Total Skills
14
Author Profile: AlexYedi
Skills published by AlexYedi with real stars/downloads and source-aware metadata.
Total Stars
14
Total Downloads
0
Skills Performance
Comparison chart based on real stars and downloads signals from source data.
database-designer
1
Stars
1
Downloads
0
data-quality-auditor
1
Stars
1
Downloads
0
mdm-and-federated-data-governance
1
Stars
1
Downloads
0
knowledge-graph-applications
1
Stars
1
Downloads
0
knowledge-graph-modeling
1
Stars
1
Downloads
0
knowledge-graph-platform-integration
1
Stars
1
Downloads
0
rag-architect
1
Stars
1
Downloads
0
data-architecture-frameworks
1
Stars
1
Downloads
0
Published Skills
database-designer
Use when the user asks to design database schemas, plan data migrations, optimize queries, choose between SQL and NoSQL, or model data relationships.
Repository SourceNeeds Review
data-quality-auditor
Audit datasets for completeness, consistency, accuracy, and validity. Profile data distributions, detect anomalies and outliers, surface structural issues, and produce an actionable remediation plan.
Repository SourceNeeds Review
mdm-and-federated-data-governance
Apply Master Data Management (MDM) styles (Consolidation, Registry, Centralized, Coexistence), federated governance via data contracts and policy automation, data catalog + metalake architecture, knowledge graphs for metadata, semantic layers, and access control models (ACL, RBAC, ABAC + PEP/PDP/PIP/PAP). Use when scoping MDM, choosing an MDM style, designing a data catalog, building governance automation, defining data contracts, or implementing fine-grained access control on data products. Triggers: "MDM strategy", "consolidation vs registry vs centralized vs coexistence", "data contract", "data catalog", "knowledge graph for metadata", "ABAC for data", "semantic layer for governance", "metalake". Produces a chosen MDM style + governance architecture with policy automation.
Repository SourceNeeds Review
knowledge-graph-applications
Apply knowledge graph patterns for real applications: identity resolution (strong vs weak identifiers, Connected Components, SIMILAR), fraud detection (fraud rings, legitimate households), organizational graphs (org charts, expertise/skills graphs), dependency analysis (chains, multidependencies, redundant, SPOF, root cause), entity-based search, document similarity, and natural-language query/generation. Use when solving fraud detection, organizational analytics, dependency analysis, semantic search, or natural-language interfaces over a knowledge graph. Triggers: "fraud ring detection", "expertise graph", "single point of failure analysis", "root cause analysis with graphs", "entity-based search", "semantic search", "natural language to Cypher". Produces a pattern + query approach.
Repository SourceNeeds Review
knowledge-graph-modeling
Model knowledge graphs with the right data model — Plain Old Graph, Property Graph, Labeled Property Graph — plus organizing principles (Taxonomies, Ontologies), Just-Enough-Semantics, federation, and virtualization (LOAD CSV, neo4j-admin import, APOC virtual nodes, Composite Databases). Use when scoping a knowledge graph, choosing property graph vs RDF, deciding when to use ontology vs taxonomy, loading initial data, or federating multiple graphs. Triggers: "model a knowledge graph", "property graph vs RDF", "taxonomy vs ontology", "load data into Neo4j", "graph federation", "data virtualization for graphs", "Schema.org vs custom ontology". Produces a graph model + import strategy + organizing principle.
Repository SourceNeeds Review
knowledge-graph-platform-integration
Integrate knowledge graphs with the data platform: ETL workflows, Kafka Connect (Neo4j Streams), Apache Spark connectors, GraphQL APIs, user-defined procedures (UDFs), Graph Data Science (GDS) algorithms, in-graph ML pipelines, entity resolution workflows (data prep + matching + curation via WCC), metadata knowledge graph hubs, and data fabric with virtualization platforms (Dremio, Denodo). Use when wiring a KG into the broader data platform, designing entity resolution, exposing the KG via GraphQL, building ML on graph features. Triggers: "Neo4j Spark connector", "Kafka Connect for Neo4j", "GraphQL API on Neo4j", "Graph Data Science", "entity resolution with WCC", "metadata knowledge graph", "data fabric for graphs". Produces an integration architecture.
Repository SourceNeeds Review
rag-architect
Use when the user asks to design RAG pipelines, optimize retrieval strategies, choose embedding models, implement vector search, or build knowledge retrieval systems.
Repository SourceNeeds Review
data-architecture-frameworks
Pick and apply data architecture frameworks: TOGAF, DAMA-DMBOK, AWS Well-Architected (six pillars), Google's Five Cloud-Native Principles, Lambda vs Kappa, Modern Data Stack vs Live Data Stack, Data Mesh, Strangler Fig, FinOps, plus build-vs-buy and the four tech-selection axes (size, speed, cost, integration). Use when designing or evaluating a data platform's high-level architecture, deciding monolith vs modular vs serverless, or choosing between Lambda and Kappa for streaming workloads. Triggers: "Lambda or Kappa", "Modern Data Stack vs Data Mesh", "build vs buy data tooling", "evaluate our data architecture", "AWS Well-Architected for data", "Strangler Fig migration", "FinOps approach". Produces a chosen framework + reasoned technology selection.
Repository SourceNeeds Review
data-mesh-domain-topologies
Pick and operate the right Data Mesh domain topology — Fully Federated, Governed, Partially Federated, Hub-and-Spoke, Centralized, Source-Aligned, Consumer-Aligned, Coarse-Grained, or Value Chain-Aligned — and apply domain-driven data product principles (Golden Source, Common Driveway, data ownership rules). Use when scoping Data Mesh adoption, choosing a domain topology that fits the org, designing landing zones, defining what a "data product" means at the company, or reconciling Mesh principles with existing centralized infrastructure. Triggers: "Data Mesh adoption", "domain topology", "data product definition", "fully federated vs governed mesh", "hub-and-spoke for data", "domain landing zones", "data ownership at scale". Produces a chosen topology with rationale and a data product blueprint.
Repository SourceNeeds Review
dataops-and-modern-data-platforms
Apply DataOps practice (SLOs, monitoring, deployment discipline for data), Modern Data Stack composition, Live Data Stack patterns, Data Mesh adoption, Semantic Layer design, Reverse ETL (BLT), Analytics Engineering / Analytics- as-Code (dbt-style), and FinOps for data. Use when establishing operations for a data team, choosing a data platform pattern (MDS vs Live vs Mesh), building a semantic layer, or operationalizing analytics. Triggers: "DataOps practice", "Modern Data Stack composition", "Live Data Stack", "Data Mesh rollout", "semantic layer", "Reverse ETL", "analytics engineering", "dbt workflow", "FinOps for data", "data platform SLOs". Produces a defined ops practice + chosen platform composition with rationale.
Repository SourceNeeds Review
enterprise-data-integration-and-distribution
Pick the right enterprise integration pattern: APIs (REST, SOA, ROA), event-driven (message queues, event brokers, event streams, CEP), CQRS, Backend-for-Frontend, composite services, choreography vs orchestration, point-to-point exceptions, queue-based load leveling. Apply to data distribution: batch vs API vs event-based ingestion. Use when designing cross-domain data flow, choosing sync vs async, deciding between message queue and event stream, or selecting between orchestration and choreography. Triggers: "REST or events", "queue vs stream", "CQRS for data", "BFF pattern", "service orchestration vs choreography", "composite service", "data distribution patterns", "event-driven for data". Produces a chosen integration approach with rationale.
Repository SourceNeeds Review
data-engineering-lifecycle-and-principles
Apply Reis & Housley's Data Engineering Lifecycle (Generation → Storage → Ingestion → Transformation → Serving) plus the six undercurrents (Security, Data Management, DataOps, Data Architecture, Orchestration, Software Engineering) and the nine architecture principles (common components, plan for failure, scalability, leadership, always architecting, loose coupling, reversibility, security, FinOps). Use when scoping a new data platform, diagnosing why a data system is failing, deciding what role / team structure a company needs, or evaluating maturity. Triggers: "build a data platform", "are we doing data engineering right", "what's the data engineering lifecycle", "data team structure", "data maturity", "data engineering principles", "data engineer vs data scientist".
Repository SourceNeeds Review
data-storage-and-modeling-patterns
Apply data storage and modeling patterns: cache hierarchy, consistency paradigms (strong vs eventual), file/object/block storage, warehouse vs lake vs lakehouse, ingestion patterns (batch, streaming, CDC, snapshot vs differential), schema-on-write vs schema-on-read, dimensional modeling (Kimball star schema, Inmon 3NF, Data Vault), Slowly Changing Dimensions (SCD types 1/2/3), and distributed-query patterns (broadcast vs shuffle hash join, MapReduce). Use when designing storage layers, modeling a warehouse, choosing ingestion frequency, or evaluating a transformation approach. Triggers: "warehouse vs lake", "Kimball vs Inmon vs Data Vault", "SCD type 2", "schema on read", "CDC vs scheduled extract", "broadcast join", "data lakehouse", "Iceberg / Delta / Hudi". Produces a chosen storage architecture + data model with rationale.
Repository SourceNeeds Review
waterfall-blueprint
Design provider sequences, throttling logic, and credit policies for enrichment waterfalls across 150+ B2B data sources. Use when building or tuning waterfall sequences, selecting provider stacks per enrichment type, or auditing credit consumption.
Repository SourceNeeds Review