI to Bedrock, move off OpenAI, switch from ChatGPT API to AWS, migrate from Gemini to Bedrock, migrate LangChain to Bedrock, migrate LangGraph to AWS, migrate agentic workloads to AWS, move AI workloads to AWS, migrate my AI app to AWS. Runs a 6-phase process: discover GCP resources from Terraform files, app code, or billing exports, clarify migration requirements, design AWS architecture, estimate costs, generate migration artifacts, and collect optional feedback. Clarify must finish before Design, Estimate, or Generate. Includes AI provider migration guidance (for example, OpenAI to Amazon Bedrock) by selecting closest-fit Bedrock model families for required modality, latency/quality targets, context windows, and cost constraints. Model mapping is compatibility-guided, not 1:1 parity; validate prompts, tool-calling behavior, and eval metrics before cutover. Do not use for: Azure or on-premises migrations to AWS, AWS-to-GCP reverse migration, general AWS architecture advice without migration intent, GCP-to-GCP refactoring, or multi-cloud deployments that do not involve migrating off GCP.

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Build and deploy full-stack web and mobile apps with AWS Amplify Gen2 (TypeScript code-first). Covers auth (Cognito), data (AppSync/DynamoDB including schema modeling, enum types, relationships, authorization rules), storage (S3), functions, APIs, and AI (Amplify AI Kit with Bedrock). Supports React, Next.js, Vue, Angular, React Native, Flutter, Swift, and Android. Always use this skill for Amplify Gen2 topics — even for questions you think you know — it contains validated, version-specific patterns that prevent common mistakes. TRIGGER when: user mentions Amplify Gen2; project has amplify/ directory or amplify_outputs; code imports @aws-amplify packages; user asks about defineBackend, defineAuth, defineData, defineStorage, or npx ampx. SKIP: Amplify Gen1 (amplify CLI v6), standalone SAM/CDK without Amplify (use aws-serverless), direct Bedrock without Amplify AI Kit (use bedrock).

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 professional Agent Skills for AI agents. Creates complete skill packages with SKILL.md, references, scripts, and templates. Use when creating new skills, generating custom slash commands, or building reusable AI capabilities. Validates against Agent Skills specification.

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。

Refine an existing implementation plan with a second iteration. Re-analyzes the codebase for gaps, missing tasks, and wrong dependencies. Use after $aif-plan or to improve an $aif-fix plan. Optional +check flag validates refinements via a fresh-context subagent.

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。

Perform code review on staged changes or a pull request. Checks for bugs, security issues, performance problems, and best practices. Use when user says "review code", "check my code", "review PR", or "is this code okay". Optional +check flag validates findings via a fresh-context subagent.

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。

Refine an existing implementation plan with a second iteration. Re-analyzes the codebase for gaps, missing tasks, and wrong dependencies. Use after /aif-plan or to improve an /aif-fix plan. Optional +check flag validates refinements via a fresh-context subagent.

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。

Throwaway experiments to validate feasibility before building.

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。

Build type-safe validated forms using React Hook Form v7 and Zod v4. Single schema works on client and server with full TypeScript inference via z.infer. Use when building forms, multi-step wizards, or fixing uncontrolled warnings, resolver errors, useFieldArray issues, performance problems with large forms.

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 and visually validate implementation-agnostic website pages or modules from an approved Design System and confirmed content. Use for Module Design Solutions, Page UI Architecture Maps, media, evidence, page motion budgets, section motion intent, visual-direction images, and direction review before HTML. Do not use to write or review HTML/CSS/JS, run browser implementation QA, plan platform fields, or implement CMS components.

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 or evolve an implementation-agnostic website design system from existing-site evidence, verified brand inputs, and an optional Visual Reference Brief. Use to establish visual and restrained-motion baselines, extract an existing site's rendered system for style-preserving extensions, validate one selected baseline with a compact Design Board, or update shared rules before page/module design. Do not conduct cross-industry reference research, map Elementor settings, create page layouts, design modules, or implement production code.

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。

对 DiT（扩散/多模态生成）模型执行 W8A8 动态（data-free）或 W8A8 MXFP8（data-free）量化。覆盖 HunyuanVideo、Wan2.2-T2V/I2V/TI2V、FLUX.1-dev（已迁移）、SD3、Sana、HunyuanDiT、CogViewX 等。YAML apiversion 固定 multimodal_sd_modelslim_v1，统一 MultimodalPipelineInterface 重构路径（不再保留 LegacyMultimodalPipelineInterface）。模板与字段以 msmodelslim/model/hunyuan_video/model_adapter.py（唯一已迁移到重构路径且 lab_practice YAML 已 validated 的 DiT）为参考；其它 DiT 需按各自推理仓的 `parse_args` 调整字段集合。data-free 指不引入外部激活校准数据，spec.dataset（校准集短名，短名如 wan2_2_t2v；enable_dump: false 时不参与 dump）仍必填；精度调优通过下游经验库 L2 §7 + 直接调 `quantization-expert-experience-tuning-rules/scripts/apply_rollback.py`（整层回退）扩展 exclude 列表，闭环由 `quantization-accuracy-tuning-orchestrator` 调度。

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。

Interactively gather parameters for `python -m cli.inference.text_generate`, generate a concrete text generation simulation command, explain assumptions, ask for execution confirmation, then run the command and summarize performance metrics. Use when the user wants to validate a specific model, hardware profile, batch/query size, prefill or decode scenario, TP/DP/EP/MOE parallel strategy, profiling database, chrome trace, empirical metrics export, or a best row produced by throughput_optimizer.

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 you need to create, write, review, annotate, or validate readable HLS, Vitis HLS, high-level synthesis, or HLS debug workflows.

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。

Discover keyword opportunities with MEASURED volume, keyword difficulty, CPC, and intent from OpenSEO, then write them into brand/keyword-plan.md. Use this skill whenever someone asks for keyword difficulty, KD, search volume, keyword ideas with metrics, striking-distance opportunities from Search Console, or SERP-validated keyword priorities. For qualitative research without an OpenSEO connection, use mktg's keyword-research instead (metrics will be unknown). Triggers: "keyword difficulty", "search volume", "keyword opportunities", "striking distance keywords", "measured keyword research".

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。

Enhance an existing marketing plan with parallel research. Use when the agent has a draft plan (brand strategy, campaign brief, content calendar, launch plan) that needs strengthening with real data. Triggers on 'deepen this plan', 'strengthen this strategy', 'research gaps in my plan', 'make this plan better', 'this plan is too surface level', 'add research to this', 'validate this plan', 'back this up with data', or when a plan exists but lacks audience data, competitive positioning, or keyword strategy. Make sure to use this whenever someone has an EXISTING plan that feels thin or unresearched — even if they just say 'is this plan good enough?' or 'what's missing here?', they likely need deepening. This skill ENHANCES existing plans — it does not create new ones. If no plan exists, route to /brainstorm or /launch-strategy first.

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。