projection-patterns

A guide to turning event streams—records of things that happened—into read models, such as database tables, views, or caches. It covers projections used in event-sourced systems, where current data is rebuilt from those records.

In plain words
What is it for?
Use it to build CQRS read sides, materialized views, real-time dashboards, search indexes, and summaries across multiple event streams.
Why use it?
It helps keep query data organized and fast without changing the original event history. It also explains how to rebuild or resume read models after interruptions.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/engineerwithai/engineerwith-agents/projection-patterns
Any agent
npx skills add EngineerWithAI/engineerwith-agents --skill projection-patterns
Clone the repo
git clone --depth 1 https://github.com/EngineerWithAI/engineerwith-agents

Made for: Claude Code, Codex.

Per session 36 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,200 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin 100% copy Near-identical to another mod in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00036 $0.03200
Opus 5 $0.00018 $0.01600
Sonnet 5 $0.00007 $0.00640
Haiku 4.5 $0.00004 $0.00320

Measured 2d ago against content hash 125b567671d5, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

projection-patterns scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

Origin

This is a copy

100% identical to projection-patterns — 14 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

plugins/backend-development/skills/projection-patterns/SKILL.md · 489 lines

How it starts

The opening of the file, as written. The whole thing — 489 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Projection Patterns

Comprehensive guide to building projections and read models for event-sourced systems.

When to Use This Skill

  • Building CQRS read models
  • Creating materialized views from events
  • Optimizing query performance
  • Implementing real-time dashboards
  • Building search indexes from events
  • Aggregating data across streams

Core Concepts

1. Projection Architecture

┌─────────────┐     ┌─────────────┐     ┌─────────────┐
│ Event Store │────►│ Projector   │────►│ Read Model  │
│             │     │             │     │ (Database)  │
│ ┌─────────┐ │     │ ┌─────────┐ │     │ ┌─────────┐ │
│ │ Events  │ │     │ │ Handler │ │     │ │ Tables  │ │
│ └─────────┘ │     │ │ Logic   │ │     │ │ Views   │ │
│             │     │ └─────────┘ │     │ │ Cache   │ │
└─────────────┘     └─────────────┘     └─────────────┘

2. Projection Types

Type Description Use Case
Live Real-time from subscription Current state queries
Catchup Process historical events Rebuilding read models
Persistent Stores checkpoint Resume after restart
Inline Same transaction as write Strong consistency

Templates

Template 1: Basic Projector

from abc import ABC, abstractmethod
from dataclasses import dataclass
from typing import Dict, Any, Callable, List
import asyncpg

@dataclass
class Event:
    stream_id: str
    event_type: str
    data: dict
    version: int
    global_position: int


class Projection(ABC):
    """Base class for projections."""

    @property
    @abstractmethod
    def name(self) -> str:
        """Unique projection name for checkpointing."""
        pass

    @abstractmethod
    def handles(self) -> List[str]:
        """List of event types this projection handles."""
        pass

    @abstractmethod
    async def apply(self, event: Event) -> None:
        """Apply event to the read model."""
        pass


class Projector:
    """Runs projections from event store."""

    def __init__(self, event_store, checkpoint_store):
        self.event_store = event_store
        self.checkpoint_store = checkpoint_store
        self.projections: List[Projection] = []

    def register(self, projection: Projection):
        self.projections.append(projection)

    async def run(self, batch_size: int = 100):
        """Run all projections continuously."""
        while True:
            for projection in self.projections:
                await self._run_projection(projection, batch_size)
            await asyncio.sleep(0.1)

    async def _run_projection(self, projection: Projection, batch_size: int):
        checkpoint = await self.checkpoint_store.get(projection.name)
        position = checkpoint or 0

        events = await self.event_store.read_all(position, batch_size)

        for event in events:
            if event.event_type in projection.handles():
                await projection.apply(event)

            await self.checkpoint_store.save(
                projection.name,
                event.global_position
            )

    async def rebuild(self, projection: Projection):
        """Rebuild a projection from scratch."""
        await self.checkpoint_store.delete(projection.name)
        # Optionally clear read model tables
        await self._run_projection(projection, batch_size=1000)

Read the full file on GitHub · 489 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2d ago First seen · 489 lines · 36 tokens per session scan A 125b567671d5

Subscribe to this mod's changes

projection-patterns is a skill published in the GitHub repository EngineerWithAI/engineerwith-agents (4 stars, last pushed 7mo ago), licensed MIT. It adds 36 tokens to every session and 3,200 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to projection-patterns, differing in 14 lines, and is treated as a copy.

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