Project event streams into queryable read models with Python templates.
npx skills add https://github.com/mokbhai/claude --skill projection-patterns-mokbhai
Please help me install this Agent Skill. Skill: projection-patterns Source: https://github.com/mokbhai/claude/tree/main/skills/projection-patterns Command: npx skills add https://github.com/mokbhai/claude --skill projection-patterns-mokbhai
This skill helps teams convert streams of events into readable, queryable read models by applying projection patterns across event-sourced systems. It provides architecture guidance, patterns for Live, Catchup, Persistent, and Inline projections, and ready-to-use template code to implement projections against common stores.
Create a projection by defining a Projection subclass, register it with a Projector, and run the projection to materialize a read model from your event stream.
High-intent search queries and answers about installing and using this skill.
To build queryable read models from event streams, you project events into materialized views using a Projection subclass. Register it with a Projector to apply transactional upserts and generate fast, readable views for CQRS architectures.
Rebuilding a read model from historical event-sourced data uses Catchup projections. This pattern replays past event streams to reconstruct materialized views, allowing you to restore or migrate read model state without disrupting live queries.
Yes, you can use Python to create projections for PostgreSQL and Elasticsearch. The skill provides ready-to-use Python template code and optional integrations for both platforms to apply transactional upserts and materialize queryable views.
Checkpointing for event-sourced projections works by persistently storing processing progress. This allows the projector to resume materializing read models from the last saved point after a failure, ensuring deterministic recovery without reprocessing the entire stream.
You need live projections in CQRS for real-time dashboard updates as new events arrive, while inline projections apply state changes synchronously within the write operation. Choose based on whether immediate query availability or write-time consistency is required.
mind-protocolIngest raw data sources and route them to specified modules using a deterministic mapping table.
mokbhaiProject event streams into queryable read models with Python templates.
AI-Foundry-CoreApply CQRS to separate read and write models with commands, queries, and projections.
SaiyedMuhammadAnasMaududiProject event streams into read models for CQRS read sides and analytics.
