Token导航 LogoToken导航TokenDH.com
研究检索需要联网github未标认证来源可访问许可证需确认审计通过

architecture-patterns架构模式

Agent Skill

architecture-patterns 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

514

周安装

21

GitHub Stars

23

下载量

165
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:architecture-patterns(架构模式)
来源仓库:https://github.com/akaszubski/autonomous-dev
仓库路径:skills/architecture-patterns
安装命令:
npx skills add https://github.com/akaszubski/autonomous-dev --skill architecture-patterns
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/akaszubski/autonomous-dev --skill architecture-patterns

简介

确保架构方案具备可测试性和可操作性,为规划 Agent 提供决策框架。

  • 适用于问题分析、备选方案对比和风险评估,要求明确约束与推荐依据。
  • 使用时需结合项目范围文档,输出结构化决策记录。
  • 安装命令:npx skills add https://github.com/akaszubski/autonomous-dev --skill architecture-patterns
  • 建议核对项目上下文,避免脱离实际约束给出建议。

SKILL.md

Architecture Patterns Enforcement Skill

Ensures every architecture plan is thorough, actionable, and testable. Used by the planner agent.

Decision Framework

Every architectural decision MUST follow this structure:

1. Problem Statement

  • What problem are we solving?
  • What are the constraints (performance, compatibility, timeline)?
  • What does PROJECT.md say about scope?

2. Options Analysis

  • Minimum 2 alternatives considered
  • Each option has explicit pros and cons
  • Effort estimate for each (low/medium/high)

3. Recommendation

  • Which option and why
  • What tradeoffs are we accepting
  • What risks remain

Plan Structure Requirements

Every architecture plan MUST include:

File-by-File Breakdown

## Files to Create/Modify

### 1. lib/new_module.py (CREATE)
- Purpose: [what this file does]
- Key classes/functions: [list]
- Dependencies: [what it imports]
- Tests: tests/unit/test_new_module.py

### 2. lib/existing_module.py (MODIFY)
- Changes: [what changes and why]
- Lines affected: ~[range]
- Risk: [low/medium/high]

Ordered Steps with Dependencies

## Implementation Order

1. Create lib/new_module.py (no dependencies)
2. Create tests/unit/test_new_module.py (depends on step 1)
3. Modify lib/existing_module.py (depends on step 1)
4. Update integration tests (depends on steps 1-3)

Steps MUST be ordered so each step can be tested independently before proceeding.

Testing Strategy

  • Unit tests for each new module
  • Integration tests for cross-module interactions
  • What to mock and why
  • Expected test count estimate

Integration Points

  • What existing code is affected
  • API contracts between modules
  • Backward compatibility considerations

ADR Format for Major Decisions

For decisions that affect architecture (new patterns, technology choices, major refactors):

# ADR-NNN: [Title]

**Date**: YYYY-MM-DD
**Status**: Proposed | Accepted | Deprecated | Superseded

## Context
[Problem and constraints]

## Decision
[What we chose and why]

## Consequences
[Positive and negative outcomes]

## Alternatives Considered
[Other options and why rejected]

This Project's Patterns

When planning for this codebase, follow these established patterns:

Two-Tier Design

  • Core lib (plugins/autonomous-dev/lib/): Pure Python, no side effects, testable
  • CLI layer (plugins/autonomous-dev/commands/): Markdown prompts that invoke lib

Progressive Enhancement

  • Base functionality works without optional dependencies
  • GenAI reasoning enhances but never replaces deterministic checks
  • Skills loaded on-demand, not all at once

HARD GATE Enforcement

  • Critical rules use FORBIDDEN/REQUIRED language
  • Hooks validate at commit/push time (100% reliable)
  • Agents enforce at review time (conditional intelligence)

Hook-Based Validation

  • Pre-commit hooks for formatting, secrets, alignment
  • Pre-push hooks for tests, coverage
  • Pre-tool hooks for security and workflow enforcement

HARD GATE: Plan Quality

FORBIDDEN:

  • Plans without specific file paths (e.g., "create a new module" without saying where)
  • "TBD" or "to be determined" placeholders — decide now or state why you cannot
  • Plans with no testing strategy
  • Plans that skip scope validation against PROJECT.md
  • Monolithic steps that cannot be tested incrementally
  • Plans that ignore existing codebase patterns
  • Hand-wavy "refactor later" promises

REQUIRED:

  • File-by-file breakdown with CREATE/MODIFY labels
  • Dependency ordering (what must come before what)
  • Error handling strategy (what happens when things fail)
  • At least 2 alternatives considered for major decisions
  • Testing strategy with expected test locations
  • Rollback plan (how to undo if the plan fails)
  • Scope check against PROJECT.md goals

Anti-Patterns

BAD: Hand-wavy plan

"We should refactor the auth module to be more modular.
We can add better error handling later."

No file paths, no steps, no testing, no "later" promises.

GOOD: Actionable plan

## Files to Modify
1. lib/auth.py (MODIFY) — Extract token validation into TokenValidator class
2. lib/token_validator.py (CREATE) — Pure validation logic, no side effects
3. tests/unit/test_token_validator.py (CREATE) — 8 tests covering valid/invalid/expired

## Order
1. Create token_validator.py + tests (independent)
2. Modify auth.py to use TokenValidator (depends on step 1)
3. Run full test suite to verify no regressions

BAD: Monolithic steps

Step 1: Rewrite the entire authentication system
Step 2: Test everything

Cannot test incrementally, cannot roll back partially.

GOOD: Incremental steps

Each step produces a testable, committable unit of work.

BAD: Missing rollback plan

If the migration fails halfway through, what happens? Every plan needs a recovery path.


Cross-References

  • research-patterns: Research feeds into architecture planning
  • code-review: Plans are reviewed against these quality standards
  • documentation-guide: ADR format and documentation requirements

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

38.89%
按下载量换算64

Claude

28.7%
按下载量换算47

Cursor

19.33%
按下载量换算32

Gemini CLI

9.41%
按下载量换算16

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。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

继续浏览同类 Skills