Results for “ia modular”

Use when you need to generate Java project diagrams — including UML sequence diagrams, UML class diagrams, C4 model diagrams, UML state machine diagrams, UML Deployment Diagrams, ER (Entity Relationship) diagrams, and bounded-context diagrams — through a modular, step-based interactive process that adapts to your specific visualization needs. This should trigger for requests such as Generate UML diagram; Create sequence diagram; Create class diagram; Create state machine diagram; Create deployment diagram; Create C4 diagram; Create bounded-context diagram; Create context-map diagram. Part of Plinth Toolkit

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Design a modular monolith, a single application with clear business boundaries. Plan module structure, external service integration, failure handling, and future separation, then produce an HTML architecture document with SVG diagrams.

• Design business boundaries for a new backend
• Plan integration that limits dependence on an external vendor
• Define retries and failure handling for external calls

Design and implement a NestJS backend organized into business modules within one deployable application. The skill defines module boundaries, separates data ownership, builds layered code and event communication, and validates isolation through checks and tests.

• Designing a new modular NestJS backend
• Implementing business modules with layered responsibilities
• Connecting modules through events

Predicts whether a DNA variant alters mRNA splicing using sequence-based deep-learning tools — SpliceAI (10kb context dilated CNN, clinical default), Pangolin (multi-tissue), MMSplice (modular per-region CNN with calibrated ΔPSI), SpliceTransformer/TrASPr (tissue-aware transformers), SpliceVault (empirical 300K-RNA lookup of likely mis-splicing outcomes), CADD-Splice (composite score). Applies the ClinGen SVI 2023 framework for ACMG/AMP variant interpretation (PVS1, PP3, BP4 evidence codes), HGVS splicing nomenclature (c.123+1G>A, c.123-3T>G, r.spl?), extended-window scoring for deep-intronic pseudoexons, tissue-specific predictions, branchpoint variant detection (BPHunter, LaBranchoR), and splice-switching ASO design. Use when interpreting splice impact of clinical variants, prioritizing VUS, identifying deep-intronic pathogenic variants, or designing ASOs.

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Predicts whether a DNA variant alters mRNA splicing using sequence-based deep-learning tools — SpliceAI (10kb context dilated CNN, clinical default), Pangolin (multi-tissue), MMSplice (modular per-region CNN with calibrated ΔPSI), SpliceTransformer/TrASPr (tissue-aware transformers), SpliceVault (empirical 300K-RNA lookup of likely mis-splicing outcomes), CADD-Splice (composite score). Applies the ClinGen SVI 2023 framework for ACMG/AMP variant interpretation (PVS1, PP3, BP4 evidence codes), HGVS splicing nomenclature (c.123+1G>A, c.123-3T>G, r.spl?), extended-window scoring for deep-intronic pseudoexons, tissue-specific predictions, branchpoint variant detection (BPHunter, LaBranchoR), and splice-switching ASO design. Use when interpreting splice impact of clinical variants, prioritizing VUS, identifying deep-intronic pathogenic variants, or designing ASOs.

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Predicts whether a DNA variant alters mRNA splicing using sequence-based deep-learning tools — SpliceAI (10kb context dilated CNN, clinical default), Pangolin (multi-tissue), MMSplice (modular per-region CNN with calibrated ΔPSI), SpliceTransformer/TrASPr (tissue-aware transformers), SpliceVault (empirical 300K-RNA lookup of likely mis-splicing outcomes), CADD-Splice (composite score). Applies the ClinGen SVI 2023 framework for ACMG/AMP variant interpretation (PVS1, PP3, BP4 evidence codes), HGVS splicing nomenclature (c.123+1G>A, c.123-3T>G, r.spl?), extended-window scoring for deep-intronic pseudoexons, tissue-specific predictions, branchpoint variant detection (BPHunter, LaBranchoR), and splice-switching ASO design. Use when interpreting splice impact of clinical variants, prioritizing VUS, identifying deep-intronic pathogenic variants, or designing ASOs.

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Design and review how a system is divided into modules, cohesive parts with clear responsibilities. Evaluate data ownership, communication rules, dependencies, and failure handling, and decide when to split or merge parts without assuming a particular technology.

• Define responsibilities and boundaries for a new module
• Review dependencies between system modules
• Clarify data ownership and communication rules

Analyzes species-environment relationships with constrained ordination (CCA, RDA, db-RDA), variance partitioning, indicator species (indicspecies IndVal.g group-equalized), PERMANOVA paired MANDATORILY with PERMDISP (Anderson & Walsh 2013; dispersion confounds centroid tests), Joint Species Distribution Models (HMSC, sjSDM, gjam) with explicit rejection of "residual covariance equals biotic interaction", phylogenetic community ecology (SES_MPD/MNTD), trait-environment via RLQ + fourth-corner with corrected modeltype=6 (Dray 2014), bipartite network metrics (NODF, modularity) with curveball null (Strona 2014), and Mantel-test replacements (dbRDA, GDM) for spatial data. Use when testing how environmental gradients structure communities, identifying habitat indicator taxa, partitioning variance among predictors, deciding whether PERMANOVA significance is location vs dispersion, picking among HMSC/sjSDM/gjam, or replacing Mantel tests for landscape data.

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Reference patterns for mathematical and combinatorial problem-solving. Covers modular arithmetic, prime sieves, GCD/LCM, binomial coefficients, fast exponentiation, counting techniques, inclusion-exclusion, and game theory. Load this skill when a problem involves number theory, combinatorics, modular operations, or strategic game analysis.

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Analyzes species-environment relationships with constrained ordination (CCA, RDA, db-RDA), variance partitioning, indicator species (indicspecies IndVal.g group-equalized), PERMANOVA paired MANDATORILY with PERMDISP (Anderson & Walsh 2013; dispersion confounds centroid tests), Joint Species Distribution Models (HMSC, sjSDM, gjam) with explicit rejection of "residual covariance equals biotic interaction", phylogenetic community ecology (SES_MPD/MNTD), trait-environment via RLQ + fourth-corner with corrected modeltype=6 (Dray 2014), bipartite network metrics (NODF, modularity) with curveball null (Strona 2014), and Mantel-test replacements (dbRDA, GDM) for spatial data. Use when testing how environmental gradients structure communities, identifying habitat indicator taxa, partitioning variance among predictors, deciding whether PERMANOVA significance is location vs dispersion, picking among HMSC/sjSDM/gjam, or replacing Mantel tests for landscape data.

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Analyzes species-environment relationships with constrained ordination (CCA, RDA, db-RDA), variance partitioning, indicator species (indicspecies IndVal.g group-equalized), PERMANOVA paired MANDATORILY with PERMDISP (Anderson & Walsh 2013; dispersion confounds centroid tests), Joint Species Distribution Models (HMSC, sjSDM, gjam) with explicit rejection of "residual covariance equals biotic interaction", phylogenetic community ecology (SES_MPD/MNTD), trait-environment via RLQ + fourth-corner with corrected modeltype=6 (Dray 2014), bipartite network metrics (NODF, modularity) with curveball null (Strona 2014), and Mantel-test replacements (dbRDA, GDM) for spatial data. Use when testing how environmental gradients structure communities, identifying habitat indicator taxa, partitioning variance among predictors, deciding whether PERMANOVA significance is location vs dispersion, picking among HMSC/sjSDM/gjam, or replacing Mantel tests for landscape data.

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Analyzes species-environment relationships with constrained ordination (CCA, RDA, db-RDA), variance partitioning, indicator species (indicspecies IndVal.g group-equalized), PERMANOVA paired MANDATORILY with PERMDISP (Anderson & Walsh 2013; dispersion confounds centroid tests), Joint Species Distribution Models (HMSC, sjSDM, gjam) with explicit rejection of "residual covariance equals biotic interaction", phylogenetic community ecology (SES_MPD/MNTD), trait-environment via RLQ + fourth-corner with corrected modeltype=6 (Dray 2014), bipartite network metrics (NODF, modularity) with curveball null (Strona 2014), and Mantel-test replacements (dbRDA, GDM) for spatial data. Use when testing how environmental gradients structure communities, identifying habitat indicator taxa, partitioning variance among predictors, deciding whether PERMANOVA significance is location vs dispersion, picking among HMSC/sjSDM/gjam, or replacing Mantel tests for landscape data.

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Package an existing custom diffusers block into a directory ready for Hub upload. Select a class from a Python file and save its source, configuration, and associated files so others can load the block.

• Preparing a custom block for Hub distribution
• Selecting one block class from a Python file
• Creating the directory layout needed to load a shared block

Construa sistemas de IA complexos com programação declarativa, otimize prompts automaticamente, crie sistemas RAG modulares e agentes com DSPy - framework de Programação Sistemática de Modelos de Linguagem da Stanford NLP

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Use for AI-assisted Unity work that needs live repo discovery, project-derived routing, runtime-owner proof, runtime-visible output hard stops, runtime numeric proof for repeated visible-output failures, state-step guards, multi-agent scope ownership, modular C#/asmdef safety, UI/scene/visual asset gates, data-first content changes, validation, cleanup proof, or durable workflow rules. Best when agents must prove the actual folder/module/scene/prefab/runtime owner before editing, especially runtime UI, generated assets, code graphs, tutorial/state flows, guided selection/action flows, overlay/dim source-bound mistakes, coordinate conversions, focus/highlight/marker/HUD alignment, predictive lead aiming, ballistic trajectories, spatial IoU verification, or repeated "fix still not visible" failures.

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Think and work like an expert Critical Care Researcher. Use when a task calls for Critical Care Researcher judgment. Reasons from acute physiology trajectories, modular organ dysfunction, and cluster-aware trial design through APACHE/SAPS/SOFA scoring, Berlin/Sepsis-3/KDIGO definitions, MIMIC-IV phenotyping, and target-trial emulation with clone-censor-weighting while treating immortal-time bias, cluster contamination, competing risks from early death, and sepsis-phenotype cohort inflation as first-class failure modes.

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Symbolic manipulation, equation solving, and algebraic structures for mathematical reasoning. Covers distributive law, factoring, completing the square, linear through polynomial equation solving, systems of equations (substitution, elimination, Gaussian elimination, matrix methods), algebraic structures (groups, rings, fields), modular arithmetic, polynomial theory, and inequalities. Use when solving equations, simplifying expressions, working with algebraic structures, or performing symbolic manipulation.

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Build editorial/magazine/report webpages on a GENUINE Müller-Brockmann modular grid (International Typographic Style). Encodes the discipline (columns + modules + baseline, grotesque type, flush-left, restrained black/white/red palette) AND the front-end engineering to make the grid real, visible, and verified: one CSS-variable source of truth, an interactive grid-toggle overlay, subgrid bands, 8px baseline lock, and runtime optical alignment. Triggers: "magazine spread", "grid system", "Swiss design", "editorial layout", "show the grid", "align everything to the grid".

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Define and deploy Azure infrastructure with ARM templates and Bicep, languages for describing cloud resources in files. Get guidance on reusable modules, resource dependencies, environment parameters, and previewing deployment changes before applying them.

• Defining Azure networks and virtual machines in code
• Sharing modules across development and production
• Previewing infrastructure changes with What-If

Build Splunk pipelines that import threat intelligence and match security events against indicators such as malicious IP addresses, domains, and file hashes. Add source, confidence, and severity details so analysts have context when investigating matches.

• Importing external threat feeds into Splunk
• Matching network and endpoint logs against threat indicators
• Adding threat and asset context to alerts

Operational arsenal for authorized external red-team and bug-bounty recon. Concrete probes, wordlists, regexes, dorks, curl one-liners for: subdomain enum, GraphQL/Swagger/REST discovery, identity fabric (Entra/Okta/ADFS/Google/SAML/M365 deep — Teams/SharePoint/OneDrive), cloud bucket enum (S3/GCS/Azure), CDN/WAF bypass, origin discovery, vendor fingerprinting (Citrix/F5/Pulse/Fortinet/PaloAlto/Cisco/VMware), CI/CD exposure, 48-pattern secret-scan catalog (AWS/GCP/GitHub/Stripe/Slack/Anthropic/OpenAI/Atlassian/DataDog/npm/PyPI), Postman workspaces, breach correlation (HudsonRock/HIBP/DeHashed/IntelX), TLS/JA3 audit, certificate transparency, JS endpoint extraction, package registry leaks, mobile/APK recon, sat imagery, sector-specific recon (healthcare DICOM, finance SWIFT, ICS/SCADA Modbus/BACnet). Detail content in 15 modular reference files, loaded on demand. Use for any authorized recon: scoping, asset discovery, attack-path mapping, secret triage, severity scoring.

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Use when building a Unity 6.3 level or open world: terrain from code or heightmaps, procedural islands, erosion, splat and foliage rules, trees and grass, ProBuilder graybox, level gym, modular kit and prefab placement, set dressing, landmarks and player guidance, the island's edge, spline roads, WFC, fog, additive scene streaming, or a world that hitches, pops in, leaks memory or runs slow on mobile. Keywords: Terrain, TerrainData, ProBuilder, blockout, graybox, modular kit, NavMesh, LoadSceneAsync, open world.

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Use when building or fixing an Unreal Engine 5 level or open world: World Partition, OFPA, Data Layers, Level Instances, Packed Level Actors, HLOD, landscape size, heightmap import or clipping, edit layers, terrain material rules, foliage and grass, a PCG forest or rock scatter, runtime GPU grass, Nanite and tessellation choices, modular kits, a river or village, streaming pop-in, cells not loading, landscape black spots, "PCG shows nothing in the editor", or a world too heavy to hand to lighting.

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AAA environment art for modular kits, buildings, terrain, vegetation, architecture, and scene composition. Use for game-optimized environments, storytelling through space, and modular asset pipelines in Blender via MCP.

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