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Agent Skill · 研究检索

nm-attune-architecture-aware-init(nm attune 架构感知初始化)

nm-attune-architecture-aware-init 用于查找、检索和筛选相关信息,适合在 OpenClaw 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。

nm-attune-architecture-aware-init 是研究检索类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为执行命令;许可证为 MIT-0,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 25,827。

总安装 3,684周安装 152GitHub Stars 公开资料未说明下载 1,204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
MIT-0
版本
1.0.0

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:nm-attune-architecture-aware-init(nm attune 架构感知初始化)
来源仓库:https://github.com/athola/nm-attune-architecture-aware-init
安装命令:
openclaw skills install nm-attune-architecture-aware-init
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 OpenClaw 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。

ClawHubOpenClaw
openclaw skills install nm-attune-architecture-aware-init

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。

nm-attune-architecture-aware-init 结合在线研究与原型选择进行架构感知初始化。适合新项目启动阶段快速收敛技术栈和基础架构方案。自动调研行业趋势并生成候选架构草图,加速决策过程。

安装命令:openclaw skills install nm-attune-architecture-aware-init。输出仅为建议,实际落地前需进行可行性验证和成本评估。

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 OpenClaw;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 openclaw skills install nm-attune-architecture-aware-init;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

    它需要执行命令,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

nm-attune-architecture-aware-init 怎么安装?

在终端执行 openclaw skills install nm-attune-architecture-aware-init。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

nm-attune-architecture-aware-init 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。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。

nm-attune-architecture-aware-init 需要什么权限?

权限级别是执行命令。安装流程涉及命令执行,可能通过 openclaw skills install nm-attune-architecture-aware-init 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

nm-attune-architecture-aware-init 是免费的吗?

来源标注的许可证是 MIT-0。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

nm-attune-architecture-aware-init 用的人多吗?

公开数据是累计安装 3,684 次,近一周安装 152 次,下载 1,204 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。

nm-attune-architecture-aware-init 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

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。
  • 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

  • 需要执行命令,装到常用环境前要先评估影响面。
  • 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 OpenClaw,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。
  • 需要许可证清楚、并且有第三方扫描结果的团队项目。

不太适合

  • 不能接受 Skill 在你机器上执行命令的人。
  • 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
nm-attune-architecture-aware-init

nm attune 架构感知初始化 · 本页

3,684—执行命令MIT-0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

name
architecture-aware-init
description
|
version
1.8.2
triggers
metadata
{"openclaw": {"homepage": "https://github.com/athola/claude-night-market/tree/master/plugins/attune", "emoji": "\�\�", "requires": {"config": ["night-market.architecture-paradigms"]}}}
source
claude-night-market
source_plugin
attune
Night Market Skill — ported from claude-night-market/attune. For the full experience with agents, hooks, and commands, install the Claude Code plugin.

Table of Contents

Architecture-Aware Project Initialization

Overview

Enhanced project initialization that combines:

  • Deep online research into current best practices for your project type
  • Archetype selection from the 14 paradigms in the archetypes plugin
  • Template customization based on chosen architecture
  • Decision documentation for future reference

When to Use This Skill

Use this skill when:

  • Starting a new project and unsure which architecture fits best
  • Want modern, industry-standard architecture choices
  • Need justification for architectural decisions
  • Want templates customized to your chosen paradigm

Use instead of project-init when architecture is undecided. Use before project-specification to establish architectural foundation.

Required TodoWrite Items

  1. arch-init:research-completed - Online research completed
  2. arch-init:paradigm-selected - Architecture paradigm chosen
  3. arch-init:templates-customized - Templates adapted to paradigm
  4. arch-init:decision-recorded - ADR created

5-Step Workflow

Step 1: Gather Project Context

Essential Information (ask user):

1. **Project Type**: What are you building?
   - Web API, CLI tool, data pipeline, desktop app, library, etc.

2. **Domain Complexity**: How complex are the business rules?
   - Simple (CRUD), Moderate (some business logic), Complex (many rules),
     Highly Complex (domain-specific language needed)

3. **Team Context**: Who will build and maintain this?
   - Team size: < 5 | 5-15 | 15-50 | 50+
   - Experience: Junior | Mixed | Senior | Expert
   - Distribution: Co-located | Remote | Distributed

4. **Non-Functional Requirements**:
   - Scalability needs (users, requests/sec, data volume)
   - Performance requirements
   - Security/compliance needs
   - Integration points (external systems, databases, APIs)

5. **Timeline & Constraints**:
   - Time to market: Rapid | Normal | Not urgent
   - Budget constraints
   - Technology constraints (must-use or must-avoid technologies)

Verification: Run the command with --help flag to verify availability.

Step 2: Research Best Practices (arch-init:research-completed)

Online Research Queries (use WebSearch):

For the project type, search for:

# Primary: Architecture patterns for [project type] [year]
WebSearch("[project type] architecture best practices 2026")

# Secondary: Language-specific patterns
WebSearch("[language] [project type] architecture patterns 2026")

# Tertiary: Framework-specific guidance
WebSearch("[framework] architecture patterns [project type]")

Verification: Run the command with --help flag to verify availability.

Research Focus Areas:

  1. Current Industry Standards: What are practitioners recommending in 2026?
  2. Emerging Patterns: Any new architectural approaches gaining traction?
  3. Anti-Patterns: What practices are being actively discouraged?
  4. Technology Alignment: Which patterns work best with your chosen stack?
  5. Case Studies: Real-world examples of similar projects

Synthesize Findings into:

  • Recommended architecture(s) for this project type
  • Key trade-offs to consider
  • Red flags or anti-patterns to avoid
  • Technology-specific considerations

Step 3: Select Architecture Paradigm (arch-init:paradigm-selected)

Option A: Manual Selection Using archetypes Plugin

Invoke the architecture paradigms skill:

**Verification:** Run the command with `--help` flag to verify availability.
Skill(architecture-paradigms)

Verification: Run the command with --help flag to verify availability.

This will guide you through selecting from the 14 available paradigms:

  • Layered Architecture
  • Functional Core, Imperative Shell
  • Hexagonal (Ports & Adapters)
  • Modular Monolith
  • Microservices
  • Service-Based Architecture
  • Event-Driven Architecture
  • CQRS + Event Sourcing
  • Serverless
  • Space-Based Architecture
  • Pipeline Architecture
  • Microkernel Architecture
  • Client-Server Architecture

Option B: Automated Paradigm Matching

Use the decision matrix below to recommend a paradigm based on project context:

**Verification:** Run the command with `--help` flag to verify availability.
┌─────────────────────┬─────────┬─────────┬──────────┬─────────────┐
│ Project Context     │ Simple  │ Moderate│ Complex  │ Highly      │
│                     │ Domain  │ Domain  │ Domain   │ Complex     │
├─────────────────────┼─────────┼─────────┼──────────┼─────────────┤
│ < 5 engineers       │ Layered │ Layered │ Hexagonal│ Functional  │
│                     │         │ Hexag. │ Functional│ Core        │
├─────────────────────┼─────────┼─────────┼──────────┼─────────────┤
│ 5-15 engineers      │ Layered │ Modular │ Modular  │ Hexagonal   │
│                     │         │ Monolith│ Monolith │ + FC, IS    │
├─────────────────────┼─────────┼─────────┼──────────┼─────────────┤
│ 15-50 engineers     │ Modular │ Micro-  │ Micro-   │ CQRS/ES     │
│                     │ Monolith│ services│ services │ + Event     │
├─────────────────────┼─────────┼─────────┼──────────┼─────────────┤
│ 50+ engineers       │ Micro-  │ Micro-  │ Event-   │ Microkernel │
│                     │ services│ services│ Driven   │ or Space-   │
│                     │         │ + Event │          │ Based       │
└─────────────────────┴─────────┴─────────┴──────────┴─────────────┘

Verification: Run the command with --help flag to verify availability.

Special Cases:

  • Real-time/Streaming: Event-Driven + Pipeline
  • Bursty/Cloud-Native: Serverless
  • Extensible Platform: Microkernel
  • Data Processing: Pipeline + Event-Driven
  • Legacy Integration: Hexagonal
  • High-Throughput Stateful: Space-Based

Step 4: Customize Templates (arch-init:templates-customized)

Template Adaptation Strategy:

  1. Load Base Templates for the chosen language (Python/Rust/TypeScript)
  2. Apply Architecture-Specific Modifications based on selected paradigm
  3. Generate Custom Configuration reflecting architectural choices
  4. Create Documentation explaining the architecture

Example Adaptations:

For Functional Core, Imperative Shell:

**Verification:** Run the command with `--help` flag to verify availability.
src/
├── core/                    # Pure business logic
│   ├── domain.py           # Domain models
│   ├── operations.py       # Pure functions
│   └── commands.py         # Command objects
└── adapters/               # Side effects
    ├── database.py         # DB operations
    ├── api.py              # HTTP operations
    └── filesystem.py       # File operations

Verification: Run the command with --help flag to verify availability.

For Hexagonal Architecture:

**Verification:** Run the command with `--help` flag to verify availability.
src/
├── domain/                 # Business logic (no framework deps)
│   ├── models.py
│   ├── services.py
│   └── ports/             # Interfaces
│       ├── input.py       # Use cases
│       └── output.py      # Repository interfaces
└── infrastructure/        # Framework-specific code
    ├── persistence/       # Repositories
    ├── web/               # Controllers
    └── messaging/         # Event handlers

Verification: Run the command with --help flag to verify availability.

For Microservices:

**Verification:** Run the command with `--help` flag to verify availability.
project/
├── services/
│   ├── service-a/         # Independent service
│   │   ├── src/
│   │   ├── tests/
│   │   ├── Dockerfile
│   │   └── pyproject.toml
│   └── service-b/         # Independent service
│       ├── src/
│       ├── tests/
│       ├── Dockerfile
│       └── pyproject.toml
├── api-gateway/
├── shared/
│   └── events/
└── docker-compose.yml

Verification: Run pytest -v to verify tests pass.

Step 5: Create Architecture Decision Record (arch-init:decision-recorded)

Generate ADR documenting the architecture choice:

# Architecture Decision Record: [Paradigm Name]

## Date
[Current date]

## Status
Accepted | Proposed | Deprecated | Superseded by [link]

## Context
[Project type, team size, domain complexity, key requirements]

## Decision
[Chosen architecture paradigm]

## Rationale
### Research Findings
[Summarize online research results]

### Key Considerations
- **Team Fit**: [Why this matches team size/experience]
- **Domain Fit**: [Why this matches problem complexity]
- **Technology Fit**: [Why this works with chosen stack]
- **Scalability**: [How this addresses scaling needs]

### Alternatives Considered
1. **[Alternative 1]**: Rejected because [reason]
2. **[Alternative 2]**: Rejected because [reason]

## Consequences
### Positive
- [Benefit 1]
- [Benefit 2]

### Negative
- [Trade-off 1] with mitigation: [strategy]
- [Trade-off 2] with mitigation: [strategy]

## Implementation
- **Templates**: [Which templates were customized]
- **Key Patterns**: [Patterns to follow]
- **Anti-Patterns**: [What to avoid]
- **Resources**: [Links to paradigm skill, examples, etc.]

## References
- [Paradigm skill link]
- [Research sources]
- [Example projects]

Verification: Run the command with --help flag to verify availability.

Output: Complete Initialization Package

After completing this workflow, you'll have:

  1. Project Structure: Customized to chosen architecture
  2. Configuration: Architecture-appropriate tooling and dependencies
  3. Documentation: ADR explaining the architecture choice
  4. Guidance: Links to relevant paradigm skill for implementation
  5. Examples: Reference projects using similar architecture

Script Integration

Claude Code can invoke the architecture research and template customization scripts:

Architecture Research

# Run architecture researcher for recommendations
uv run python plugins/attune/scripts/architecture_researcher.py \
  --project-type web-api \
  --domain-complexity complex \
  --team-size 5-15 \
  --language python \
  --output-json

Verification: Run python --version to verify Python environment.

The researcher returns a recommendation with:

  • Primary paradigm and rationale
  • Trade-offs and mitigations
  • Alternative paradigms considered
  • Confidence level

Template Customization

# Generate architecture-specific directory structure
uv run python plugins/attune/scripts/template_customizer.py \
  --paradigm cqrs-es \
  --language python \
  --project-name my-project \
  --output-dir ./my-project

Verification: Run python --version to verify Python environment.

This creates the paradigm-appropriate structure (e.g., commands/, queries/, events/ for CQRS).

Full Interactive Flow

# Interactive architecture-aware initialization
uv run python plugins/attune/scripts/attune_arch_init.py \
  --name my-project \
  --lang python

# Non-interactive with explicit architecture
uv run python plugins/attune/scripts/attune_arch_init.py \
  --name my-project \
  --lang python \
  --arch hexagonal \
  --accept-recommendation

Verification: Run python --version to verify Python environment.

Using as Library (within Claude Code)

# Import and use programmatically
from architecture_researcher import ArchitectureResearcher, ProjectContext
from template_customizer import TemplateCustomizer

# Create context and get recommendation
context = ProjectContext(
    project_type="web-api",
    domain_complexity="complex",
    team_size="5-15",
    language="python"
)
researcher = ArchitectureResearcher(context)
recommendation = researcher.recommend()

# Apply template customization
customizer = TemplateCustomizer(
    paradigm=recommendation.primary,
    language="python",
    project_name="my-project"
)
customizer.apply_structure(Path("./my-project"))

Verification: Run the command with --help flag to verify availability.

Integration with Existing Commands

This skill enhances /attune:project-init by adding an architecture selection phase:

# Standard initialization (no architecture decision)
/attune:project-init --lang python --name my-project

# Architecture-aware initialization
/attune:brainstorm           # Explore project needs
Skill(architecture-aware-init) # Select architecture based on research
/attune:project-init --arch <paradigm> # Initialize with chosen architecture

Verification: Run python --version to verify Python environment.

Example Session

User: I'm creating a Python web API for a fintech application. Team of 8 developers, complex business rules, need high security and audit trails.

Step 1 - Context: Project type=Web API, Domain=Highly Complex, Team=5-15, Requirements=Security, Auditability

Step 2 - Research:

WebSearch("Python fintech API architecture patterns 2026")
WebSearch("financial services API audit trail architecture")
WebSearch("CQRS Event Sourcing Python examples")

Verification: Run python --version to verify Python environment.

Step 3 - Selection: Research + Decision Matrix → CQRS + Event Sourcing

Step 4 - Templates: Customized structure for CQRS/ES with:

  • Command handling module
  • Query handling module
  • Event store configuration
  • Aggregate patterns
  • Projection handlers

Step 5 - ADR: Documenting why CQRS/ES for fintech (auditability, complex business rules, regulatory compliance)

Result: Project initialized with architecture-appropriate structure and clear decision rationale.

Related Skills

  • Skill(architecture-paradigms) - Comprehensive paradigm selection
  • Skill(architecture-paradigm-*) - Specific paradigm implementation guidance
  • Skill(attune:project-brainstorming) - Project ideation before architecture
  • Skill(attune:project-specification) - Requirements after architecture chosen

See Also

  • /attune:project-init - Basic project initialization
  • /attune:blueprint - Architecture planning after paradigm selection
  • Architecture paradigms README for paradigm details

Troubleshooting

Common Issues

Command not found Ensure all dependencies are installed and in PATH

Permission errors Check file permissions and run with appropriate privileges

Unexpected behavior Enable verbose logging with --verbose flag

平台分布

OpenClaw

72.87%
按下载量换算877

安全审计

VirusTotal

未展示

ClawScan

通过

Static analysis

通过

权限和风险

执行命令

安装流程涉及命令执行,可能通过 openclaw skills install nm-attune-architecture-aware-init 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

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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