「models」の検索結果

Expert guide for integrating Large Language Models (LLMs), Model Context Protocol (MCP v1.x), hybrid reasoning models, RAG architecture, vector databases, and AI agents / Panduan ahli untuk integrasi LLM, Model Context Protocol (MCP), model hybrid reasoning, arsitektur RAG, vector database, dan agen AI.

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Real-world problem formulation, mathematical abstraction, and applied mathematics for translating between practical problems and mathematical frameworks. Covers the modeling cycle (problem identification, assumptions, formulation, analysis, validation, interpretation), Polya's framework adapted for modeling, common model types (linear, exponential, logistic, periodic, power-law), dimensional analysis (Buckingham Pi theorem), optimization (linear programming, gradient descent, constraint satisfaction), probability models (Markov chains, queuing theory, Monte Carlo simulation), statistical modeling (regression, hypothesis testing, model selection), model criticism (overfitting, underfitting, sensitivity analysis), and real-world case studies. Use when formulating mathematical models, performing dimensional analysis, optimizing systems, running simulations, or evaluating model validity.

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 departures from rational choice theory and their implications for economic analysis and policy. Covers cognitive heuristics (anchoring, availability, representativeness), biases (loss aversion, status quo, overconfidence), prospect theory (reference dependence, probability weighting, diminishing sensitivity), nudge theory and choice architecture, and the integration of psychological findings into economic models. Use when analyzing decision-making under uncertainty, evaluating policy interventions that exploit behavioral patterns, or assessing where standard rational-agent models break down.

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。

Bias detection and mitigation, fairness metrics, privacy frameworks, consent models, transparency requirements, and accountability structures for data science practice. Covers algorithmic bias sources, disparate impact testing, differential privacy, GDPR principles, model cards, datasheets for datasets, responsible AI frameworks, and the organizational governance needed to make ethics actionable. Use when auditing models for bias, designing privacy-preserving systems, establishing governance processes, or evaluating the social impact of data-driven decisions.

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。

Interpersonal communication theory and practice for one-on-one and small group interaction. Covers communication models (linear, transactional, constructivist), self-disclosure and Johari Window, relational dialectics, communication climate, supportive versus defensive communication, feedback models, and adapting style across relationships and contexts. Use when analyzing communication dynamics, improving relationships, giving feedback, or understanding communication breakdowns between individuals.

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。

Python web and API development enforcing strict route/domain/data layer separation, Pydantic v2 strict request-response models, edge-resolved auth, and async-safe HTTP clients. Use when working with FastAPI, Starlette, Django, Flask, HTTP endpoints, request models, authentication flows, async handlers, or any Python web framework task.

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。

Runs several language models as independent coders on one labeling or discovery task and treats disagreement as data. Covers diverse panel assembly, independent voting, consensus rules, Cohen’s and Fleiss kappa, Krippendorff’s alpha, correlated-error limits on validity claims, human validation, and reporting. Use when the user asks about a council, panel, ensemble, or jury of models, how to combine model labels, or what kappa or alpha to report for model coders. Single-model codebook and validation work goes to text-classification, per-item confidence to llm-calibration-logprobs.

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。

Apple FoundationModels framework for on-device LLM — text generation, guided generation with @Generable, tool calling, and snapshot streaming in iOS 26+.

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。

Open a ready-to-use local 3D model composition workbench for GLB or GLTF files. Use when the user says "打开模型工作台", asks to place, rotate, scale, arrange, or frame one or more 3D models, or needs a clean composition screenshot before website or Image 2 design. If the user has not supplied a model, request GLB/GLTF files first; after models are available, generate the workspace, start its localhost server, and open it automatically.

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。

Get external agent review and feedback. Routes Anthropic models through Claude Agent SDK (uses local subscription) and other models through OpenRouter API. Use for code review, architecture feedback, or any external consultation.

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。

Self-knowledge for stella, the self-hosted AI assistant. Use when users ask about configuration, setup, onboarding, providers, models, agents, channels, webhooks, memory, schedules, workflows, goals, workers, skills, plugins, session compaction, notifications, self-update, multi-user behavior, or how stella works. Also use for changing models, setting up channels or webhooks, configuring providers, updating stella, onboarding, switching agents, reporting bugs, and goal status or acceptance.

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。

Authoritative reference for working with the released Wandelbots NOVA API v2 in the wandelbots-nova Python SDK. Use whenever a task touches the NOVA API, the generated `wandelbots_api_client` models (`nova.api.models.*`), motion planning requests (`plan_trajectory`, `MotionGroupSetup`, `LimitSet`, `LimitsOverride`), robot limits, or torque/velocity/acceleration/jerk limits. Reach for this skill before guessing field names or assuming a field exists — the installed client reflects the RELEASED API, while nightly specs may contain unreleased fields. Especially relevant for controller-reported limits (e.g. KUKA torque) and any "does field X exist / how is it forwarded to the planner?" 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。

Assesses going-concern viability with 13-week cash flow models, liquidity runway, and critical vendor analysis. Use when evaluating liquidity crises, building 13-week models, or assessing near-term solvency.

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。

Constructs venture return models with entry valuation, follow-on reserve, multiple scenario exits, and portfolio-level fund math. Use when modeling VC returns, calculating fund economics, or projecting portfolio outcomes.

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Constructs LBO models with sources/uses, debt schedules, operating projections, and returns analysis across entry/exit scenarios. Use when modeling leveraged buyouts, calculating sponsor returns, or analyzing leverage capacity.

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Constructs plan of reorganization models with debt-for-equity conversion, new money injection, and emergence capital structure. Use when modeling restructuring plans, designing emergence capital structures, or analyzing plan feasibility.

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Constructs stat arb strategies with pair selection, signal generation, and portfolio optimization under market neutrality constraints. Use when building stat arb models, designing market-neutral strategies, or optimizing pair portfolios.

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Run GGUF models directly, load LoRA adapters, benchmark inference speed, and serve models via llama-server using llama.cpp. Includes Qwen 3.5 serve scripts (9B dense + F16, 35B MoE) with asymmetric KV cache and thinking mode. Secondary to Ollama; use when needing direct model control or LoRA hot-loading. Triggers on 'llama.cpp', 'GGUF', 'LoRA adapter', 'benchmark inference', 'llama-server'.

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。

List configured models, guide provider setup, verify local Kimi companion readiness, and manage the PreToolUse hook plus optional review gate. Use when asked about available models, subscription/API-key setup, default selection, or explicitly requested to install, check, enable, disable, or uninstall the integration. Codex and Claude Code share one ~/.kimi-code/config.toml but each own a host-scoped block, so $kimi-setup here does not disturb Claude Code's /kimi:setup (and vice-versa).

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。

Export and optimize 3D models for FDM/SLA printing including STL/3MF export, mesh integrity verification, wall thickness checking, support generation, and slicing. Use when exporting from CAD or modeling software for 3D printing, verifying STL/3MF files are printable before slicing, troubleshooting models that fail to slice correctly, optimizing part orientation for strength or surface finish, or converting between model formats while preserving printability.

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Fit hidden Markov models using the Baum-Welch (EM) algorithm with model selection, Viterbi decoding for state sequences, and forward-backward probabilities. Use when observations are generated by unobservable latent states, you need to segment a time series into latent regimes (market regimes, speech phonemes, biological sequences), compute sequence probabilities, decode the most likely hidden state path, or compare models with different numbers of hidden states.

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。

Forecast infrastructure and application metrics using Prophet or statsmodels for capacity planning, cost optimization, and proactive scaling. Visualize predictions in Grafana and set up alerts for projected resource exhaustion. Use when forecasting infrastructure capacity needs for CPU, memory, or disk, planning hardware procurement for next quarter, predicting cost trends to optimize cloud spending, or setting up proactive scaling policies based on predicted load.

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 production serving infrastructure using MLflow, BentoML, or Seldon Core with REST/gRPC endpoints, implement autoscaling, monitoring, and A/B testing capabilities for high-performance model inference at scale. Use when deploying trained models for real-time inference, setting up REST or gRPC prediction APIs, implementing autoscaling for variable load, running A/B tests between model versions, or migrating from batch to real-time inference.

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。

Flask-specific delta on top of python-foundation:sqlalchemy-patterns: Flask-SQLAlchemy extension setup, db.Model declarative models (3.x Mapped style and 2.x legacy db.Column), synchronous db.session lifecycle bound to the app context, Flask-Migrate integration. Used by flask-architect (model definitions) and flask-migrate-specialist (column finalization and migration). Activated automatically by flask-plugin/stack.md. Use this skill to: - Set up the SQLAlchemy and Migrate extensions with the app-factory pattern. - Write Flask-SQLAlchemy models with the db.Model base. - Query with db.session and manage the request-scoped session lifecycle. - Integrate Flask-Migrate for Alembic-based migrations managed via flask db commands. Do NOT use this skill for: - Framework-agnostic model, column, relationship, and querying rules — see python-foundation:sqlalchemy-patterns (load it first). - Flask routing and template/API patterns — see flask-plugin:flask-conventions. - Migration execution (flask db migrate, flask db upgrade) — that's flask-migrate-specialist's job.

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。