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 skills add EngineerWithAI/engineerwith-agents --skill projection-patternsgit clone --depth 1 https://github.com/EngineerWithAI/engineerwith-agentsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/engineerwithai/engineerwith-agents/projection-patterns)<a href="https://agentmods.dev/skills/engineerwithai/engineerwith-agents/projection-patterns"><img src="https://agentmods.dev/badge/skills/engineerwithai/engineerwith-agents/projection-patterns/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/engineerwithai/engineerwith-agents/projection-patterns"><img src="https://agentmods.dev/badge/skills/engineerwithai/engineerwith-agents/projection-patterns.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00036 | $0.03200 |
| Opus 5.5 | $0.00014 | $0.01280 |
| Sonnet 5.5 | $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 1mo 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.
- 1mo 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 9mo ago), licensed MIT. It adds 36 tokens to every session and 3,200 once invoked, about $0.0001 per session on Opus 5.5. 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.
Other skills, from other repositories
pinecone
Managed vector DB for production RAG and search.
haven-db
Reset, reseed, locate and prune a haven stack's databases with haven db, and know which verbs destroy data. Use when someone says 'haven db', 'reset the database', 'drop the database', 'reseed in place', 'db preset', 'demo preset', 'database url', 'connection string for postgres', 'haven db prune', 'haven seed'…
redis-js
Work with the Upstash Redis JavaScript/TypeScript SDK for serverless Redis operations. Use for caching, session storage, rate limiting, leaderboards, full-text search (querying, filtering, aggregating with @upstash/redis search extension), and all Redis data structures. Supports automatic serialization/deserialization…
using-redis-token-buckets
Use when adding a bucket-like rate limit backed by Redis: a per-caller budget with burst capacity and continuous refill, a refund path for requests that did no work, or a limit whose Retry-After must be a real wait rather than a window edge. posthog/tokenbucket.py provides an atomic Lua token bucket (consume, refund…
byted-milvus
Manages Milvus on Volcano Engine (Volcengine): provision/inspect/scale/delete clusters and run collection + CRUD/search operations via bundled CLIs. Use when the user mentions Milvus + Volcengine/Volcano Engine or asks to operate Milvus there.
bun-redis
Use when working with Redis in Bun (ioredis, Upstash), caching, pub/sub, session storage, or key-value operations.