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Agent Skill · 研究检索

eventmodeling-identifying-outputs(事件建模识别输出)

eventmodeling-identifying-outputs 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

eventmodeling-identifying-outputs 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为只读;许可证信息还不明确,第三方扫描已通过,按安装量在研究检索类的 73,674 个 Skill 里排第 56,028。

总安装 465周安装 19GitHub Stars 公开资料未说明下载 150核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
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unknown

安装方式

通过对话安装

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请帮我安装这个 Agent Skill:eventmodeling-identifying-outputs(事件建模识别输出)
来源仓库:https://github.com/trogonstack/agentskills
仓库路径:skills/eventmodeling-identifying-outputs
安装命令:
npx skills add https://github.com/trogonstack/agentskills --skill eventmodeling-identifying-outputs
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/trogonstack/agentskills --skill eventmodeling-identifying-outputs

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

eventmodeling-identifying-outputs 识别所有由事件产生的查询需求与计算字段。区分原始数据存储与派生值展示,避免重复计算。建立读模型刷新策略,平衡实时性与性能开销。eventmodeling-identifying-outputs 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

适用于高并发查询场景下的性能优化前置工作。输出清单可用于数据库索引设计与缓存机制规划。

来源仓库内路径:skills/eventmodeling-identifying-outputs

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/trogonstack/agentskills --skill eventmodeling-…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它标注为只读,先用只读任务试一次,确认行为符合预期再扩大使用范围。

常见问题

6 条

eventmodeling-identifying-outputs 怎么安装?

在终端执行 npx skills add https://github.com/trogonstack/agentskills --skill eventmodeling-identifyin…。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

eventmodeling-identifying-outputs 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

eventmodeling-identifying-outputs 需要什么权限?

权限级别是只读。该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

eventmodeling-identifying-outputs 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

eventmodeling-identifying-outputs 用的人多吗?

公开数据是累计安装 465 次,近一周安装 19 次,下载 150 次。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

eventmodeling-identifying-outputs 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 第三方扫描已通过,常见的高风险行为没有命中。
  • 标注为只读权限,试用阶段不容易误改本地文件。

先想清楚的地方

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。
  • 只想先试试效果、不希望它改动本地文件的人。

不太适合

  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
eventmodeling-identifying-outputs

事件建模识别输出 · 本页

465—只读未明确通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

Identifying Outputs

Interview Phase (Optional)

When to Interview: Skip if the user has clearly identified: read model queries needed by UI, processor needs, and refresh patterns. Interview when unclear which data queries are critical or how frequently they're accessed.

Interview Strategy: Establish query patterns and identify any calculations before designing read models. The most common architecture error at this step is modeling recalculated state as an event — identifying calculated fields upfront prevents that anti-pattern.

Critical Questions

  1. Query Patterns (Impact: Determines which read models are needed and their update frequency)

- Question: "What data do users/processors need to query? (A) Real-time (sub-second), (B) Near-real-time (seconds), (C) Periodic (minutes/hours)?" - Why it matters: Query frequency drives read model design and caching strategy - Follow-up triggers: If (A) → ask which specific screens or processors require sub-second reads; these need dedicated, highly optimized read models

  1. Event vs Read Model Clarification (Impact: Ensures we don't model calculations as events)

- Question: "Are there calculated/aggregated fields? (e.g., average rating, total sales, inventory count) - These are read models, not events." - Why it matters: Common mistake to model calculations as events; identifying them upfront prevents architecture errors - Follow-up triggers: For each calculated field mentioned → confirm "This recalculates as source data changes, so it belongs in a read model projection — does that match your expectation?"

Interview Flow

Conditional Entry:

If user has provided:
  - UI screens with data needs mapped to event sources
  - AND processor query needs documented
  - AND calculated/aggregated fields identified as read models (not events)

Then: Skip interview, proceed directly to read model design

Else: Conduct interview

Phase 1: Query Pattern Mapping (Question 1)

  • Identify which UI screens and processors need data
  • Establish freshness requirements per consumer
  • Determine if any queries require real-time consistency

Phase 2: Calculation Detection (Question 2)

  • Surface any aggregated or computed values
  • Confirm they are projections, not events
  • Prevent the calculation-as-event anti-pattern before design begins

Capturing Interview Findings

Append findings to the project's event modeling file:

File: .trogonai/interviews/[project-name]/EVENTMODELING.md

Use Write tool to add/update this section:

## 5. Identifying Outputs (eventmodeling-identifying-outputs)

### Query Patterns
[From Q1: Which consumers need what freshness? Real-time vs. periodic?]

### Calculated Fields Identified
[From Q2: Which fields are aggregated/calculated? Confirmed as read models?]

### Read Model Summary
- Real-time read models: [list]
- Near-real-time read models: [list]
- Calculation-as-event anti-patterns caught: [list or "None"]

Update Interview Trail:

| 5 | eventmodeling-identifying-outputs | Done | Read model catalog, query patterns, calculation classification |

CRITICAL: Events vs Read Models

This is the most important distinction in event sourcing. Many architectures fail because this line gets blurred.

Events = Immutable Domain Facts

Things that actually happened in the domain. Once created, they never change:

  • CustomerCreated (a customer actually signed up)
  • OrderPlaced (someone actually placed an order)
  • PaymentAuthorized (payment gateway actually authorized)
  • OrderShipped (fulfillment actually shipped the order)

Characteristics:

  • Represents an action someone took
  • Immutable once recorded
  • Can be replayed to rebuild state
  • Provides audit trail
  • Independent of other events

Read Models = Derived Projections

Optimized views calculated FROM events. They recalculate multiple times:

  • CustomerDashboard (projects current customer data)
  • OrderStatusView (projects order state)
  • InventoryLevelView (projects available stock from receipt/sale events)
  • InventoryLevel (projects available stock)

Characteristics:

  • Calculated/aggregated state
  • Recalculates when source events change
  • Derived from other events
  • Query optimization
  • Can be regenerated from events

The Test: Is It an Event or Read Model?

Ask these questions in order:

QuestionAnswerTypeExample
Did an actor perform an action?YESEVENTCustomer confirmed the order
Is this pure calculation?YESREAD MODELInventory level total
Is it immutable once created?YESEVENTPaymentAuthorized
Does it recalculate multiple times?YESREAD MODELTotal sales (updates as orders change)
Is it independent (causes no other events)?YESEVENTOrderFlagged (flagged for manual review)
Is it derived FROM other events?YESREAD MODELOrderStatus (derived from multiple events)

Common Anti-Patterns

DON'T model these as EVENTS:

  • Inventory level totals (calculation from stock events)
  • Inventory totals (sum of transactions)
  • Account balances (calculation from transactions)
  • Search indexes (derived from documents)
  • Aggregated metrics (sums, counts, averages)
  • Scheduled calculations (processor outputs that are pure calculation)

DO model them as READ MODELS:

WRONG: Modeling as Event

InventoryLevelRecalculated
  productId: product-456
  currentStock: 84          (This recalculates!)
  reservedStock: 12         (Derived, not a fact)

CORRECT: Model as Read Model

InventoryLevelView
  productId: product-456
  totalReceived: 200
  totalSold: 116
  currentStock: 84
  lastUpdated: 2025-01-24T10:30:00Z
  history:
    - 2024-12-01: stock 150 (200 received)
    - 2024-12-15: stock 110 (40 sold)
    - 2025-01-24: stock 84 (26 sold)

WHY:

  • Events should capture facts (what happened)
  • Calculations should be projections (how we view the facts)
  • Otherwise you end up with circular dependencies and replay issues

Workflow

Given commands and events, identify all outputs:

1. Map Event Data to UI Screens

For each screen, identify source events:

Screen: Order Status View
Displays data from events:
  orderId ← OrderCreated event
  customerId ← OrderCreated event
  items ← OrderCreated event
  total ← OrderCreated event
  status ← OrderConfirmed event (or OrderCancelled)
  confirmedAt ← OrderConfirmed event
  paymentId ← PaymentAuthorized event
  shipmentId ← OrderShipped event
  shippedAt ← OrderShipped event

This screen is a projection of these events:
  - OrderCreated
  - OrderConfirmed
  - PaymentAuthorized
  - OrderShipped

2. Define Read Models

Create optimized views from event data:

ReadModel: OrderStatusView
Purpose: UI displays current order status
Events subscribed: OrderCreated, OrderConfirmed, PaymentAuthorized, OrderShipped, OrderCancelled
Data:
{
  orderId: string (from OrderCreated)
  customerId: string (from OrderCreated)
  status: enum (from events: Draft → Confirmed → Authorized → Shipped → Delivered)
  createdAt: Date (from OrderCreated)
  confirmedAt: Date (from OrderConfirmed)
  paymentId: string (from PaymentAuthorized)
  shipmentId: string (from OrderShipped)
  shippedAt: Date (from OrderShipped)
}

3. Document Event → Data Mapping

Show exactly what data each event provides:

Event: OrderCreated
Provides to UI/Processors:
  orderId
  customerId
  items[]
  total
  shippingAddress
  createdAt

Event: OrderConfirmed
Provides to UI/Processors:
  orderId (link to stream)
  paymentMethod (user selected method)
  confirmedAt (timestamp)
  paymentId (payment system reference)

Event: PaymentAuthorized
Provides to UI/Processors:
  orderId (link to stream)
  paymentId
  authCode
  authorizedAt (timestamp)
  amount (verified amount)

Event: OrderShipped
Provides to UI/Processors:
  orderId (link to stream)
  shipmentId
  shippedAt (timestamp)
  carrier (shipping company)
  trackingNumber (for delivery tracking)

4. Create Output Catalog

List all read models:

ReadModel Catalog: Order System

1. OrderStatusReadModel
   Purpose: UI shows current order status
   Events: OrderCreated, OrderConfirmed, PaymentAuthorized, OrderShipped, OrderCancelled
   Data: orderId, status, createdAt, confirmedAt, paymentId, shipmentId
   Consumed by:
     - Order Status screen (UI)
     - Customer Dashboard (UI)
     - Order Processing Processor (decides if can ship)

2. OrderListReadModel
   Purpose: UI lists all orders for a customer
   Events: OrderCreated, OrderConfirmed, OrderCancelled
   Data: orderId, customerId, total, status, createdAt
   Consumed by:
     - Customer Order History (UI)
     - Order Search/Filter (UI)

3. PaymentStatusReadModel
   Purpose: UI shows payment status
   Events: OrderConfirmed, PaymentAuthorized, PaymentFailed
   Data: orderId, paymentId, status, authCode, failureReason, timestamp
   Consumed by:
     - Payment Status screen (UI)
     - Accounting Processor (reconciliation)

4. ShipmentTrackingReadModel
   Purpose: UI shows tracking information
   Events: OrderShipped, DeliveryConfirmed
   Data: orderId, shipmentId, trackingNumber, carrier, shippedAt, estimatedDelivery
   Consumed by:
     - Order Tracking screen (UI)
     - Customer notifications (Processor)

5. Identify Missing Data

Check if all UI needs are covered:

Question: What if UI needs "estimated delivery date"?
Event: OrderShipped has carrier + trackingNumber
Action needed: Add estimatedDelivery to OrderShipped event
  (or compute from carrier info)

Question: What if UI needs to show "payment method" on status?
Event: OrderConfirmed has paymentMethod
Action needed: Include paymentMethod in relevant read models

Question: What if UI needs "item descriptions"?
Event: OrderCreated has items[]
But: items[] only has productId
Action needed: Enrich with product descriptions from catalog
  (via join with product service)

6. Processor Outputs

Identify what processors consume:

Processor: Inventory System
Consumes from read models:
  - Orders in "PaymentAuthorized" status
  - Items and quantities needed
Produces commands:
  - ReserveInventory

Processor: Fulfillment System
Consumes from read models:
  - Orders in "InventoryReserved" status
  - Items and quantities
  - Shipping address
Produces commands:
  - CreateShipment

Processor: Notification System
Consumes from read models:
  - OrderCreated (sends confirmation)
  - OrderConfirmed (sends receipt)
  - OrderShipped (sends tracking)
  - DeliveryConfirmed (sends thank you)
Does not produce commands (info-only)

Output Format

Present as:

# Outputs: [Domain Name]

## Read Models Summary

| ReadModel | Purpose | Events | Consumed By |
|-----------|---------|--------|-------------|
| OrderStatus | Show order state | OrderCreated, OrderConfirmed | UI, Processor |
| OrderList | List orders | OrderCreated, OrderCancelled | UI |
| PaymentStatus | Payment info | OrderConfirmed, PaymentAuthorized | UI, Accounting |
| Shipment Tracking | Track delivery | OrderShipped, DeliveryConfirmed | UI, Notifications |

---

## Detailed Read Models

### ReadModel: OrderStatusView

**Purpose**: Order Status screen displays current order state

**Events subscribed**:
- OrderCreated
- OrderConfirmed
- PaymentAuthorized
- OrderShipped
- OrderCancelled
- DeliveryConfirmed

**Data**:

{orderId: string customerId: string status: 'Draft' | 'Confirmed' | 'Authorized' | 'Shipped' | 'Delivered' | 'Cancelled' items: Array<{productId, quantity, unitPrice}> total: number shippingAddress: Address

createdAt: Date confirmedAt: Date paymentId: string paymentMethod: 'card' | 'transfer' authorizedAt: Date

shipmentId: string carrier: string trackingNumber: string shippedAt: Date estimatedDelivery: Date}

**Update Logic**:
- OrderCreated: Insert with status='Draft'
- OrderConfirmed: Update status='Confirmed'
- PaymentAuthorized: Update status='Authorized', set paymentId
- OrderShipped: Update status='Shipped', set shipmentId, carrier, trackingNumber
- DeliveryConfirmed: Update status='Delivered'
- OrderCancelled: Update status='Cancelled'

**Consumed By**:
- Order Status Screen (displays)
- Order Processing Processor (checks status)
- Notification System (sends updates)

--- [Repeat for each read model]

---

## Data Completeness Check

### Events → UI Needs

Verify all UI needs have event sources:

| UI Need | Event Source | Status |
|---------|-------------|--------|
| Order status | OrderConfirmed, OrderShipped |  |
| Tracking number | OrderShipped |  |
| Order items | OrderCreated |  |
| Estimated delivery | OrderShipped |  |
| Cancellation reason | OrderCancelled |  |

### Missing Data

Identify UI needs without event sources:
- None identified

---

## Processor Consumption

### Processors and their reads:

| Processor | Reads From | Writes Commands |
|-----------|-----------|-----------------|
| Inventory | OrderStatusView (Authorized) | ReserveInventory |
| Fulfillment | OrderStatusView (InventoryReserved) | CreateShipment |
| Notification | OrderStatusView (all) | None (info-only) |
| Accounting | PaymentStatusView | None (reporting) |

Quality Checklist

Read Model Design

  • Every UI screen maps to read model(s)
  • Every read model has clear purpose
  • Every data field has event source
  • Update logic for each event is explicit
  • All UI needs are covered
  • Processor reads are identified
  • Read model access patterns clear
  • No undocumented data sources
  • Compensation/cancellation handled
  • Error states shown

CRITICAL: Event vs Read Model Validation

  • Reviewed each read model: "Is this pure calculation or an actual domain fact?"
  • No aggregations modeled as events: (totals, averages, counts are read models)
  • No recalculated state modeled as events: (if value changes multiple times, it's a read model)
  • Processor outputs are categorized:

- Produces NEW EVENT = actual domain fact (e.g., PaymentAuthorized) - Updates READ MODEL = calculation (e.g., SellerRatingCalculated) - Sends NOTIFICATION = info-only (no event or model)

  • History tracking is clear: Derived state keeps history in read model history[], not as separate events

Key Principles

  1. Event-Driven: All data comes from events
  2. Projection-Based: Read models are projections, not persistent
  3. UI-Focused: Optimized for UI display needs
  4. Processor-Friendly: Enough data for processor decisions
  5. Completeness: All needed data available

Common Patterns

Status View Pattern

Events: Create, Confirm, Process, Ship
ReadModel: Accumulates data from all events
Displayed: Current state reflecting all events

List View Pattern

Events: Create, Update, Delete (Cancel)
ReadModel: Summary of each item
Used for: Filtering, sorting, searching

Timeline View Pattern

Events: Any event with timestamp
ReadModel: Chronological list
Used for: History, audit trail

Processor Decision Pattern

Events: State-changing events
ReadModel: Current state only
Processor reads to decide next action

平台分布

Codex

36.13%
按下载量换算54

Claude

27.76%
按下载量换算42

Cursor

17.16%
按下载量换算26

Gemini CLI

10%
按下载量换算15

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

安装前确认

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