「mode」の検索結果

Fit cognitive drift-diffusion models (Ratcliff DDM) to reaction time and accuracy data with parameter estimation (drift rate, boundary separation, non-decision time), model comparison, and parameter recovery validation. Use when modeling binary decision-making with reaction time data, estimating cognitive parameters from experimental data, comparing sequential sampling model variants, or decomposing speed-accuracy tradeoff effects into latent cognitive 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。

Deploy machine learning models to edge devices using Google AI Edge Gallery, TensorFlow Lite, ONNX Runtime, and MediaPipe. Covers model quantization (INT8/INT4), on-device inference with Gemma 4 models, Android/iOS deployment via AI Edge Gallery, hardware delegate selection (GPU/NPU/DSP), and performance benchmarking on constrained devices. Use when deploying models to mobile phones, IoT devices, or embedded systems where cloud inference is impractical due to latency, cost, or connectivity constraints.

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。

在实现适配器前对候选模型做分析。确定模型实现来源（transformers / 模型目录 / diffusers）、结构特征、内存约束下的逐层量化建议（可选）及 MoE 融合权重风险。适用于用户询问模型适配可行性或做适配前分析时使用。识别为 DiT 扩散模型时，索取推理仓路径后回传 `next_step: model-adapt`，由 orchestrator 委派 `msmodelslim-model-adapt`（DiT 扩展节承接）。

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 a viable business model for a one-person company using Lean Canvas and a simplified Business Model Canvas. Use when Codex needs to explain the business-model concepts when needed, verify niche and value-proposition prerequisites, ask one question at a time, present multiple model choices, and write user-confirmed outputs into `opc-doc/`.

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。

MATLAB R2026a and Simulink workflow for creating, inspecting, modifying, simulating, validating, and exporting .slx models. Use for Simulink, Stateflow, Simscape, model-based design, parameter sweeps, signal logging, requirements, and simulation automation. On this machine, Simulink tasks should be visual-first: open the model in a visible Simulink window before doing substantial automated modeling or simulation so the user can watch the process.

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 three-statement financial models (Income Statement, Balance Sheet, Cash Flow) with revenue forecasting, assumption management, and scenario analysis. Use this skill when the user needs to project financials, build a fundraising model, create financial projections for a business plan, or evaluate M&A targets — even if they say 'build a financial model', 'project our revenue', 'how much money will we make next year', or 'model this acquisition'.".

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。

Audits whether a machine-readable regelrecht law model is faithful to the LETTER of the wettekst — and strictly separates the wettekst (leading) from the toelichting (Nota/Memorie van Toelichting; explanatory, not a norm). Runs in critical passes per article/lid and detects four deviation classes: toelichting-bleed (a modeled criterion that the letter only states as an open norm, or not at all), missing verbatim elements (a word/condition such as a "tenzij" or a qualifier dropped from the model), wrong legal_basis anchoring (an endpoint hung on a "kapstok"-article while the operative norm lives elsewhere), and verbatim-drift in text: (an ingetrokken/older redaction). Also detects positief-lid vs uitsluitings-lid conflicts and checks the chapeau for an explicit derogation rule ("zo nodig in afwijking van het eerste lid") before judging precedence. Classifies every finding as modelfout (fix), wettekst-gevolg (report, do not fix), or letter-vs-toelichting-question (jurist decides what is leading + revision signal). Use when the user wants to know if a model is "dicht bij de letter", mentions letter vs toelichting/bedoeling, fidelity, "natuurgetrouw", or before trusting feature outcomes.

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。

Marketing analytics and data-driven optimization for B2B SaaS campaigns. Use this skill when analyzing marketing performance, optimizing conversion rates, running A/B tests, conducting customer research, creating marketing dashboards, building attribution models, analyzing funnel metrics, or storytelling with data. Also triggers on: marketing ops, performance analytics, CRO, A/B testing, data storytelling, customer insights, tracking plan, event taxonomy, UTM taxonomy, GA4 audit, consent mode, instrumentation contract, sample size calculation, minimum detectable effect, test duration, not enough traffic to A/B test, statistical significance, Bayesian vs frequentist, sample ratio mismatch, experiment feasibility, martech stack, marketing tech stack audit, tool consolidation, build vs buy, vendor evaluation, shadow IT tools, ESP/CRM/platform migration, switching cost, cutover plan, suppression list migration, data portability, customer interviews, survey design, sampling frame, non-response bias, how many people should I interview, thematic saturation, voice of customer, jobs-to-be-done, buyer persona research, is this finding real, data enrichment, which enrichment provider should we buy, waterfall enrichment, contact data quality, match rate, our list data is bad, email verification, catch-all domain, accept-all, bounce rate from bad data, B2B data decay, how often should we re-verify contacts, ZoomInfo vs Apollo vs Clearbit evaluation, cost per usable record, where did this list come from, GDPR Article 14 notice, purchased list compliance, suppression screening, TAM list building, demand planning, marketing plan, how many leads do we need, what pipeline target should marketing carry, funnel model, top-down vs bottom-up plan, plan of record, annual marketing planning, quarterly planning, pipeline coverage we have to create, sales cycle lag, our leads cannot close this quarter, carry-in pipeline, scenario planning, conservative moderate aggressive forecast, stretch target, reforecast, re-forecast triggers, assumption register, capacity ceiling, addressable audience ceiling, marginal cost per lead, blended CPL is wrong, we missed plan and nobody knows why, AI policy for the marketing team, AI governance, AI acceptable use policy, which AI tools are we allowed to use, can I paste customer data into ChatGPT, shadow AI, AI use register, human review of AI output, when can we stop reviewing every AI draft, can the agent send on its own, the AI made a mistake now what, pull back AI autonomy, AI evals, our AI vendor changed the model, AI output went out wrong, prompt injection, can our AI read prospect replies safely, lethal trifecta, the AI leaked internal notes, turn off auto-generated ad assets, auto-applied recommendations, AI literacy training, EU AI Act Article 4, duplicate records in the CRM, dedupe before import, which value wins in a merge, CRM cleanup.

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。

Write a threat model document. Use when the user says "threat model", "threat modeling", "STRIDE analysis", "attack surface analysis", "security design review", "what are the threats to this system", "adversarial analysis", "identify threats", "security threat assessment", or needs to systematically identify and document threats to a system or feature - even if they don't explicitly say "threat 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。

Use when the user says "map out our business model", "business model canvas", "how does our business work", "what are our key resources", "define our value proposition", "revenue model design", "cost structure", "who are our key partners", "lean canvas", "business model design", "how do we make money", or wants to document, stress-test, or redesign how [your startup] creates, delivers, and captures value.

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。

Agent Platform Model Tuning. Use when you need to fine-tune open models or Gemini models using Agent Platform infrastructure. Don't use for model training outside Agent Platform, model deployment to endpoints (use `agent-platform-deploy`), or managing serving endpoints (use `agent-platform-endpoint-management`).

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。

Requirements for a Salesforce Enterprise Territory Management (ETM) territory design: alignment criteria, coverage model selection, assignment rule logic, hierarchy depth and breadth, and user-to-territory ratios. Trigger keywords: territory design, territory alignment, territory model requirements, sales coverage model, territory criteria, geographic territory, named account territory, overlay territory. NOT for ETM setup steps — use admin/enterprise-territory-management. NOT for loading territory assignment data — use data/territory-data-alignment. Trigger keywords: territory design questionnaire, territory requirements document, assignment rule matrix, territory type priority, realignment plan, territory access level decision, accountAccessLevel, opportunityAccessLevel, ObjectTerritory2Association, UserTerritory2Association, Planning state model, territory acceptance tests.

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 a new Salesforce custom object: naming the object and setting its API name, selecting optional features (Activities, Chatter, History Tracking), choosing an org-wide default sharing model, and creating a tab. Triggers: 'create a custom object', 'new custom object setup', 'what sharing model should I choose', 'how do I create a tab for my object', 'object features like activities and history tracking'. NOT for designing the fields on the object - use admin/custom-field-creation. NOT for sharing rules or role hierarchy configuration - use admin/sharing-and-visibility. NOT for lookup-vs-master-detail and junction design - use data/data-model-design-patterns. More triggers: 'custom object records not showing in search', 'sharing model greyed out on my object', 'auto number restarted at 1 after deploy', 'cannot create a queue for my custom object', 'CustomObject deploy failed enableBulkApi', 'field history tracking shows no rows'.

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 when an admin needs to create an Einstein Discovery story in CRM Analytics Studio, configure prediction definitions, deploy writeback fields, set up what-if analysis, or manage model refresh and activation. Trigger keywords: Einstein Discovery story, prediction definition, writeback field, CRM Analytics Studio, model refresh, what-if analysis, bulk scoring, prediction field, 1OR prefix. NOT for the Einstein Discovery Flow action or model-health monitoring in Model Manager — use admin/einstein-discovery-deployment. NOT for Einstein Prediction Builder, a separate product needing no CRM Analytics license — use agentforce/einstein-prediction-builder. NOT for programmatic scoring through the Connect REST API — use agentforce/einstein-discovery-development.

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。

Safely auto-detect the current machine's hardware (GPU, VRAM, system RAM, OS) with read-only commands, then recommend the top 3 local AI models to run — most powerful that fits, best for coding, and best small-and-fast — each with download links. Use when someone asks "what AI models can my computer run" or "what's the best local model for my rig".

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。

Session-scoped safety guardrails for destructive commands. Warns before rm -rf, DROP TABLE, force-push, git reset --hard, kubectl delete, and similar destructive operations. User can override each warning. Active for the current session only. Use when touching prod, debugging live systems, or working in a shared environment. Use when asked to "be careful", "safety mode", "prod mode", or "careful mode". NOT for scoping edits to a specific directory (see mk:freeze).

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。

Chief Security Officer mode. Infrastructure-first security audit: secrets archaeology, dependency supply chain, CI/CD pipeline security, LLM/AI security, skill supply chain scanning, plus OWASP Top 10, STRIDE threat modeling, and active verification. Two modes: daily (zero-noise, 8/10 confidence gate) and comprehensive (monthly deep scan, 2/10 bar). Trend tracking across audit runs. Use when: "security audit", "threat model", "pentest review", "OWASP", "CSO 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。

Design deployment-focused distillation systems that balance model size, accuracy, calibration, and cascade escalation under real resource limits. Best for teacher-student compression, threshold design, and failure-aware deployment. Activate on "model compression", "teacher- student", "distillation score", "cascade model", "edge deployment", or "model calibration". NOT for generic deep-learning overviews, prompt optimization, or training work without a concrete distillation objective.

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。

Fits structural equation models to GWAS summary statistics using GenomicSEM (Grotzinger 2019), including common-factor models, confirmatory factor models, ESEM, common-factor GWAS with Q_SNP heterogeneity, multivariate Wald tests, and stratified GenomicSEM partitioned heritability. Reconciles results against MTAG multi-trait analysis. Handles sample overlap via the LDSC sampling-covariance matrix, identifies and resolves Heywood cases, and verifies model fit with CFI / RMSEA. Use when modeling latent genetic architecture across correlated traits, running multivariate GWAS on a shared factor, distinguishing factor-mediated from trait-specific SNP effects, or comparing GenomicSEM common-factor results against MTAG when both depend on accurate sampling covariance.

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。

Treinar modelos de Mixture of Experts (MoE) usando DeepSpeed ou HuggingFace. Use ao treinar modelos em larga escala com computação limitada (redução de 5× em custos vs modelos densos), implementar arquiteturas esparsas como Mixtral 8x7B ou DeepSeek-V3, ou escalar capacidade de modelo sem aumento proporcional de computação. Cobre arquiteturas MoE, mecanismos de roteamento, balanceamento de carga, paralelismo de especialistas e otimização de inferência.

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-principles strategy — strip away conventional assumptions and find the theoretical maximum for your business. Three modes. Diagnostic mode runs a full session (45+ min) to define your theoretical maximum, map the gap honestly, classify constraints, invert for failure modes, set strategic intent, and work backwards to your first intervention. Review mode (15-20 min) checks whether your theoretical maximum has shifted, whether the gap has changed, and whether you're still climbing the right peak. Alert mode defines constraint-shift, peak-shift, and local-peak triggers that tell you when to reassess. Use when someone is stuck optimising incrementally, when conventional wisdom is driving strategy, when a new technology or regulation changes what's physically possible, or when you suspect the business is climbing a local peak. Integrates with company-context.md. Based on practice by Eterdis (eterdis.com).

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。

Stress-test plans before committing by imagining they've already failed. Three modes. Diagnostic mode runs a full pre-mortem session — vivid failure scene-setting, six failure categories, the monkey test, and concrete plan adjustments. Review mode checks previous findings monthly for changed probabilities and new failure modes. Alert Triggers mode defines and monitors early warning signals across four trigger categories. Use before any major commitment — strategy, investment, product launch, partnership — or regularly to keep assumptions honest. The planning fallacy is real. The spreadsheet looked great so everyone stopped thinking. This is the antidote. Based on practice by Eterdis (eterdis.com).

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。

Knowledge base from DoD Architecture Framework (DoDAF) 2.02 — Volume II: Architectural Data and Models. Use for DoDAF viewpoints, DM2 meta-model, capability modeling, operational architecture, systems architecture, services architecture, data/information modeling, standards compliance, project/portfolio integration, and fit-for-purpose model selection. Does not cover DoDAF Volume I (policy/governance) or Volume III (enterprise-level guidance); also does not substitute for the live DISR or DARS registries.

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。

Implement, review, or improve data persistence using SwiftData. Use when defining @Model classes with @Attribute, @Relationship, @Transient, @Unique, or @Index; when querying with @Query, #Predicate, FetchDescriptor, or SortDescriptor; when configuring ModelContainer and ModelContext for SwiftUI or background work with @ModelActor; when planning schema migrations with VersionedSchema and SchemaMigrationPlan; when setting up CloudKit sync with ModelConfiguration; or when coexisting with or migrating from Core Data.

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。