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system-architecture系统架构

Agent Skill

system-architecture 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。

总安装

188

周安装

8

GitHub Stars

1

下载量

66
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:system-architecture(系统架构)
来源仓库:https://github.com/hungv47/prod-skills
仓库路径:skills/system-architecture
安装命令:
npx skills add https://github.com/hungv47/prod-skills --skill system-architecture
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/hungv47/prod-skills --skill system-architecture

简介

用于查找、检索和筛选相关信息,支持基于关键词定位内容。

  • 适用于需要快速筛选候选结果的场景。适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。
  • 可通过 GitHub 仓库路径和技能名称进行安装。
  • 使用前需确认权限范围及是否触发网络或文件操作。
  • system-architecture 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

System Architecture Designer — Orchestrator

*Productivity — Multi-agent orchestration. Transforms product specifications into a comprehensive technical blueprint covering stack, schema, APIs, and deployment.*

Core Question: "Will this still work at 10x scale with 10x team?"

Inputs Required

  • Product specification, PRD, or description of what needs to be built
  • Scale expectations (users, requests, data volume) — gathered via interview if missing
  • Known constraints (existing stack, compliance, budget, team skills)

Output

  • .agents/system-architecture.md

Chain Position

Previous: plan-interviewer or task-breakdown (optional) | Next: task-breakdown (optional) | Cross-stack: reads solution-design.md (from research-skills), user-flow.md (from product-skills)

Re-run triggers: When product spec changes significantly, when scale requirements change (10x growth), when migrating core infrastructure, or when adding major new integrations.


Multi-Agent Architecture

Agent Manifest

AgentFileFocus
stack-selection-agentagents/stack-selection-agent.mdTechnology choices with rationale and alternatives
infrastructure-agentagents/infrastructure-agent.mdDeployment, CI/CD, monitoring, env vars
schema-agentagents/schema-agent.mdDatabase tables, relationships, indexes, queries
api-agentagents/api-agent.mdEndpoints, auth, request/response contracts
integration-agentagents/integration-agent.mdFile structure, service connections, feature blueprints
scaling-agentagents/scaling-agent.mdBottleneck analysis, failure modes, edge cases
critic-agentagents/critic-agent.mdQuality gate review, internal consistency

Execution Layers

Layer 1 (parallel):
  stack-selection-agent ──┐
  infrastructure-agent ───┘─── run simultaneously

Layer 2 (sequential):
  schema-agent ─────────────── depends on stack choice
    → api-agent ────────────── depends on stack + schema
      → integration-agent ──── depends on stack + schema + API
        → scaling-agent ────── validates everything above
          → critic-agent ───── final quality review

Dispatch Protocol

  1. Gather context — extract user types, data entities, critical flows, scale profile, and constraints from the product spec. If missing, run the Architecture Interview (see below).
  2. Layer 1 dispatch — send brief + constraints to stack-selection-agent and infrastructure-agent in parallel.
  3. Layer 2 sequential chain — pass stack output to schema-agent, then stack + schema to api-agent, then all three to integration-agent, then everything to scaling-agent.
  4. Critic review — send assembled document to critic-agent.
  5. Revision loop — if critic returns FAIL, re-dispatch affected agents with feedback. Maximum 2 revision rounds.
  6. Assembly — merge all agent outputs into the 12-section artifact template. Save to .agents/system-architecture.md.

Routing Logic

ConditionRoute
User provides tech stack upfrontSkip stack-selection-agent; pass user's stack directly to schema-agent
User needs stack recommendationsRun stack-selection-agent first
Critic returns PASSAssemble and deliver
Critic returns FAILRe-dispatch only the agents cited in critic's issues
Revision round > 2Deliver with critic's remaining issues noted as Open Questions

Critical Gates

Before delivering, the critic-agent verifies ALL of these pass:

  • Every tech choice has a rationale (not just "it's popular")
  • API endpoints exist for every user-facing feature
  • Database schema covers all entities mentioned in product spec
  • Deployment section includes complete env var list
  • File structure matches chosen framework conventions
  • Auth model covers all user roles and permission levels
  • At least one architectural trade-off is documented with alternatives considered

If any gate fails: the critic identifies which agent must fix it and the orchestrator re-dispatches with specific feedback.


Single-Agent Fallback

When context window is constrained or the product is simple (fewer than 3 user types, fewer than 5 data entities):

  1. Skip multi-agent dispatch
  2. Execute Steps 1-4 from the original process sequentially:

- Step 1: Gather context and constraints - Step 2: Architecture decisions (use references/tech-stack-patterns.md and references/tech-stack-matrix.md) - Step 3: Generate all 12 sections of the architecture document - Step 4: Validation cross-reference

  1. Run the Critical Gates checklist as self-review
  2. Save to .agents/system-architecture.md

Before Starting

Step 0: Product Context

Check for .agents/product-context.md. If missing: interview for product dimensions (what, who, problem, differentiator, scale, integrations) and save to .agents/product-context.md. Or recommend running icp-research (from hungv47/marketing-skills) to bootstrap it.

If .agents/product-context.md has a date field older than 30 days, recommend re-running icp-research (from marketing-skills) to refresh it.

Required Artifacts

None — this skill can run standalone.

Optional Artifacts

ArtifactSourceBenefit
product-context.mdicp-research (from hungv47/marketing-skills)Industry context, user personas, and constraints
task-breakdown.mdtask-breakdownFeature list already decomposed into buildable units
solution-design.mdsolution-design (from hungv47/research-skills)Business initiatives and constraints from strategy track
.agents/design/user-flow.mduser-flow (from hungv47/product-skills)User flow diagrams for API endpoint design and feature scoping

Two Modes of Operation

Mode 1: Tech Stack Already Chosen User provides tech stack upfront. Skip stack-selection-agent. Focus on schema, API, file structure, and implementation details.

Mode 2: Need Tech Stack Recommendations User needs help choosing stack. Run stack-selection-agent first, then chain remaining agents.

Architecture Interview

If the user provides only a vague description ("build me an app", "I need a platform"):

  1. What is the product and its core value proposition?
  2. Who are the primary users? Expected concurrent users at launch and at 12 months?
  3. What are the 3-5 critical user flows?
  4. What data needs to be stored and queried?
  5. Existing tech stack or team skill constraints?
  6. Specific integrations needed? (payments, email, auth providers, etc.)
  7. Performance requirements? (real-time updates, complex queries, offline support)
  8. Security/compliance needs? (SOC2, HIPAA, GDPR, PCI)

All 8 answers are necessary before dispatching agents.


Anti-Patterns

Anti-PatternProblemINSTEAD
Premature microservicesAdds operational complexity before product-market fitStart monolith, extract services at pain points
Schema without queriesTables look clean but critical queries require full scansDesign schema around access patterns via schema-agent
Auth as afterthoughtRetrofitting permissions breaks existing flowsapi-agent defines roles and permissions before endpoint design
Missing error statesHappy-path-only architecture crumbles in productionscaling-agent traces failure modes for every critical operation
"We'll add monitoring later"Debugging production without observability is guessworkinfrastructure-agent includes logging and error tracking in v1
Over-engineering for scaleBuilding for 1M users when you have 100 wastes monthsscaling-agent designs for 10x current load, plans for 100x

Worked Example

User: "I need architecture for a SaaS invoicing tool. Small businesses send invoices, clients pay online. Need Stripe integration."

Orchestrator gathers context:

  • Users: business owners (send invoices), clients (view/pay)
  • Scale: ~500 businesses, ~2000 invoices/month at launch
  • Integrations: Stripe for payments, SendGrid for email
  • Constraints: small team, fast launch needed

Layer 1 dispatch (parallel):

  • stack-selection-agent → recommends Next.js + Supabase + Clerk + Stripe + Vercel
  • infrastructure-agent → plans Vercel deployment, GitHub Actions CI/CD, Sentry monitoring

Layer 2 chain (sequential):

  • schema-agent → designs businesses, invoices, payments, clients tables with indexes on (business_id, status) and (stripe_payment_id)
  • api-agent → maps POST /api/invoices (business_owner), GET /api/pay/:token (public), POST /api/webhooks/stripe (stripe_signature)
  • integration-agent → designs file structure, Stripe checkout flow, SendGrid email integration
  • scaling-agent → identifies invoice PDF generation as first bottleneck at 10x, traces webhook failure modes

Critic review: PASS — all 7 quality gates pass.

Artifact saved to .agents/system-architecture.md with all 12 sections.


Artifact Template

On re-run: rename existing artifact to system-architecture.v[N].md and create new with incremented version.

---
skill: system-architecture
version: 1
date: {{today}}
status: draft
---

# System Architecture: [Product Name]

## 1. System Overview
## 2. Tech Stack
## 3. File & Folder Structure
## 4. Database Schema
## 5. API Architecture
## 6. State Management & Data Flow
## 7. Service Connections
## 8. Authentication & Authorization
## 9. Key Features Implementation
## 10. Deployment & Infrastructure
## 11. Monitoring & Debugging
## 12. Security Checklist

## Open Questions
## Next Step
Run `task-breakdown` to decompose this architecture into implementable tasks.

References

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能力概览

能力 1

按任务关键词查找相关 Skills

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

38.15%
按下载量换算25

Claude

28.6%
按下载量换算19

Cursor

19.82%
按下载量换算13

Gemini CLI

9.27%
按下载量换算6

安全审计

Gen Agent Trust Hub

通过

Socket

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

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