「property」の検索結果

Use when you need data access with Micronaut Data — @MappedEntity, CrudRepository/PageableRepository, @Query with parameters, @Transactional services, projections, @Version, and @MicronautTest with TestPropertyProvider and Testcontainers. For raw java.sql access without generated repositories, use @511-frameworks-micronaut-jdbc. This should trigger for requests such as Review or implement Micronaut Data repositories and entities; Add transactions, pagination, or projections in Micronaut persistence layer; Model Micronaut Data entities and repositories; Review Micronaut Data transactions and pageable queries; Improve projections or optimistic locking with Micronaut Data. Part of Plinth Toolkit

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Use when you need Kafka messaging in Micronaut — including @KafkaClient and @KafkaListener design, @Serdeable serialization, topic/partition strategy, TestPropertyProvider integration tests, retries and dead-letter processing, and error handling. This should trigger for requests such as Add Kafka in Micronaut; Review Micronaut Kafka listeners; Improve retry and failure handling for Micronaut Kafka; Configure Micronaut Kafka clients listeners or SerDes; Add Micronaut Kafka retry or dead-letter handling. Part of Plinth Toolkit

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Use when you need to set up, review, or improve Java integration tests — including generating a BaseIntegrationTest.java with WireMock for HTTP stubs, detecting HTTP client infrastructure from import signals, injecting service coordinates dynamically via System.setProperty(), creating WireMock JSON mapping files with bodyFileName, isolating stubs per test method, verifying HTTP interactions, or eliminating anti-patterns such as Mockito-mocked HTTP clients or globally registered WireMock stubs. This should trigger for requests such as Review Java code for integration tests; Apply best practices for integration tests in Java code; Write Java integration tests with real infrastructure boundaries; Improve Testcontainers integration tests for Java code; Review integration test setup and teardown in Java projects. Part of Plinth Toolkit

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Routes Maven version questions to the right workflow by choosing project-local update report interpretation for a user’s own pom.xml, or Maven artifact discovery from maintainer-approved structured evidence. Use when interpreting dependency, plugin, or property update reports; finding Maven coordinates; verifying groupId artifactId version; browsing versions from approved evidence; or constructing artifact URLs without fetching remote content. Part of Plinth Toolkit

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Add a new OSGi configuration property to the oak-store-document module. Modifies Configuration.java, DocumentNodeStoreService.java, DocumentNodeStoreBuilder.java, RDBDocumentNodeStoreBuilder.java, Utils.java, and all relevant test files with 90%+ coverage.

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Molecular machine learning toolkit. Property prediction (ADMET, toxicity), GNNs (GCN, MPNN), MoleculeNet benchmarks, pretrained models, featurization, for drug discovery ML.

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Therapeutics Data Commons (TDC) AI-ready drug discovery datasets. Curated ADME, toxicity, DTI, DDI with scaffold/cold splits, standardized metrics, molecular oracles, and ADMET benchmarks for therapeutic ML and property prediction. For chemical database queries use chembl-database-bioactivity; for featurization use molfeat.

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PyTorch-based ML platform for drug discovery: graph molecular representation learning, property prediction (ADMET, activity), retrosynthesis, drug-target interaction (DTI), and pretraining on large molecular datasets. Provides GNN layers (GraphConv, GAT, MPNN), pretrained models, and benchmark datasets.

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Query the ZINC22 virtual compound library (CartBlanche API, billions of make-on-demand + purchasable molecules). Look up substances by ZINC ID, resolve a SMILES to its ZINC ID (exact match), inspect purchasability/catalogs, and assemble compound sets for docking. Property (MW/logP) filtering is done locally with RDKit. For bioactivity use chembl-database-bioactivity; for approved drugs use drugbank-database-access.

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Look up NYC ACRIS deeds, mortgages, liens, sales, and ownership history. Use for property transactions or recorded ownership; not permits or violations.

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Testing strategies, patterns, and tools for robotics software. Use this skill when writing unit tests, integration tests, simulation tests, or hardware-in-the-loop tests for robot systems. Trigger whenever the user mentions testing ROS nodes, pytest with ROS, launch_testing, simulation testing, CI/CD for robotics, test fixtures for sensors, mock hardware, deterministic replay, regression testing for robot behaviors, or validating perception/planning/control pipelines. Also covers property-based testing for kinematics, fuzz testing for message handlers, and golden-file testing for trajectories.

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Use DGL-LifeSci and dgllife for molecular graph learning, molecule featurization, datasets, property prediction, reaction prediction, binding affinity, model-zoo/pretrained models, and molecular generation workflows.

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Use Chemprop 2.2.3 for molecular property prediction: CLI training, prediction, fingerprints, data validation, Python APIs, reaction/atom-bond tasks, uncertainty, hpopt, and conversion workflows.

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Important: Before you begin, fill in the generatedBy property in the meta section of .actor/actor.json. Replace it with the tool and model you're currently using, such as "Claude Code with Claude Sonnet 4.5". This helps Apify monitor and improve AGENTS.md for specific AI tools and models.

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Use when introducing or correcting grouped Spring Boot configuration, typed property binding, validation, profiles or secret injection. Do not rewrite unrelated single-value configuration.

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Complete testing strategy covering TDD workflow, test pyramid, unit/integration/E2E/property testing, framework best practices (Jest, Vitest, pytest), mock strategies, and CI integration. Use when writing tests, reviewing test quality, or establishing testing standards.

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Translate natural-language requirements or structured specification documents into formal temporal logic properties (LTL, CTL, safety/liveness properties). Use when users need to formalize requirements for model checking, formal verification, or property specification. Handles embedded/real-time systems, hardware verification, concurrent systems, and reactive systems. Resolves ambiguities, asks clarifying questions when needed, and outputs machine-checkable formulas with explanations. Supports multiple output formats (SPIN, NuSMV, Uppaal, TLA+, Maude).

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Automatically repair C/C++ code violations detected by TLA+ model checking. Takes a program, TLA+ specification, and TLC counterexample trace as input, then generates minimal code modifications to eliminate the violation. Use when: (1) TLC model checker reports an invariant violation, deadlock, or temporal property failure, (2) You have a counterexample trace and need to fix the corresponding code, (3) You need to understand how a TLA+ violation maps to program-level bugs, (4) You want to validate repairs by re-running TLC. Supports safety properties (invariants), liveness properties (temporal logic), and deadlock detection.

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Automatically identify metamorphic properties (symmetry, linearity, additivity, input invariances) from programs or functions. Use when generating metamorphic tests, discovering program properties, validating transformations, or creating test oracles without explicit specifications. Analyzes control flow, data flow, and sample executions to output structured properties for metamorphic test generation and verification.

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Explain why counterexamples violate specifications by analyzing formal specifications (temporal logic, invariants, pre/postconditions, code contracts), informal requirements (user stories, acceptance criteria), test specifications (assertions, property-based tests), and providing step-by-step traces showing state changes, comparing expected vs actual behavior, identifying root causes, and assessing violation impact. Use when debugging test failures, understanding model checker output, explaining runtime assertion violations, analyzing static analysis warnings, or teaching specification concepts. Produces structured markdown explanations with traces, comparisons, state diagrams, and cause chains. Triggers when users ask why something failed, explain a violation, understand a counterexample, debug a specification, or analyze why a test fails.

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Automatically generates executable test cases from model checking counterexample traces. Translates abstract counterexample states and transitions into concrete test inputs, execution steps, and assertions that reproduce property violations. Use when working with model checker outputs (SPIN, CBMC, NuSMV, TLA+, Java PathFinder, etc.) and needing to create regression tests, validate bug fixes, or reproduce verification failures in executable test suites.

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Public-records OSINT: SEC, sanctions, courts, property.

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Think and work like an expert Logician. Use when a task calls for Logician judgment. Reasons from syntax-versus-semantics and object-versus-metalanguage discipline, the ⊢/⊨ distinction, and matching each logic to its intended semantics through natural deduction and sequent calculus (cut-elimination, subformula property), compactness and Löwenheim–Skolem, forcing and diagonalization, and proof assistants (Lean 4/mathlib, Coq, Isabelle/HOL, Agda) with Z3/CVC5/Vampire/Mace4 automation while treating use–mention conflation, second-order misuse of compactness, mis-stated incompleteness hypotheses (ω-consistency vs consistency), and Gödel philosophical overreach as first-class failure modes.

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Think and work like an expert Materials Chemist. Use when a task calls for Materials Chemist judgment. Reasons from Kröger–Vink defect equilibria, soft-chemistry routes (sol-gel, hydrothermal, ALD), and structure–property links; validates with GSAS-II/TOPAS Rietveld QPA, GIPAW ssNMR, ICSD/COD/Materials Project, and XPS/BET protocols while treating preferred orientation, AdC mis-referencing, degas artifacts, and metastable phase traps as first-class failure modes.

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。