「cad」の検索結果

Automating post-sandbox-refresh cleanup via the `SandboxPostCopy` interface — the Apex class that runs ONCE after a sandbox refresh or clone completes, before users can log in. Used to mask emails, deactivate users, scrub integration endpoints, disable scheduled jobs, and reapply per-environment configuration the metadata copy doesn't carry. Covers the interface contract (`runApexClass` setting, `SandboxContext` parameter, single-execution semantics), the email-masking pattern that stops sandbox tests emailing production addresses, and the refresh runbook checklist. NOT for choosing sandbox type or refresh cadence — use admin/sandbox-strategy. NOT for re-seeding reference data after a refresh — use data/sandbox-refresh-data-strategies. Trigger keywords: SandboxPostCopy, runApexClass, SandboxContext, Test.testSandboxPostCopyScript, RunAsAutoProcUser, Automated Process user, CronTrigger, System.abortJob, sandbox email masking, post-refresh runbook.

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。

Designing Lookup vs Master-Detail vs Hierarchical vs External relationships: cascade delete, roll-up summaries, ownership, sharing implications, polymorphic lookups, junction objects, relationship depth limits. NOT for restricting which records a lookup can pick — use admin/lookup-filter-cross-object-patterns. NOT for a roll-up across a plain lookup — use data/roll-up-summary-alternatives.

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。

Education Cloud (EDA — Education Data Architecture) setup: student success hub, advisor workflows, enrollment management, academic data model. NOT for the EDA object model in depth (Affiliations, Course_Connection__c) — use data/eda-data-model-and-patterns. NOT for SIS enrollment sync — use integration/sis-integration-patterns.

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 BA or admin needs to document the Salesforce data model: creating field inventories, object relationship maps, ER diagrams, or analyzing field usage across objects. Triggers: 'data dictionary', 'document our data model', 'object relationship map', 'field inventory', 'ER diagram for Salesforce'. NOT for designing the model or choosing lookup vs master-detail — use data/data-model-design-patterns. NOT for generating the Mermaid or PlantUML diagram — use architect/salesforce-erd-and-diagramming. More trigger keywords: field inventory CSV, describe API, getGlobalDescribe, DescribeFieldResult, sObject describe, schema snapshot, schema drift, blank field descriptions, undocumented fields, field ownership, data dictionary review cadence.

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。

Aggregate friction log files across a time period to identify recurring patterns, classify each cluster by intervention type, produce a prioritized action plan with success criteria, and calculate convergence metrics. Complements `skmtc-retro` (which captures per-session signal) by acting as the system's actuator: turning accumulated observations into decisions. The primary output is a review document that makes the "is it getting better?" question answerable. Use this skill when the user asks to "review retros", "review friction", "run a retro review", "what's the pattern across sessions", "what should we fix next", or on a periodic cadence (monthly, pre-release). Also run after a cluster of sessions on the same feature area to surface systemic issues before they compound. Distinct from `skmtc-retro` — that skill captures per-session observations; this skill synthesizes them into decisions. Do not run this skill as a substitute for a per-session retro.

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 creating scientific magazine cover style images or prompts, research hero visuals, journal-cover-inspired concepts, academic poster covers, PPT covers, and high-impact scientific visual concepts. If the user asks to generate the image, use ChatGPT image generation.

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 creating high-quality scientific or medical infographic images or prompts, including review figures, disease maps, teaching diagrams, mechanism summaries, classification overviews, timelines, matrices, and academic visual explainers. If the user asks to generate the image, use ChatGPT image generation.

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。

Academic research assistant — literature review + methodology (quant/qual) + paper outline (IMRaD) + citation + research gap analysis

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。

学术论文数学公式转换工具 - 将Markdown中的LaTeX公式转换为docx和html格式

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。

Add abstracts to .bib file entries by searching academic databases (arXiv, Semantic Scholar, CrossRef) with browser fallback.

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 academic research platform for AI agents. Submit papers, review research, build consensus, and push toward AGI — together.

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。

Porta el cambio de la rama actual a las variantes hermanas de esta plantilla (es/en × con-IA/sin-IA) — traduce el contenido, adapta o elimina la capa de IA según el destino y deja cada variante con una rama lista para PR. Úsalo cuando un cambio del template deba replicarse en las otras variantes (p. ej. "porta este cambio al template en inglés", "replica esto en las variantes").

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。

Audita los metadatos SEO de una página o ruta (title, description, canonical, Open Graph, robots) frente a las convenciones de SEO del proyecto y enumera lo que falta o está mal. Úsalo cuando la persona pida revisar/auditar el SEO, revisar las meta tags o verificar que una página esté lista para buscadores (p. ej. "audita el SEO de la página de precios", "revisa las meta tags de esta ruta").

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。

JSON-canonical session memory. Use when capturing session learnings, extracting patterns, or tracking costs — persists to .meowkit/memory JSON stores; matching Markdown files are generated views. Activates during Phase 0 (Orient) to load context and Phase 6 (Reflect) to persist it. NOT for weekly-cadence engineering retrospectives (see mk:retro); NOT for arbitrary file storage.

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。

Weekly-cadence engineering retrospective. Analyzes commit history, work patterns, and code quality metrics with persistent history and trend tracking. Team-aware: breaks down per-person contributions with praise and growth areas. Use when asked to "weekly retro", "what did we ship", or "engineering retrospective". Proactively suggest at the end of a work week or sprint. NOT for per-session or per-task reflection (see mk:memory / Phase 6 Reflect).

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 stuck on approach (not debugging). Strategic unsticking via 7 non-default techniques: simplification cascades, collision-zone thinking, meta-pattern recognition, inversion, scale game, first principles, via negativa. Triggers on complexity spiraling, innovation block, recurring patterns across domains, forced-assumption solutions, scale uncertainty, told-it's-impossible, or bloated systems needing subtraction. For evidence-based root-cause debugging, use mk:sequential-thinking 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。

Prepare, execute and verify authorized Fusion CAD changes using resolved entities, explicit units and state-bound plans. Use for reviewed parameter, sketch, feature, assembly or configuration operations.

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。

Inspect scoped Autodesk data, compare versioned BOMs, and prepare ownership-aware drafts or approved custom-property changes. Not for implicit PLM release, membership changes or CAD geometry editing.

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 this skill for ANY of the following — do not skip it even if the query seems simple: strategic decisions, business analysis, thesis building, complex problem solving, geopolitical analysis (non-market), philosophical reasoning, academic research, system design, decision under uncertainty, long-form thinking, or any question that benefits from First Principle deconstruction and multi-agent reasoning. ALWAYS use this skill when the user asks about: why something works the way it does, whether a decision is correct, how to think about a complex problem, what could go wrong, analyzing a situation from multiple angles, or any "should I..." question involving real-world trade-offs. This is NOT a market/trading skill — for financial markets, BTC, gold, stocks, forex, macro economics, use money-atlas-intelligence-os 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。

Italian geographic and property data through Openapi - geocoding/reverse geocoding, zip codes (CAP), municipalities/provinces/regions with ISTAT codes, Italian cadastre (catasto) property and owner lookups, visure catastali/ipotecarie, real estate valuations.

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。

Systematic methodology for eliciting, representing, and operationalizing expert cognitive skills—pattern recognition, situation assessment, mental simulation—from practitioners without requiring research training. Bridges academic rigor and practical usability.

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。

Strategic patterns for solving intractable problems through cascading approximation, self-improvement, and heterogeneous evaluation from DeepMind's AlphaGo system

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。

Tests genotype-environment associations and identifies adaptive loci while correcting for the four-confound landscape (structure, demography, background selection, sampling design) using LFMM2 with mandatory K via sNMF cross-entropy elbow (LEA 3), BayPass Core/AUX/C2/IS with Omega covariance matrix, RDA / pRDA for polygenic adaptation (Forester 2018; requires imputed genotypes), OutFLANK with trimmed FST null, pcadapt, gradient forests (Ellis-Smith-Pitcher 2012, NOT mis-cited Ellis-Manel), Capblancq & Forester 2021 RDA Swiss-army-knife, genomic-offset prediction with Lind & Lotterhos 2025 three-regime caveat, Lotterhos-Whitlock sampling optima, Wang & Bradburd 2014 IBD vs IBE, and Circuitscape + ResistanceGA. Use when identifying adaptive loci across gradients, choosing K for LFMM2, deciding among GEA methods, predicting maladaptation with the novel-environment caveat, distinguishing IBD vs IBE, or optimizing sampling design.

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。

Queries myvariant.info BioThings aggregator for ClinVar, gnomAD, dbSNP, dbNSFP, COSMIC, CADD, and CIViC annotations in batched, version-tracked requests. Use when annotating variant lists from multiple databases simultaneously without managing per-source APIs, and when reproducibility-grade analyses require recording source data versions via _meta.

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。