DART CI/CD troubleshooting - GitHub Actions, cache debugging, platform-specific failures
中文优先：用于DartFlutter模式相关任务，帮助识别、设计、实现或验证对应工作流。English keywords: Production-ready Dart and Flutter patterns covering null safety, immutable state, async composition, widget…
Define and generate mock objects for external dependencies using `package:mockito` and `build_runner`.
Dart/Flutter MCP tools reference for app lifecycle, hot reload/restart, logging, widget inspection, and code analysis. Use when debugging Flutter applications.
Guidelines and best practices for refactoring consecutive prints, single-line string concatenations, and complex output blocks into triple-quoted multi-line string literals ('''…
Understand and improve test coverage in a Dart package. Helps agents run coverage, interpret results, and identify missed lines.
Core concepts and best practices for `package:test`. Covers `test`, `group`, lifecycle methods (`setUp`, `tearDown`), and configuration (`dart_test.yaml`).
Provides comprehensive darts scoring rules and board structure knowledge. Use when implementing or testing darts scoring logic, validation, simulation, or any feature related to…
Hybrid cognitive architecture combining Darwinian evolution with Gödel Machine self-improvement for maximum reasoning power.
Build production-grade interactive dashboards with Plotly Dash - enterprise features, callbacks, and scalable deployment
Proper authentication patterns for dashboard frontend API calls. Use when adding new API endpoints, creating components that fetch data, or debugging 401 Unauthorized errors.
Next.js dashboard patterns including chat-first UI, D3.js visualizations, and API proxy architecture
Quick dashboard health and status overview — checks the Agent Monitor API (port 4820), reports session/agent/event counts from /api/stats, confirms WebSocket connectivity,…
Comprehensive data science, machine learning, and AI guide covering Python, deep learning, NLP, LLMs, prompt engineering, and MLOps.
Master machine learning, data engineering, AI engineering, LLMs, prompt engineering, and MLOps. Build intelligent systems with Python.
A skill for analyzing data using Python (pandas) and generating professional visualizations (matplotlib/seaborn).
Create data pipeline and analytics architecture diagrams using PlantUML syntax with database/analytics stencil icons.
Review or assess a data architecture, data model, or data platform design. Evaluates six data-specific quality attributes (DAMA-DMBOK aligned), topology choice, data contracts,…
Single source of truth patterns, facts.ts structure, type safety, and data helper functions. Use when working with project data or adding new facts.
Use when planning how to archive aging Salesforce records to reduce storage costs, maintain query performance, and meet retention policies.
Using data-* attributes as the HTML/CSS/JS bridge for state, variants, and configuration. Use when managing element state, styling variants, or configuring behavior without…
Update data-cleaning-implementation skill with critical pandas patterns and testing learnings from vp-e62a
Use when designing or evaluating a Data Cloud implementation architecture — covering data lake strategy (DSO/DLO/DMO layers), identity resolution rule design, activation target…
Use when choosing or explaining how Salesforce Data Cloud (Data 360) and CRM Analytics (Tableau CRM / Einstein Analytics) fit together versus overlap — unified data platform vs…
Investigate mismatches, duplicates, missing records, broken keys, and reconciliation defects using structured SQL or Python diagnostics.
Build scalable data pipelines, modern data warehouses, and real-time streaming architectures. Implements Apache Spark, dbt, Airflow, and cloud-native data platforms.
Use when user needs scalable data pipeline development, ETL/ELT implementation, or data infrastructure design.
Data engineering patterns for ETL pipelines, data warehousing, Apache Spark, and data quality validation
Build features guided by data insights, A/B testing, and continuous measurement using specialized agents for analysis, implementation, and experimentation.
You are a data pipeline architecture expert specializing in scalable, reliable, and cost-effective data pipelines for batch and streaming data processing.
Common patterns for extracting and combining analytics data from GA4, GSC, and SE Ranking. Includes API patterns, rate limiting, caching, and error handling.
Best practices and conventions for server-side data fetching, caching, and rendering in Next.js 16+ applications.
LobeHub data-fetching pipeline guide. Use for service layer, Zustand store, SWR, lambdaClient, useClientDataSWR, useFetchXxx hooks, or migrating useEffect fetches.
Architect data fetching with TanStack Query v5, SWR, optimistic updates, prefetching, and cache invalidation strategies.
Use Python to profile exports, find data quality issues, inspect bad records, and summarize findings in plain language.
Skill professionnel pour Claude Code permettant d'accéder, télécharger et analyser les données ouvertes françaises via data.gouv.fr.
Use when planning, reviewing, or troubleshooting Salesforce data imports, migrations, and bulk updates.
Use when planning a product feature, fix, or prioritization task where Pendo usage data, signals, issues, or customer feedback could ground the plan in real product behavior —…
Build data ingestion pipelines for batch and streaming data from multiple sources. Covers extraction strategies, format normalization, deduplication, validation gates, and staging…
Use this agent when you need to review database migrations, data models, or any code that manipulates persistent data.
Guidelines for data analysis and Jupyter Notebook development with pandas, matplotlib, seaborn, and numpy.
Patterns Apache Kafka — topics, partitions, consumer groups, exactly-once semantics et Kafka Streams.
Provides architectural guidance for data lake design including partitioning strategies, storage layout, schema design, and lakehouse patterns.
Data Lake architecture and management including medallion architecture (bronze/silver/gold zones), data catalog with AWS Glue, partitioning strategies, schema evolution, data…
Data Mesh architecture patterns — domain ownership, data products with SLOs, self-serve platform design, Delta Lake vs Iceberg, federated Trino queries, data contracts,…
Use when planning, reviewing, or troubleshooting a Salesforce data migration — covering tool selection (Data Loader, Bulk API 2.0, MuleSoft, Informatica, Jitterbit), migration…
Create safe, reversible database migration scripts with rollback capabilities, data validation, and zero-downtime deployments.
Analyze and Refactor TypeScript data models. Detect duplication, improve type safety, and validate relationships in interfaces/types.
Database schema design and data modeling patterns including normalization principles (1NF-5NF), denormalization trade-offs, entity relationship design, indexing strategies, schema…
Designs and builds ETL/ELT data pipelines. Takes data sources, destination, transformation requirements.
트리거: "데이터 파이프라인", "celery task", "kafka consumer", "파이프라인 만들어줘", "비동기 작업", "airflow dag", "airflow 만들어줘", "배치 파이프라인", "rabbitmq", "메시지 큐", "etl 파이프라인", "데이터 처리", "스케줄러 만들어줘" 수행:…
Review or design a data pipeline architecture. Assesses ingestion pattern, transformation design, orchestration, idempotency, freshness SLAs, data contracts at boundaries, dbt…
Process JSON with jq and YAML/TOML with yq. Filter, transform, query structured data efficiently. Triggers on: parse JSON, extract from YAML, query config, Docker Compose, K8s…
Comprehensive guide to data quality validation, testing frameworks, anomaly detection, and data observability for production data pipelines
中文优先：用于数据抓取智能体相关任务，帮助识别、设计、实现或验证对应工作流。English keywords: Build a fully automated AI-powered data collection agent for any public source — job boards, prices, news, GitHub, sports,…
Create database seed scripts with realistic test data for development and testing. Use when setting up development environment or creating demo data.
Generates deterministic seed data for development and testing with factory functions, realistic fixtures, and database reset scripts.
Use when creating test data for scratch orgs, sandboxes, or CI pipelines: Apex @testSetup factories, sf data import tree plans, CumulusCI datasets, Snowfakery.
Write correct, performant SQL across all major data warehouse dialects (Snowflake, BigQuery, Databricks, PostgreSQL, etc.).
Guide Blumark24 OS data storage decisions, tenant-bound data, sensitive-data handling, and avoiding localStorage for business data.