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Agent Skill · 研究检索

system-architecture(系统架构)

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。

system-architecture 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,权限为需要联网;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 73,675 个 Skill 里排第 72,323。

总安装 188周安装 8GitHub Stars 1下载 66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
许可证
unknown

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

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。

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。

来源仓库内路径:skills/system-architecture

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/hungv47/prod-skills --skill system-architectur…;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

    它需要需要联网,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

system-architecture 怎么安装?

在终端执行 npx skills add https://github.com/hungv47/prod-skills --skill system-architecture。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

system-architecture 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。

system-architecture 需要什么权限?

权限级别是需要联网。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。

system-architecture 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。

system-architecture 用的人多吗?

公开数据是累计安装 188 次,近一周安装 8 次,下载 66 次,GitHub 1 星。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。

system-architecture 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。

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。

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
system-architecture

系统架构 · 本页

1881需要联网未明确通过

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

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

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

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。目前只有一个公开来源,建议同时核对源仓库。

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