「mode」の検索結果

Busabase-backed App-in-Skill dashboard for a shared LLM gateway's cost and model governance — daily spend trend, cost breakdown by service/model, a canary-rollout status board, and a deterministic cost/error anomaly list. Use when the user invokes $kelly-llm-gateway or /kelly-llm-gateway, or wants to review LLM gateway spend, model routing, canary rollouts, rollback readiness, or cost/error anomalies for services routed through one shared gateway to multiple LLM providers/models. Human actions (promote/rollback/hold a rollout, acknowledge an anomaly) write directly onto the route's own Busabase record — this skill never changes a real routing config.

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。

Track ML experiments, manage model registry with versioning, deploy models to production, and reproduce experiments with MLflow - framework-agnostic ML lifecycle platform

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。

Cost modeling and ROI analysis for specialized LLM development. Use when deciding whether to train a custom model, estimating total cost, or calculating break-even vs frontier APIs. Covers training costs, inference costs, and time-to-ROI projections.

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。

Reduce LLM size and accelerate inference using pruning techniques like Wanda and SparseGPT. Use when compressing models without retraining, achieving 50% sparsity with minimal accuracy loss, or enabling faster inference on hardware accelerators. Covers unstructured pruning, structured pruning, N:M sparsity, magnitude pruning, and one-shot methods.

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。

Validates predictive models on omics and biomedical data with nested cross-validation, group/batch/temporal-aware splits, the full data-leakage taxonomy, probability calibration, decision-curve net benefit, optimism correction, sample-size planning, and TRIPOD+AI reporting. Use when estimating model performance honestly, choosing a CV scheme, detecting leakage, or judging whether reported discrimination means the model is actually useful. For feature selection itself see machine-learning/biomarker-discovery; for confirmatory-trial inference see clinical-biostatistics/trial-reporting.

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。

End-to-end genome-scale metabolic modeling from genome sequence to flux predictions. Covers automated reconstruction with CarveMe, model validation with memote, FBA/FVA analysis, and gene essentiality prediction. Use when building metabolic models or predicting metabolic phenotypes from genomic 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。

Statistical models library for Python. Use when you need specific model classes (OLS, GLM, mixed models, ARIMA) with detailed diagnostics, residuals, and inference. Best for econometrics, time series, rigorous inference with coefficient tables. For guided statistical test selection with APA reporting use statistical-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。

OpenAI API (developers.openai.com) の Responses API リファレンス。 text generation, embeddings, structured outputs, streaming, function calling, programmatic tool calling, conversation state, prompt caching, reasoning models, background mode, compaction, multi-agent。 webhooks, error codes, rate limits, Realtime API server controls。 公式 SDK (Python / JavaScript(JS) / .NET / Java / Go / Ruby), openai CLI。 Chat Completions / Assistants からの移行, deprecations。

TanStack Table (React Table) v9 ヘッドレステーブル (@tanstack/react-table) の API リファレンス。 ark-ui / chakra-ui のスタイル付き Table とは別。 useTable, tableFeatures, createColumnHelper, flexRender, createCoreRowModel, sorting, column / global / fuzzy filtering, faceting, pagination, row selection, expanding, grouping / aggregation, column / row の ordering・pinning・sizing・visibility, virtualization。 v8 legacy (useReactTable / getCoreRowModel) と v9 移行。

OpenAI API (developers.openai.com) の Evals / Fine-tuning リファレンス。 Evals API, Datasets, graders（string check, text similarity, model, python, multi）, agent evals, trace grading, external models、 SFT, DPO, RFT（reinforcement fine-tuning）, vision fine-tuning, distillation, prompt optimizer, fine-tuning best practices。 セルフサービス fine-tuning は段階的廃止進行中。

Rive アニメーションランタイムリファレンス。 Rive クラス、useRive、useStateMachineInput、RiveComponent、 State Machine、Data Binding、ViewModel、ViewModelInstance、Events、 Layout / Fit / Alignment、.riv ファイル読み込み。 @rive-app/canvas、@rive-app/webgl2、@rive-app/react-canvas、@rive-app/react-webgl2。 Rive Scripting API (Luau)、Artboard、Property、Path / Paint / Renderer、 WGSL Shaders、GPU pipeline、Node / Layout / Converter スクリプト。

OpenAI API (developers.openai.com) の運用・管理リファレンス。 Administration API, admin API keys, RBAC, roles, groups, invites, projects, spend limits, audit logs, usage / costs API, Terraform provider (openai/openai), workload identity federation (WIF, OIDC/SPIFFE), safety best practices, moderation API, production best practices, deployment checklist, cost / latency optimization, fast mode, Realtime API costs。rate limits・error handling・prompt caching は openai-api-core、Codex のワークスペース 管理は openai-codex が担当。

OpenAI API (developers.openai.com) の Agents SDK (Python / TypeScript) リファレンス。 Agent, Runner, handoffs, guardrails, tracing、 built-in tools（web search, file search, computer use, code interpreter, image generation）、 function tools, vector stores, MCP (Model Context Protocol) connectors, background mode, multi-agent orchestration、 SandboxAgent（隔離実行環境）。

Android アプリ開発 (Kotlin) の推奨アーキテクチャリファレンス。 Jetpack, androidx, UI layer, Domain layer, Data layer, state holder, UDF, SSOT, オフラインファースト, モジュール化, ViewModel, viewModelScope, SavedStateHandle, Lifecycle, repeatOnLifecycle, collectAsStateWithLifecycle, LiveData, Hilt による DI, Paging 3 によるページング処理。

Claude Code (code.claude.com) の権限・セキュリティ・企業管理リファレンス。 permissions (allow / ask / deny), permission modes, auto mode, sandboxing (sandboxed Bash), sandbox environments, zero data retention, data usage, admin setup, server-managed settings, corporate launcher, network config, authentication (SSO), analytics, monitoring (OpenTelemetry), costs, large codebases。

Apple オンデバイス機械学習フレームワークリファレンス。 Core ML / Create ML / Vision / Natural Language / Speech。 MLModel, MLModelConfiguration, MLMultiArray, MLComputeUnits, MLImageClassifier, MLTextClassifier, MLDataTable, VNImageRequestHandler, VNRecognizeTextRequest, VNCoreMLRequest, NLTagger, NLLanguageRecognizer, NLEmbedding, SFSpeechRecognizer, SFSpeechAudioBufferRecognitionRequest, SFTranscription。

visionOS / iOS 空間コンピューティングリファレンス。RealityKit、ARKit、visionOS SwiftUI API。 Entity, ModelEntity, AnchorEntity, Component, RealityView, MeshResource, Material, System, EntityAction, GaussianSplatComponent, ComputeGraphComponent, PhysicsJointsComponent, ForceEffectComponent, ClothBodyComponent, NavigationComponent, NavigationMeshComponent, NavigationController, ImmersiveSpace, ImmersionStyle, Model3D, WindowGroup, VolumetricWindowStyle, ObjectTrackingProvider, RoomTrackingProvider, ARSession, ARWorldTrackingConfiguration, ARKitSession, WorldTrackingProvider, HandTrackingProvider, SceneReconstructionProvider。

モデル世代交代の手順（model-roster.md 更新 → ADR-0001 確認 → drift 検査 → agents 更新 → 再測定 → カタログ追記）

Designs and analyzes in vivo CRISPR screens in animal tumor models, organoids, and immune-cell adoptive transfers. Covers bottleneck math (250x cells/sgRNA requires ~25M cells implanted; impossible for most syngeneic models, forcing focused libraries), focused library design (Manguso 2017 Nature 547:413 immune screen; Chen 2015 tumor screens), CRISPR-StAR intrinsic-control screening (Uijttewaal 2025 Nat Biotechnol 43:1848), clonal-dynamics-limited detection, tumor-explant DNA recovery, syngeneic vs xenograft vs PDX considerations, and the relationship to downstream MAGeCK / drugZ analysis. Use when designing in vivo CRISPR screens for tumor / immune / metastasis biology, choosing focused vs genome-wide for animal models, addressing bottleneck-induced clonal collapse, picking the syngeneic / xenograft / PDX model, integrating in vivo with in vitro results, or applying CRISPR-StAR for animal experiments.

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。

Predict RBP binding from RNA sequence using deep learning models (RBPNet sequence-to-signal, RNAProt RNN, GraphProt2 GCN with structure, DeepCLIP, DeepRiPe multi-modal CNN) for variant-effect prediction, in silico binding-site discovery, model interpretation, and transfer learning from CLIP and RBNS datasets. Use when computational prediction of RBP binding from sequence is needed, evaluating variant effects on binding without further wet-lab experiments, comparing model performance, or training a custom model on ENCODE eCLIP 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。

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". (gstack) Voice triggers (speech-to-text aliases): "see-so", "see so", "security review", "security check", "vulnerability scan", "run security".

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。

Orchestrator for the pre-decision business-idea lifecycle — generate ideas, model them, evaluate them, validate them with customers, and only then hand off to product-soul / brainstorming. Routes through `idea-generation`, `business-modeling`, `idea-evaluation`, and `customer-discovery`. Load when the user asks to explore business ideas, find a startup idea, evaluate a venture, validate an idea, says "what business should I start", "should I build this", "is this a good business", "I have a startup idea", "evaluate this venture", "model this business", "validate this idea", "Mom Test this", "Lean Canvas this", "Business Model Canvas", "Value Proposition Canvas", "go/no-go on this idea". Pre-decision suite — once one idea is committed, hands off to `product-soul`. Does NOT design features (use `brainstorming`) or audit built products (use `reality-check`).

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 and analyzes in vivo CRISPR screens in animal tumor models, organoids, and immune-cell adoptive transfers. Covers bottleneck math (250x cells/sgRNA requires ~25M cells implanted; impossible for most syngeneic models, forcing focused libraries), focused library design (Manguso 2017 Nature 547:413 immune screen; Chen 2015 tumor screens), CRISPR-StAR intrinsic-control screening (Uijttewaal 2025 Nat Biotechnol 43:1848), clonal-dynamics-limited detection, tumor-explant DNA recovery, syngeneic vs xenograft vs PDX considerations, and the relationship to downstream MAGeCK / drugZ analysis. Use when designing in vivo CRISPR screens for tumor / immune / metastasis biology, choosing focused vs genome-wide for animal models, addressing bottleneck-induced clonal collapse, picking the syngeneic / xenograft / PDX model, integrating in vivo with in vitro results, or applying CRISPR-StAR for animal experiments.

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。

Predict RBP binding from RNA sequence using deep learning models (RBPNet sequence-to-signal, RNAProt RNN, GraphProt2 GCN with structure, DeepCLIP, DeepRiPe multi-modal CNN) for variant-effect prediction, in silico binding-site discovery, model interpretation, and transfer learning from CLIP and RBNS datasets. Use when computational prediction of RBP binding from sequence is needed, evaluating variant effects on binding without further wet-lab experiments, comparing model performance, or training a custom model on ENCODE eCLIP 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。