「logic」の検索結果

Problem-solving strategies for predicate logic in mathematical logic

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。

Problem-solving strategies for propositional logic in mathematical logic

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。

This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.

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。

Create visualize finance logic diagrams (e.g., Draw.io XML) to explain complex finance transmission chains or finance logic flows.

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。

This skill is applicable when using LaminDB. LaminDB is an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR-compliant. It is suitable for managing biological datasets (scRNA-seq, spatial transcriptomics, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakes, or ensuring data lineage and reproducibility in biological research. It covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integration with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.

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。

Detects methodological gaps across study design, analysis, validation, bias control, reproducibility, and implementation readiness within a biomedical research area. Use this skill when a user wants to identify what current studies are still methodologically missing, which weaknesses are most consequential, and what upgrade path would produce a stronger next-step study. Always separate design gaps, analysis gaps, validation gaps, and reproducibility gaps. Never treat technical complexity as methodological rigor.

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 when targeting Environmental Health Perspectives or deciding whether an environmental-health manuscript fits this venue. Encodes the journal's fit, the public-health-relevance and methodological-rigor bar, epidemiological/toxicological expectations, house style, official-submission re-check, and desk-reject heuristics.

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 when targeting the Bulletin of the American Meteorological Society (BAMS) or deciding whether a broad-interest atmospheric/oceanic/hydrologic-sciences contribution fits this venue. Encodes the journal's review-and-community positioning, the broad-audience-significance bar, synthesis and field-campaign expectations, AMS house style, official-submission re-check, and misfit heuristics.

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 when asked to run Design Error Detection (quick defect scan), find design errors in a Simulink model, perform root cause analysis on DED findings, fix division-by-zero, overflow, dead logic or out-of-bounds defects detected by SLDV, or diagnose why missing coverage cannot be achieved (dead logic blocking coverage objectives). Do NOT use for requirement verification, test generation, Inf/NaN detection, active logic analysis, or coverage measurement.

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。

Estimates the minimum biological replicates (or cells/events) for a target power at a target FDR in genomics experiments using ssizeRNA, PROPER, powsimR for scRNA-seq, and pilot-data dispersion estimation from DESeq2/edgeR. Covers the biological-versus-technical replication distinction (technical replicates do not add degrees of freedom for biological inference), replicate-number-versus-sequencing-depth budgeting, scRNA-seq sample-versus-cell allocation under a pseudobulk model, and the critique that "n=3" is a publication convention rather than a power calculation. Use when budgeting a sequencing experiment, writing the sample-size justification in a grant, estimating replicates from pilot data, allocating a fixed budget between samples and depth, or planning scRNA-seq cohort size. For clinical-trial sample size see clinical-biostatistics/power-and-sample-size; for the power-given-n direction see experimental-design/power-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。

Creates, edits, and manages Power Pages Server Logic files — server-side JavaScript that runs securely on the Power Pages runtime. Orchestrates the full lifecycle: gathering requirements, fetching documentation, implementing code, configuring site settings, and deploying. Use when the user wants to add server-side code, create API endpoints, or move logic from the browser to the server in their Power Pages site.

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。

Analyze digital and analog captures from Saleae Logic MSO devices. Decode protocols like UART, SPI, I2C from exported binary files. Use when analyzing logic analyzer captures for CTF challenges, hardware reverse engineering, or protocol decoding.

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。

Expert blueprint for puzzle games including undo systems (Command pattern for state reversal), grid-based logic (Sokoban-style mechanics), non-verbal tutorials (teach through level design), win condition checking, state management, and visual feedback (instant confirmation of valid moves). Use for logic puzzles, physics puzzles, or match-3 games. Trigger keywords: puzzle_game, undo_system, command_pattern, grid_logic, non_verbal_tutorial, state_management.

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。

Applies Functional Core, Imperative Shell to isolate logic from side effects. Use when business logic is entangled with I/O or unit tests are slow and brittle.

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。

Business-logic flaw hunting - workflow/state bypass, price/quantity tampering, negative/overflow values, coupon/refund abuse, mass assignment, and logic races. The top-paying bug class with no scanner coverage. Wiki-first, FIND schema output.

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。

Alert Logic integration. Manage data, records, and automate workflows. Use when the user wants to interact with Alert Logic 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。

Think and work like an expert Geological Oceanographer. Use when a task calls for Geological Oceanographer judgment. Reasons from sediment transport mechanics, stratigraphic context, and accommodation through multibeam (EM122/EM712) and sub-bottom/seismic imaging, gravity and piston cores with XRF/X-ray CT, CTD tow-yo ORP/Mn/CH4 plume surveys, and Bouma/Lowe facies analysis, while treating bathymetry-mimicking multipath artifacts, bioturbation homogenizing event beds, glacial isostasy mistaken for eustasy, and single-core turbidite extrapolation 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。

Think and work like an expert Biological Oceanographer. Use when a task calls for Biological Oceanographer judgment. Reasons from light-nutrient-grazing coupling, the microbial loop, and size-structured export through CTD/MOCNESS sampling, 14C and O2/Ar production with 234Th export flux, imaging and flow cytometry enumeration, and SILVA/PR2 metabarcoding while treating spatial patchiness, diel-migration tow aliasing, CDOM-biased chlorophyll algorithms, and eDNA-detection-as-abundance 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。

Query Reactome for biological pathways and reactions. Use when user asks about signaling cascades, biological processes, pathway diagrams, or reaction details. Triggers on "reactome", "signaling cascade", "biological pathway", "pathway diagram", "reaction mechanism".

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 this skill for the architecture phases of an MBSE workflow in MATLAB, when writing idempotent buildXxx.m scripts that produce a three-layer RFLPV architecture (Functional, Logical, Physical) with interface dictionaries, stereotype profiles, allocation sets, and requirements Implement links. Trigger for defining stereotype properties, functional-to-logical / logical-to-physical allocation, mapping requirements to components via slreq Implement links, or running quantitative roll-up analysis on the architecture. Do NOT trigger for ad-hoc structural edits to an already-built System Composer model (adding one component, rewiring a port) — use `building-simulink-models` with `model_edit` for that. Works alongside the `system-composer` skill for detailed SC API 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。

AI-powered Claude Code skill that performs deep code review using formal logic and reasoning frameworks to detect bugs, anti-patterns, and security risks beyond what linters catch.

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。

First-order logic with quantifiers, predicates, functions, and identity. Covers syntax, interpretation over a domain, the universal and existential quantifiers, quantifier scope and binding, translation from natural language with multi-variable predicates, natural deduction for quantifiers, undecidability at first order, and the soundness/completeness theorems. Use when propositional logic is insufficient -- whenever "for all" or "there exists" matters to the argument.

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。

The logic of sentential connectives -- and, or, not, if-then, if-and-only-if -- as a formal system. Covers syntax (well-formed formulas), semantics (truth tables), satisfiability, validity, logical equivalence, normal forms (CNF, DNF), natural-deduction rules, and common translation pitfalls. Use when formalizing arguments with propositional structure, checking validity mechanically, or teaching the foundations of formal reasoning.

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。

Extending propositional and predicate logic with modal operators -- necessity and possibility, and their relatives (obligation, knowledge, belief, time). Covers Kripke possible-worlds semantics, accessibility relations, the main modal systems (K, T, S4, S5), translation from natural language, and applications in epistemic, deontic, and temporal reasoning. Use when ordinary logic is insufficient to capture distinctions like "must" vs "might," "knows" vs "believes," "always" vs "sometimes."

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。