projection-patterns

Overview of projection-patterns skill

What projection-patterns is for

The projection-patterns skill helps you design and implement read models that are built from event streams. It is aimed at teams using event sourcing or CQRS who need dependable ways to turn append-only events into query-friendly tables, views, caches, dashboards, or search indexes.

Best fit for Backend Development

This projection-patterns skill is best for backend engineers, architects, and AI-assisted coding workflows working on:

• CQRS read sides
• materialized views
• denormalized query models
• real-time dashboards
• search or reporting indexes
• rebuildable read databases from historical events

If your system is event-driven but you are still hand-waving the read side, projection-patterns for Backend Development gives you a more concrete implementation path than a generic prompt.

The real job to be done

Most users do not need theory; they need a projector design that answers practical questions fast:

• which projection type fits this consistency need
• how to process historical events safely
• how to checkpoint progress
• how to replay without corrupting state
• how to shape read models around query patterns instead of write models

That is where projection-patterns is most useful.

What makes this skill different

The strongest differentiator is that it centers projection architecture and projection types, not event sourcing in the abstract. The source material explicitly covers:

• the event store → projector → read model flow
• live, catchup, persistent, and inline projections
• templates for building projectors

That makes it more actionable than a plain “build me a CQRS read model” request, especially when you need to choose between low latency, replayability, and operational simplicity.

When this skill is not the right choice

Skip projection-patterns if:

• your data is not event-based
• you only need a normal CRUD read path
• you need deep vendor-specific setup for Kafka, EventStoreDB, PostgreSQL, or DynamoDB
• you want production-ready framework code without adapting templates

This skill is concept-first and implementation-oriented, but not tied to one stack.

How to Use projection-patterns skill

projection-patterns install context

The repository does not expose a dedicated installer inside SKILL.md, so the practical projection-patterns install path is to add the parent skills repository and invoke the skill by name in your agent environment.

A common pattern is:

npx skills add https://github.com/wshobson/agents --skill projection-patterns

If your toolchain loads skills from a local clone instead, point it at:

plugins/backend-development/skills/projection-patterns

Read this file first

Start with:

• plugins/backend-development/skills/projection-patterns/SKILL.md

This skill is self-contained. There are no extra rules/, resources/, or helper scripts surfaced in the repository signals, so most of the value is in understanding the patterns and templates in that one file.

What input the skill needs from you

projection-patterns usage gets much better when you provide concrete read-side requirements, not just “build a projection.” At minimum, include:

• event types and sample payloads
• target read model shape
• query patterns to optimize for
• expected ordering and idempotency guarantees
• replay volume and rebuild expectations
• consistency needs: real-time, eventual, or inline
• failure and restart expectations
• storage target for the read model

Without those inputs, the skill can still generate a pattern, but it will likely guess wrong on projection type and state management.

Turn a rough goal into a strong prompt

Weak prompt:

Create a projection for orders.

Stronger prompt:

Use the projection-patterns skill to design an order summary projection from OrderPlaced, OrderItemAdded, OrderPaid, and OrderShipped events. Target PostgreSQL. Queries need order status by customer, recent orders, and revenue by day. We need replay support for 50M historical events, checkpointing, idempotent handlers, and eventual consistency within 5 seconds.

Why this works:

• it names the event stream
• it defines the read model consumers
• it sets scale and rebuild constraints
• it clarifies consistency and durability requirements

Choose the right projection type early

One of the best uses of projection-patterns guide content is deciding between projection styles before code generation:

• Live: use when subscription-driven freshness matters most
• Catchup: use when rebuilding from historical events is a first-class need
• Persistent: use when restart safety and checkpoint resume matter
• Inline: use when strong consistency outweighs write-path simplicity

Many bad implementations happen because teams pick inline for convenience or live for freshness without planning replay and recovery.

Suggested usage workflow

A practical workflow for the projection-patterns skill:

1. Define the consumer queries first.
2. List all source events and event version assumptions.
3. Ask the skill to recommend a projection type with tradeoffs.
4. Generate handler logic per event type.
5. Add checkpointing and idempotency strategy.
6. Define rebuild and backfill procedure.
7. Review failure cases: duplicates, out-of-order events, poison events.
8. Only then ask for framework-specific code.

This order improves design quality because the skill is strongest at the architectural layer first.

What to ask the skill to produce

For high-value output, ask for one or more of these deliverables:

• projection design doc
• event-to-read-model mapping table
• handler pseudocode
• checkpoint schema
• replay strategy
• idempotency rules
• failure recovery plan
• test cases for rebuild and duplicate handling

These outputs are more decision-useful than jumping straight to a full code dump.

Repository-reading path for faster adoption

Since the repo signals show only SKILL.md, use this reading path:

1. read “When to Use This Skill”
2. read “Core Concepts”
3. inspect the projection architecture diagram
4. compare the projection types table
5. review the templates only after you know which type fits your system

This avoids copying a template that mismatches your consistency model.

Practical tips that change output quality

Ask the skill to be explicit about:

• how checkpoints are stored
• whether handlers are idempotent
• how to handle event schema evolution
• what happens during replay versus live processing
• whether ordering is guaranteed per stream or globally

These details determine whether the resulting projection survives real operations.

Common implementation constraints to surface up front

Before you rely on projection-patterns usage, tell the skill about:

• single stream vs multi-stream aggregation
• required lag tolerance
• acceptable rebuild time
• whether read models can be dropped and recreated
• whether writes and reads share a database
• whether exactly-once delivery is unavailable

The skill becomes much more useful when constrained by real operating conditions.

projection-patterns skill FAQ

Is projection-patterns only for full event sourcing systems

No. It is most natural in event-sourced systems, but it also fits event-driven architectures where domain or integration events are already available and you need query-optimized read models.

Is projection-patterns beginner friendly

Moderately. The core idea is simple, but you will get the most value if you already understand events, handlers, and eventual consistency. Beginners can still use the skill well if they provide sample events and ask for a step-by-step design.

How is this different from a normal AI coding prompt

A generic prompt often jumps straight to code. projection-patterns is more useful when you need the design choices behind the code: projection type, replay strategy, checkpointing, and read-model shape. That reduces the chance of generating a read side that looks plausible but fails during rebuilds or restarts.

Can projection-patterns generate production-ready code

It can help produce solid scaffolding and patterns, but you should not expect one-shot production readiness. You still need to adapt for your event bus, database, concurrency model, and deployment environment.

When should I not use projection-patterns

Do not use projection-patterns when:

• you just need transactional CRUD reads
• your source data is mutable state, not events
• your main problem is broker configuration or infra provisioning
• you need highly vendor-specific operational docs rather than projection design

Does it help with replay and rebuild planning

Yes. That is one of the biggest reasons to use the skill instead of a plain coding request. The projection type distinctions directly affect rebuild behavior, catchup processing, and restart resilience.

How to Improve projection-patterns skill

Give better event examples

The fastest way to improve projection-patterns results is to provide 3 to 6 real event samples with fields, not just event names. Field-level detail helps the skill:

• map state transitions correctly
• spot missing denormalized fields
• avoid inventing data that is not in the stream

Specify the read model from query needs

Do not ask for “a projection table.” Ask for the exact queries you need to support, such as:

• list customer orders by status
• show current inventory by SKU
• aggregate revenue per day
• search invoices by supplier and due date

This keeps the skill focused on read optimization rather than mirroring the write model.

Force tradeoff discussion before code

Ask the projection-patterns skill to compare at least two projection types for your use case before implementation. This surfaces hidden tradeoffs around:

• consistency
• replay cost
• operational recovery
• write-path coupling

That comparison is often more valuable than the first code sample.

Prevent common failure modes

Common weak outputs usually come from missing constraints. Explicitly ask the skill to address:

• duplicate event delivery
• out-of-order processing
• partial projector failure
• checkpoint corruption
• schema evolution
• replay versus live divergence

If these are not discussed, the design is probably too shallow for production.

Iterate after the first draft

After the first answer, improve the result by asking:

• Rewrite this projection for idempotency.
• Add a checkpointing model and recovery flow.
• Show how replay differs from live subscription processing.
• Identify where this design breaks under high event volume.
• Refactor the read model around these three query patterns.

This works better than requesting a larger first response.

Ask for tests, not just handlers

To improve projection-patterns for Backend Development, request test scenarios such as:

• replay from zero
• duplicate event ingestion
• handler restart from checkpoint
• event version upgrade
• missing or malformed event payloads

A projection that cannot be tested under replay and failure is rarely ready to trust.

Use the skill as a design reviewer

A strong pattern is to bring your own draft projector and ask projection-patterns to critique it. For example:

• identify non-idempotent handlers
• find missing checkpoints
• detect read-model fields that cannot be derived reliably
• point out where inline projections may hurt write throughput

This review mode often yields higher information gain than greenfield generation.