Results for “mapping”

Reads an OpenAPI 3.0 specification and manually implements a type-safe Dart API layer using Dio for HTTP, Freezed or Equatable for models, and json_serializable for serialisation. Use when given an OpenAPI/Swagger file (JSON or YAML) and asked to implement the API in Flutter/Dart, create Dart models from an API schema, build a Dio API client, implement endpoints from a spec, convert openapi to dart, or set up a data/network layer from an API contract. Handles $ref resolution, oneOf union types, enums, query params, request bodies, and DioException error mapping.

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Implements BLoC (v9.x) event-driven state management for Flutter with unidirectional data flow using the flutter_bloc package. Use when implementing Bloc/Cubit classes, mapping Events to States, consuming state with BlocBuilder/BlocListener/BlocConsumer, applying event transformers (restartable/droppable/sequential) for concurrency control, setting up BlocProvider/MultiBlocProvider DI, integrating Freezed for sealed union types and exhaustive pattern matching, or testing with blocTest. Ideal for enterprise apps requiring business logic separation, precise concurrency control (search debouncing, form deduplication), or event replay debugging.

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Enforces error handling standards: silent error prohibition, custom error class hierarchy (AppError base with category never-fallback / transient / permanent, cause preservation, ValidationError / UnauthorizedError / UpstreamError etc. — canonical definitions in ERRORS.md), external-boundary normalization (normalizeExternalError), Result pattern (Result.ok/Result.fail), proper try-catch with error type checking, structured error logging with a single Logger contract, fallback prohibition categories (isNeverFallback), and HTTP status code mapping. Use when implementing error handling, reviewing catch blocks, or designing error responses.

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Cinematic lighting: production-level key/fill/rim, ambient, shadows, tone mapping. Use when implementing, reviewing or testing cinematic lighting for React, Vite, Next.js or Perfect_AI projects.

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VibeFarsi design themes: production-level token mapping and palette integration. Use when implementing, reviewing or testing vibefarsi design themes for React, Vite, Next.js or Perfect_AI projects.

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Color science: production-level linear sRGB, Oklab, OKLCH gamut mapping. Use when implementing, reviewing or testing color science for React, Vite, Next.js or Perfect_AI projects.

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Build comprehensive attack trees to visualize threat paths. Use when mapping attack scenarios, identifying defense gaps, or communicating security risks to stakeholders.

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Generate or refresh docs/ARCHITECTURE.md from current codebase state with evidence-backed component and data-flow mapping

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Context-aware remapping of stale parent shard references to new sub-shard targets after shard split operations

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Systematic methodology for verifying that IA, BE, and FE specification layers are mutually consistent. Covers coverage matrices, field mapping checks, error code propagation, access control consistency, and data contract drift detection.

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Analyzes codebase structure, data flow, module relationships, and key patterns to generate and maintain a living architecture document (ARCHITECTURE.md).

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Use when working with Codeowners — cODEOWNERS file management covering ownership mapping analysis, coverage gap detection, team assignment validation, orphaned path identification, and review requirement auditing. Use when auditing code ownership across repositories, identifying unowned code paths, validating team configurations, or optimizing pull request review workflows.

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Use when working with Deno Deploy Deep — deep Deno Deploy analysis covering project inventory, deployment history, KV database usage, cron job schedules, custom domain mappings, analytics metrics, and environment variable auditing. Use for comprehensive Deno Deploy platform management.

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Use when working with Cucumber — cucumber BDD testing management and analysis. Covers feature file organization, step definition mapping, scenario tagging, Gherkin syntax validation, test report parsing, and step reuse analysis. Use when managing Cucumber test suites, investigating BDD scenario failures, or reviewing feature file coverage.

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Use when working with Census — census reverse ETL management — monitor syncs, destinations, models, source connections, and sync run health. Use when debugging sync failures, inspecting record delivery, auditing destination mappings, or reviewing sync schedules.

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Use when working with Cloudflare Workers Deep — deep analysis of Cloudflare Workers deployments including script inventory, route mappings, KV namespace usage, Durable Objects, cron triggers, CPU time metrics, and error rate tracking. Use for comprehensive Workers platform health checks and optimization.

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Use when working with Caprover — capRover self-hosted PaaS management covering app inventory, container status, custom domain mappings, persistent directory mounts, environment variable auditing, cluster node status, Docker registry integration, and Nginx configuration review. Use for managing CapRover infrastructure.

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Use when working with Aws Lambda Deep — deep AWS Lambda analysis covering function inventory, cold start profiling, memory optimization, concurrency patterns, layer dependency auditing, dead letter queue health, event source mappings, and cost estimation. Goes beyond basic metrics to identify optimization opportunities.

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Use when working with Kubescape — kubescape Kubernetes security posture analysis. Covers NSA/CISA framework assessment, MITRE ATT&CK mapping, CIS Kubernetes benchmarks, workload scanning, RBAC analysis, and network policy evaluation. Use when assessing Kubernetes cluster security, evaluating compliance frameworks, or analyzing workload configurations.

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Use when working with Aws Eks — aWS EKS cluster management, nodegroup status, addon management, IRSA configuration, and cluster health analysis. Covers Kubernetes version tracking, nodegroup scaling, addon compatibility, IAM role mapping, and control plane logging.

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Build comprehensive attack trees to visualize threat paths. Use when mapping attack scenarios, identifying defense gaps, or communicating security risks to stakeholders.

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Core Python library for astronomy/astrophysics: units with dimensional analysis, celestial coordinate transforms (ICRS/Galactic/AltAz/FK5), FITS I/O, tables (FITS/HDF5/VOTable/CSV), cosmology (Planck18, distance/age), precise time (UTC/TAI/TT/TDB, Julian, barycentric), WCS pixel-world mapping, model fitting. For general tables use pandas/polars; for radio interferometry use CASA.

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Route Tavily web-search, extraction, mapping, crawling, and cited-research requests to the narrowest `tvly` command. Use for command-line Tavily work, installation checks, authentication setup, or choosing among the specialized Tavily skills.

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Build or review production-ready Tavily SDK and API integrations for web search, extraction, crawling, mapping, and research. Use when implementing Tavily in an agent, RAG pipeline, or application rather than only running one CLI command.

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