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architect架构师

Agent Skill

architect 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:architect(架构师)
来源仓库:https://github.com/jschulte/claude-plugins
仓库路径:skills/architect
安装命令:
npx skills add https://github.com/jschulte/claude-plugins --skill architect
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/jschulte/claude-plugins --skill architect

简介

用于查找、检索和筛选相关信息。architect 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

  • 适合根据关键词或任务场景快速定位候选结果。
  • 可辅助研究类任务的信息收集环节。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。
  • 建议结合具体需求验证搜索策略的有效性。
  • 需注意来源仓库的维护状态和实际功能边界。

SKILL.md

Architecture Generator

Generate an architecture document from reverse-engineering documents (hereafter "RE docs") and user constraints.

Estimated Time: 10-20 minutes Prerequisites: Gear 2 (Reverse Engineer) completed with RE docs present Output: architecture.md — location determined by mode detection in Step 4

This skill assumes single-session execution. If interrupted, restart from Step 1.


Process

Step 1: Load Context

Read all available RE docs from docs/reverse-engineering/. Read all files in parallel for speed.

Primary architecture sources (5 files):

  • data-architecture.md — Current data models, API contracts, domain boundaries
  • integration-points.md — External services, data flows, auth patterns
  • operations-guide.md — Current deployment, infrastructure, scalability
  • decision-rationale.md — Why current choices were made, trade-offs
  • configuration-reference.md — Configuration landscape

Supporting context (4 files):

  • functional-specification.md — What the system needs to do
  • business-context.md — Business constraints, scale expectations, compliance
  • technical-debt-analysis.md — What needs to change
  • observability-requirements.md — Monitoring and logging needs

Error handling for Step 1:

  1. Use Glob to list all files in docs/reverse-engineering/.
  2. Compare found files against the 9 listed above. Classify each as present or missing.
  3. If any primary architecture source (the first 5) is missing: report the missing files to the user and ask whether to proceed with partial context or stop. Do not silently continue.
  4. If only supporting context files (the last 4) are missing: proceed, but log which files are absent. Record these gaps for the assumptions section of the architecture document.
  5. If docs/reverse-engineering/ does not exist or is empty: stop and tell the user to run Gear 2 (Reverse Engineer) first.

Log after completing Step 1: "Step 1 complete: Loaded [N] of 9 RE docs. [List any missing files.]"

Step 2: Ask Constraint Questions

Present these questions conversationally. Skip a question only if a single unambiguous answer is directly stated in the RE docs. When skipping a question, state the detected answer and ask the user to confirm or override. If multiple options are plausible or the RE docs are silent on a topic, ask the question.

Question 1: Tech Stack Preference

What tech stack do you want for the architecture?

A) Same as current (documented in decision-rationale.md)
   [Show detected stack: e.g., "TypeScript + Next.js + PostgreSQL"]

B) Let me specify
   Ask: "What languages, frameworks, and databases?"
   Examples: "Next.js 15 + TypeScript + Prisma + PostgreSQL"
             "Python + FastAPI + SQLAlchemy + PostgreSQL"
             "Go + Gin + GORM + PostgreSQL"

C) Recommend based on requirements
   Analyze functional-specification.md + business-context.md
   Recommend stack with rationale

Question 2: Deployment Target

Where will this run?

A) AWS (EC2, ECS, Lambda, RDS, etc.)
B) Google Cloud (GKE, Cloud Run, Cloud SQL, etc.)
C) Azure (AKS, App Service, Azure SQL, etc.)
D) Self-hosted / On-premise
E) Hybrid (specify)
F) Recommend based on requirements

Question 3: Scale Expectations

What scale should the architecture support?

A) Startup / MVP
   Single-region, simple deployment, cost-optimized
   100s of users, minimal redundancy

B) Growing Product
   Multi-AZ, auto-scaling, managed services
   1,000s - 10,000s of users

C) Enterprise / High-Scale
   Multi-region, microservices-ready, full redundancy
   100,000s+ users, strict SLAs

D) Specify custom requirements
   Ask for: expected users, requests/sec, data volume, SLA targets

Question 4: Hard Constraints (free text, optional)

Any hard constraints to keep in mind?

Examples:
- "Must be HIPAA compliant"
- "Budget under $500/month"
- "Team of 3 developers, keep it simple"
- "Must support offline mode"
- "No vendor lock-in"
- "Must use Kubernetes"

Enter constraints or press enter to skip.

Question 5: Architecture Style If the architecture style is clear from the RE docs, skip this question: state the detected style and ask the user to confirm or override. Otherwise, ask:

What architecture style fits your needs?

A) Monolith (recommended for small teams / MVPs)
   Single deployable, simpler operations
   Can be modular monolith with clear boundaries

B) Microservices (recommended for larger teams / scale)
   Independent deployments, team autonomy
   Higher operational complexity

C) Serverless (recommended for event-driven / variable load)
   Pay-per-use, auto-scaling
   Cold start considerations

D) Hybrid (specify)

E) Recommend based on team size and requirements

Error handling for Step 2: If the user provides contradictory constraints (e.g., "budget under $100/month" and "multi-region full redundancy"), identify the conflict explicitly, explain why the constraints conflict, and ask the user to resolve before proceeding.

For "Recommend" answers on any question, analyze functional-specification.md + business-context.md + observability-requirements.md to make an informed suggestion. Present the recommendation with rationale and ask the user to confirm.

Log after completing Step 2: "Step 2 complete: Collected constraints for [N] questions. Proceeding to generation."

Step 3: Generate Architecture Document

Using the RE docs and user constraints, generate the architecture document. Follow the template at operations/architecture-template.md (relative to this skill).

Generation approach:

  1. Start from the current architecture as described in the RE docs.
  2. For each architecture decision, check user constraints and select the option that satisfies all hard constraints. Where constraints conflict, prefer the user's explicit choice over inferred preferences.
  3. For each component in the current architecture, determine whether it maps to the target state unchanged, needs modification, or should be replaced. Generate recommendations that bridge current to target state.
  4. Create ADRs justifying each major decision. Generate 3-10 ADRs focusing on consequential decisions. Source from decision-rationale.md where available; generate new ADRs for target-state decisions.
  5. Draw Mermaid diagrams for visual clarity. Use Mermaid C4 notation for system context diagrams.
  6. If the RE docs indicate a brownfield/evolution scenario (existing system being migrated or modernized), generate Section 10: Migration Path using the technical-debt-analysis.md Migration Priority Matrix. If the project is greenfield with no existing system to migrate from, omit Section 10.

Progress signals: Announce each major section as you generate it (e.g., "Generating Section 3: System Architecture..."). This ensures all sections are produced and prevents drift during the lengthy generation.

Step 3.5: Verify Generated Content

Before writing the final document, verify:

  1. All Mermaid diagrams use valid syntax (proper node definitions, valid arrow notation, balanced subgraphs).
  2. ADR decisions are consistent with the stated user constraints. No ADR should contradict a hard constraint from Step 2.
  3. Cost estimates align with the chosen cloud provider and scale tier. Flag all cost estimates as rough approximations.
  4. If Section 10 (Migration Path) is included, confirm it references actual current-state findings from the RE docs.
  5. The technology stack table is fully populated with rationale for each choice.

If any verification check fails, fix the issue before proceeding to Step 4.

Step 4: Detect Mode and Write Output

Mode detection:

  • If the directory _bmad-output/ exists in the project root, operate in BMAD mode.
  • Otherwise, operate in standalone mode.

BMAD mode: Write to _bmad-output/planning-artifacts/architecture.md. If running as part of BMAD Synthesize and RE docs have already been parsed in this session, reuse the parsed context instead of re-reading files.

Standalone mode: Write to docs/architecture.md. If the docs/ directory does not exist, create it.

Error handling for Step 4: If the file write fails, retry once. If it fails again, report the error to the user with the full file path that was attempted.

Log after completing Step 4: "Architecture document written to [full path]. Generation complete."


Verification Checklist

After writing, confirm all of the following before reporting completion:

  • All 10 sections present (or Section 10 intentionally omitted for greenfield)
  • Mermaid diagrams included (context, component, data flow, infrastructure)
  • 3-10 ADRs generated for major decisions
  • User constraints respected throughout
  • Cost estimation included (if cloud deployment)
  • Technology stack justified with rationale
  • Missing RE doc gaps noted in assumptions (if any docs were absent in Step 1)

Integration with Other Skills

BMAD Synthesize: This skill can run as part of /stackshift.bmad-synthesize. Output is compatible with BMAD's architecture.md format.

Reimagine: /stackshift.reimagine may invoke this skill to generate architecture for the reimagined system. Constraint questions are informed by multi-repo capability analysis.

Spec Kit: The architecture document supplements .specify/ structure and can be referenced from feature specs as architectural context.

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

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

32.72%
按下载量换算22

Claude

33.46%
按下载量换算22

Cursor

19.98%
按下载量换算13

Gemini CLI

9.91%
按下载量换算7

安全审计

Gen Agent Trust Hub

通过

Socket

未通过

Snyk

通过

权限和风险

external-service

该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

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。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。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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