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fp-kstream-designfp kstream 设计

Agent Skill

用于辅助界面设计、视觉规范、排版、配色、布局和交互体验优化。它适合让 Agent 根据产品场景整理页面结构、生成 UI 方案、检查视觉一致性或改进组件层级。使用时需要结合现有品牌、设计系统和用户任务,不应只堆装饰元素;涉及真实页面改动时,应通过截图或浏览器预览检查文本溢出、对齐和响应式表现。

总安装

349

周安装

14

GitHub Stars

公开资料未说明

下载量

113
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:fp-kstream-design(fp kstream 设计)
来源仓库:https://github.com/mpurbo/purbo-skills
仓库路径:skills/fp-kstream-design
安装命令:
npx skills add https://github.com/mpurbo/purbo-skills --skill fp-kstream-design
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/mpurbo/purbo-skills --skill fp-kstream-design

简介

用于辅助界面设计、视觉规范和交互体验优化,支持 UI 方案生成与一致性检查。

  • 适合让 Agent 整理页面结构、调整布局层级或改进组件可用性。
  • 使用时需结合品牌指南、设计系统和真实用户任务场景进行定制。
  • 涉及实际页面改动时应通过截图或浏览器预览验证文本溢出与响应式表现。
  • fp-kstream-design 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

Kafka Topology Design Skill

Design deterministic, replay-safe, cost-efficient Kafka Streams topologies using KSA patterns.

Required Reading

Before responding, load the shared reference:

cat ${SKILL_PATH}/references/KSA.md

This is the authoritative source for all patterns, principles, and constraints.


Workflow

Step 1 — Understand the Problem

Gather from the user (ask if missing):

  1. Source events — what topics trigger this service?
  2. Enrichment needs — what data beyond the event itself?
  3. Outputs — output topics, DB writes, notifications?
  4. Statefulness — does output depend on past events?
  5. Partition key — what entity scopes this? (userId, orderId, etc.)

Step 2 — Select Recipes

Map to KSA recipes (KSA.md §4):

Problem involves…Recipe
Cleaning/validating inbound events01 — Validation & Normalization
Duplicate events from upstream02 — Deduplication
Splitting events to different consumers03 — Routing & Fan-Out
Looking up reference data04 — Data Enrichment
Reference data + historical computation05 — Enrichment + Stateful
Counting, rate limiting, windowed metrics06 — Windowed Aggregation
Entity lifecycle (order, payment, KYC)07 — Per-Key State Machine
Cross-service coordination with rollback08 — Saga Orchestrator
Building a read model or search index09 — CQRS Projection
Bug fix replay or data backfill10 — Event Replay

Step 3 — Compose the Topology

Arrange recipes left to right:

Source → [Ingress] → [Enrichment] → [Computation] → [Egress] → Sink

Not every stage needed. Only include what the problem requires.

Step 4 — Draw the Diagram

Produce a Mermaid flowchart LR using the KSA symbol legend (KSA.md §3):

  • [TopicName] — Kafka topic
  • [TopicName*] — compacted topic (KTable source)
  • (Processor) — stateless processor
  • {{Processor}} — stateful processor
  • ((Join)) — stream–table join
  • [[Sink]] — side-effect boundary
  • {Decision?} — conditional branch

Step 5 — Declare Policies

For every topology, explicitly document:

  1. Missing-state policy per join (drop / dead-letter / retry / buffer)
  2. Partition key and why it aligns with all joins
  3. State store retention per stateful processor
  4. Sink idempotency strategy

Step 6 — Cost Check

Estimate per KSA.md §7.4:

FactorEstimateRed Flag
State store size/keyvalue × keys × retention> 50 GB/instance
Changelog overheadstore size × replication> 100 GB total
Repartition countselectKey/through calls> 2 on high-volume
KTable restore timetopic size / throughput> 10 minutes
Partition countall internal + output topics> 500 total

Multiple red flags → recommend alternatives (KSA.md §7.3).

Step 7 — Compliance Checklist

Verify against KSA.md §6 before signing off.


Output Format

Always produce:

  1. Summary — one paragraph describing what the service does
  2. Recipes used — numbered list of KSA recipe numbers and names
  3. Topology diagram — Mermaid flowchart LR
  4. State diagram — Mermaid stateDiagram-v2 (if FSM involved)
  5. Policy table — missing-state, retention, idempotency decisions
  6. Cost estimate — back-of-napkin numbers for the heuristic
  7. Compliance — checklist pass/fail

Anti-Patterns to Flag

Anti-PatternWhy It's WrongSuggest
HTTP calls inside processorBreaks replay determinismKTable enrichment
DB queries inside processorSame as aboveCompacted topic
No declared missing-state policyUndeclared behavior = design defectAsk: "what happens when KTable has no entry?"
Partition key mismatchJoin key ≠ partition keyRepartition (flag cost)
Unbounded state storesNo TTL = unbounded growthAsk about retention
GlobalKTable for large dataLoads ALL data on EVERY instanceRegular KTable with partition-aligned joins
Multiple repartitions on same streamEach doubles I/ORedesign key strategy
Stateful where stateless sufficesUnnecessary state store overheadRemove state store

Conversation Style

  • Ask clarifying questions before designing. Problem statements are often incomplete.
  • When multiple recipes apply, explain trade-offs and let the engineer choose.
  • Always produce a diagram.
  • Be explicit about what the topology does NOT handle (scope).
  • If the problem is better solved without KStreams, say so (KSA.md §7.3).

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

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能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

36.13%
按下载量换算41

Claude

28.85%
按下载量换算33

Cursor

17.47%
按下载量换算20

Gemini CLI

8.31%
按下载量换算9

安全审计

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通过

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通过

Snyk

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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