designing-data-intensive-applications

Plugin designing-data-intensive-applications from marketplace bookforge-skills in bookforge-ai/bookforge-skills. Commands read from the repo's manifest — 1 month ago.

Skills (14)

• batch-pipeline-designer

  Design batch data processing pipelines for large-scale, bounded datasets processed offline. Use when building ETL workflows, processing logs or clickstream data at scale, generating ML feature pipelines or search indexes, or joining two large datasets that cannot fit in memory. Trigger phrases: "des

• concurrency-anomaly-detector

  Scan application code, SQL queries, or ORM code for exposure to the 6 database concurrency anomalies and produce a findings report with severity, affected locations, and fix recommendations. Use when: debugging a nondeterministic data corruption or race condition bug under concurrent load; auditing

• consistency-model-selector

  Choose the correct consistency model (linearizability, causal consistency, or eventual consistency) for each operation in a distributed system, and select the matching implementation mechanism. Use when designing a new distributed data system, deciding whether ZooKeeper or etcd is needed for coordin

• data-integration-architect

  Design the integration architecture for systems with multiple specialized data stores (Postgres, Elasticsearch, Redis, data warehouses) that must stay in sync. Use when deciding how data flows between components, avoiding dual writes, reasoning about correctness across system boundaries (idempotency

• data-model-selector

  Choose between relational, document, and graph data models for an application by analyzing data shape, relationship complexity, and query patterns. Use when asked "should I use MongoDB or PostgreSQL?", "when does a graph database make sense?", "how do I choose between SQL and NoSQL?", or "what data

• distributed-failure-analyzer

  Diagnose distributed system failures caused by network faults, unreliable clocks, or process pauses — and map each to its correct mitigation. Use when: a node is intermittently timing out with no clear network outage; a lock-holder or leader keeps acting after being declared dead (zombie leader / sp

• encoding-format-advisor

  Select a data encoding format (JSON, Protobuf, Thrift, or Avro) and design a schema evolution strategy that preserves backward and forward compatibility through rolling upgrades. Use when asked "should I use Protobuf or JSON?", "how do I evolve my schema without breaking old clients?", "how does Avr

• oltp-olap-workload-classifier

  Classify a data workload as OLTP, OLAP, or hybrid, then recommend the appropriate database architecture — transactional database, dedicated data warehouse, or both with an ETL pipeline. Use when asked "should I use a data warehouse?", "why are my analytics queries slow on my production database?", "

• partitioning-strategy-advisor

  Select the right partitioning (sharding) strategy — range, hash, or compound key — and configure secondary indexes, rebalancing, and request routing for a distributed database. Use when: designing a partition key for a new system; diagnosing write hotspots on monotonically increasing keys (timestamp

• replication-failure-analyzer

  Diagnose active replication failures by mapping symptoms to leader failover pitfalls, replication lag anomalies, or quorum edge cases — and produce a structured remediation plan. Use when: data just written disappears or shows stale on re-read (read-after-write violation); records appear and vanish

• replication-strategy-selector

  Choose a replication topology (single-leader, multi-leader, or leaderless) and configure it correctly — including sync vs. async mode, quorum parameters (w + r > n), and consistency guarantees. Use when designing replication for a new system, configuring quorum values for Cassandra/Riak/DynamoDB, de

• storage-engine-selector

  Select the right storage engine architecture (LSM-tree, B-tree, or in-memory) for a database workload using a 7-dimensional scored trade-off analysis. Use when evaluating RocksDB vs InnoDB vs LevelDB, diagnosing write amplification in production, choosing between write-optimized vs read-optimized st

• stream-processing-designer

  Design a stream processing system for unbounded, continuously arriving data. Use when choosing a message broker (Kafka vs RabbitMQ), implementing change data capture (CDC) from PostgreSQL, MySQL, or MongoDB via Debezium or Maxwell, selecting window types for aggregation (tumbling, hopping, sliding,

• transaction-isolation-selector

  Choose the correct transaction isolation level and serializability implementation for an application's concurrency patterns. Use when: selecting an isolation level for a new system; evaluating whether read committed or snapshot isolation is safe for your access patterns; deciding whether to upgrade

Hooks (1)

• SessionStart

  ${CLAUDE_PLUGIN_ROOT}/hooks/session-start

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