「model」の検索結果

Molecular featurization for ML (100+ featurizers). ECFP, MACCS, descriptors, pretrained models (ChemBERTa), convert SMILES to features, for QSAR and molecular ML.

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Molecular ML with diverse featurizers and pre-built datasets. Use for property prediction (ADMET, toxicity) with traditional ML or GNNs when you want extensive featurization options and MoleculeNet benchmarks. Best for quick experiments with pre-trained models, diverse molecular representations. For graph-first PyTorch workflows use torchdrug; for benchmark datasets use pytdc.

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Completes the online SBTI personality test at sbti.fancc.de5.net, answers all single-choice questions using the model's default personality and stable behavioral tendencies, submits the test, and returns the final type, match percentage, result description, and fifteen-dimension scores. Use when the user asks to take the SBTI test, run the SBTI quiz, get an SBTI result, or complete that personality test website for them.

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Query aggregate Langfuse cost, volume, latency, and model telemetry efficiently with Metrics API v2. Use for dashboards, rollout checks, usage summaries, and cost or latency comparisons; use Observations API v2 instead when individual rows are actually required.

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Forensic case investigation with evidence-graded findings, calibrated to the input. Use when the user asks to investigate a bug, trace what caused an incident, walk through unfamiliar code, or build a mental model of a code area before working on it.

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Survey a codebase's animation and motion code as a senior motion advisor, then produce a prioritized audit and self-contained implementation plans for other agents (or cheaper models) to execute. Read-only on source code — it plans improvements, it does not apply them. Use when the user asks to "improve the animations", "audit the motion", "make this app feel better", or wants a roadmap of animation fixes rather than a review of a single diff.

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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.

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Change persistent models and SQLite/PostgreSQL migrations without breaking existing servers.

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Implement server APIs, authentication, and application data access. Schema modeling and query tuning use oma-db.

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Deploy AI models to mobile applications. TRIGGERS - Use when user needs help with ai-mobile-deployment related tasks.

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Build marketplaces for AI models and prompts. TRIGGERS - Use when user needs help with ai-marketplace-builder related tasks.

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Build playgrounds for AI model testing. TRIGGERS - Use when user needs help with ai-playground-builder related tasks.

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Deploy AI models to edge devices. TRIGGERS - Use when user needs help with ai-edge-deployment related tasks.

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Build AI fallback systems with model routing. TRIGGERS - Use when user needs help with ai-fallback-system related tasks.

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Guide AI model fine-tuning with data preparation. TRIGGERS - Use when user needs help with ai-fine-tuning-guide related tasks.

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Design AI model deployment strategies with rollout. TRIGGERS - Use when user needs help with ai-deployment-strategy related tasks.

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Build data pipelines for AI model training. TRIGGERS - Use when user needs help with ai-data-pipeline related tasks.

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Deploy AI models to browser environments. TRIGGERS - Use when user needs help with ai-browser-deployment related tasks.

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Design AI benchmarks for comparing models and tracking improvement. TRIGGERS - Use when user needs help with ai-benchmark-creator related tasks.

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Create benchmark suites for AI model comparison. TRIGGERS - Use when user needs help with ai-benchmark-suite related tasks.

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Design AI A/B testing systems for prompts and models. TRIGGERS - Use when user needs help with ai-ab-test-system related tasks.

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Imports FBX meshes into CARLA on UE 5.8 as spawnable props — drives the editor headlessly to import the mesh, derives the prop size from its measured bounds, and registers it in PropParameters.json so it appears in the blueprint library as static.prop.<name>. Handles the FBX unit-scale problem, verifies the result against a running server, and can revert cleanly. Use when the user asks to "import a prop", "add a custom mesh/object to CARLA", "make my model spawnable", or a prop imports but never shows up in the blueprint library.

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Runs the Autoware autonomous-driving stack against CARLA on UE 5.8 over CARLA's native ROS 2 interface — no bridge process. Reports the DDS contract (rt/vehicle/status/* out, rt/control/command/* in), the Autoware-only sensor blueprints, generates the lanelet2 + point-cloud map artifacts Autoware needs instead of an .xodr, and drives the two shipped modes (classical NDT stack, or the camera-only VAD end-to-end model). Use when the user asks to "run Autoware on CARLA", "connect an AV stack", "drive the ego from Autoware", "generate a lanelet2 map", or asks which topics CARLA exchanges with Autoware.

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Imports an FBX vehicle into a CARLA source build as a spawnable vehicle.<make>.<model> — checks the FBX carries CARLA's 4-wheel bone rig, imports the mesh, then assembles the vehicle through CarlaTools' VehicleAuthoringLibrary (physics asset with kinematic wheel bodies, retargeted anim blueprint, four configured wheel blueprints, the vehicle blueprint), registers it in VehicleFactory through CARLA's own add_vehicle_to_vehicle_factory.py, and verifies it spawns, drives and steers. Use when the user asks to "add a car/truck/van to CARLA", "import a vehicle FBX", or needs a custom drivable vehicle spawnable from the Python API.

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。