「dom」の検索結果

Model interpretability and explainability using SHAP (SHapley Additive exPlanations). Use this skill when explaining machine learning model predictions, computing feature importance, generating SHAP plots (waterfall, beeswarm, bar, scatter, force, heatmap), debugging models, analyzing model bias or fairness, comparing models, or implementing explainable AI. Works with tree-based models (XGBoost, LightGBM, Random Forest), deep learning (TensorFlow, PyTorch), linear models, and any black-box 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。

Add custom local tools to ToolUniverse alongside the 1000+ built-in tools. Covers JSON-config tools (simplest, no code), Python class tools (REST/SOAP/GraphQL APIs, computational logic), and best-practices for return schemas. Use for wrapping new APIs, adding domain-specific computations, or contributing tools to the registry.

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。

Dereplicate a putative natural product and assign its chemical taxonomy. Use to answer "is [compound] a known natural product", "what microbe/organism produces [compound]", "what chemical class is [compound]", "dereplicate this metabolite (by formula/exact mass/InChIKey/SMILES)", or "classify this molecule into ChemOnt". Searches NPAtlas for known microbial natural products (producing organism + literature reference), assigns the ChemOnt kingdom→superclass→class→subclass hierarchy via ClassyFire, resolves systematic IUPAC names to structure via OPSIN, and cross-references identity in PubChem. NOT for general drug/compound identity or ADMET (use tooluniverse-chemical-compound-retrieval / tooluniverse-small-molecule-discovery) and NOT for metabolomics pathway/enrichment analysis (use tooluniverse-metabolomics skills).

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。

Lipid analysis and lipid-disease associations using LIPID MAPS classification, HMDB metabolite data, KEGG/Reactome lipid pathways (sphingolipid, eicosanoid, steroid, fatty acid), and PubChem chemical info. Use for lipid identification, lipid metabolism pathway mapping, and lipid-associated disease analysis (cardiovascular, diabetes, NAFLD).

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。

Comprehensive toolkit for creating, analyzing, and visualizing complex networks and graphs in Python. Use when working with network/graph data structures, analyzing relationships between entities, computing graph algorithms (shortest paths, centrality, clustering), detecting communities, generating synthetic networks, or visualizing network topologies. Applicable to social networks, biological networks, transportation systems, citation networks, and any domain involving pairwise relationships.

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 the MS-DIAL preprocessing workflow (peak picking, MS2Dec spectral deconvolution, alignment, gap-filling) and imports the alignment-result table into R or Python with honest filtering. Use when preprocessing LC-MS DDA/DIA (SWATH) raw data with MS-DIAL, deciding MS-DIAL vs XCMS, configuring the MsdialConsoleApp console run, or parsing an MS-DIAL export into a clean feature matrix. For programmatic R peak detection and the feature-table-as-artifact framing see metabolomics/xcms-preprocessing; for lipid annotation mode see metabolomics/lipidomics; for MSI-level confidence honesty see metabolomics/metabolite-annotation; for drift correction and QC see metabolomics/normalization-qc.

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。

Turns untargeted LC-MS/MS features (m/z, RT, MS/MS) into confidence-stratified metabolite annotations using spectral-library matching (matchms), in-silico tools (SIRIUS/CSI:FingerID, MetFrag) and molecular networking, and assigns a defensible MSI/Schymanski confidence level to each. Use when naming detected features, scoring MS/MS against a reference library, running SIRIUS, or deciding what confidence level an evidence set actually supports. For upstream feature extraction see metabolomics/xcms-preprocessing and metabolomics/msdial-preprocessing; for downstream enrichment that must respect these levels see metabolomics/pathway-mapping; for lipid-specific structural annotation see metabolomics/lipidomics.

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。

Assigns honest lipid annotation levels, designs class-based internal-standard quantification, and runs lipid-aware differential and enrichment analysis with lipidr, guarding against in-source-fragment phantoms, sn-position over-claims, and invalid cross-class quantification. Use when naming or canonicalizing lipid species (shorthand separators, Goslin), deciding shotgun vs RP vs HILIC LC-MS, picking internal standards (SPLASH/EquiSPLASH), interpreting MS-DIAL/LipidSearch output, or comparing lipid classes. For general feature detection see metabolomics/xcms-preprocessing and metabolomics/msdial-preprocessing; for non-lipid annotation confidence see metabolomics/metabolite-annotation; for normalization/QC see metabolomics/normalization-qc; for multivariate stats see metabolomics/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。

Predicts ADMET properties using ADMETlab 3.0 (119 endpoints with uncertainty), ADMET-AI, DeepChem MolNet, and chemprop D-MPNN with explicit handling of OECD QSAR principles, applicability domain assessment, calibration, hERG/CYP/AMES gold-standard endpoints, and PAINS / Lipinski / Ro5 / Veber / BBB druglikeness filters. Use when filtering compounds for drug-likeness, prioritizing leads by predicted safety, or building an in-house ADMET QSAR 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。

Cross-chain interoperability for Stellar. Entry point with a rail-selection decision table and shared pitfalls, routing to two companion files — cctp.md (Circle CCTP V2, native USDC burn-and-mint between Stellar and EVM/Solana chains, domain 27, the CctpForwarder requirement for Stellar recipients) and axelar.md (Axelar GMP for Soroban contracts calling contracts on other chains, and the Interchain Token Service for multichain tokens). Also covers NEAR Intents (intent-based cross-chain swaps into XLM or Stellar USDC) at the routing level. Use when bridging USDC to or from Stellar, sending messages between a Stellar contract and another blockchain, making a token exist on multiple chains, or adding cross-chain swaps to an app.

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。

TypeScript and test style conventions for the testmex monorepo. Use when writing or reviewing TypeScript types, interfaces, core domain models, or tests in this repository.

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。

Maintain weighted collections, datasets, groups, selectors, and random content tables.

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 common EntitySystem helpers, prototype APIs, spawning, deletion, audio, popup, and random 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。

Coordinate multiple agents for a complex multi-domain project using PM planning, parallel agent spawning, and QA 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。

Auto-detect project tech stack and generate stack-specific references for domain skills

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。

Initialize project harness — AGENTS.md as table of contents, ARCHITECTURE.md as domain map, structured docs/ knowledge base

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。

Launches a CARLA RPC server a carla.Client can connect to, working out for itself what you pointed it at — a downloaded/extracted release, a package cooked inside a checkout, or a source checkout run through the UE4 editor — headless or windowed, and shuts it down cleanly. ROS2=1 starts it with the native ROS 2 interface active (--ros2, --rmw=fastdds/cyclonedds/zenoh, --ros-domain-id). Use when the user asks to "run/start the CARLA server", "boot CARLA headless", "launch CARLA with a window", "run the CARLA I downloaded", "start CARLA with ROS2 enabled", or "serve a packaged CARLA build".

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。

Visualises and validates a map's pedestrian navigation mesh by sampling walkable locations (get_random_location_from_navigation) and drawing them as debug points. Use when the user asks to "show/visualise the pedestrian navmesh", "check walkable areas", "validate pedestrian navigation after a map import", or "why won't my walkers move". Confirms the navmesh loaded and how far it covers.

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。

Adds a new topic to CARLA's native ROS 2 interface — a publisher (BasePublisher + PublisherImpl, topic suffix, QoS, registration in ROS2::GetOrCreateSensor, a ProcessDataFrom* entry point and its call site in the UE4 plugin) or a subscriber that lets ROS drive an actor. Also covers adding a middleware (RMW) behind IPublisherMiddleware. Use when the user asks to "publish X to ROS from CARLA", "add a ROS topic/publisher/subscriber", "make the obstacle/lane-invasion sensor publish", "add odometry to ROS", or "add another RMW".

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。

Spawns pedestrians that wander autonomously via WalkerAIController, and destroys them in the correct order. Pairs each walker.pedestrian.* with a controller.ai.walker, places them on the navigation mesh, and sends them to random navmesh points at walking speed. Use when the user asks to "spawn pedestrians/walkers", "add people/crowds", "make pedestrians walk around", or "remove the walkers". Reads the world settings first: if the world is already synchronous it never ticks it, and if it is asynchronous it makes it synchronous and drives the clock itself.

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。

Generates Policy pattern for PHP 8.5. Creates encapsulated business rules for authorization, validation, and domain constraints. Includes unit 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。

Scaffolds a complete .NET solution following Clean Architecture principles with proper layer separation (API, Application, Domain, Infrastructure). Creates project structure, dependency injection setup, and cross-cutting concerns configuration.

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。

Raccourcir des URLs via is.gd ou v.gd. Use when: l'utilisateur demande de raccourcir une URL, créer un lien court, ou simplifier une URL longue. NOT for: URLs nécessitant une authentification, liens personnalisés avec domaine propre, analytics avancés.

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。

Compile the Unity project and report errors/warnings. Use after C# edits or when a full Domain Reload compile is needed.

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。