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.
npx agentmods add skills/engineerwithai/engineerwith-agents/projection-patternsnpx skills add EngineerWithAI/engineerwith-agents --skill projection-patternsgit clone --depth 1 https://github.com/EngineerWithAI/engineerwith-agentsWhat 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.
| Model | Per session | Once 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 |
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.
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.
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)
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.
- 2d ago First seen · 489 lines · 36 tokens per session scan A 125b567671d5
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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