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write-adr写地址

Agent Skill

write-adr 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

346

周安装

14

GitHub Stars

54

下载量

109
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:write-adr(写地址)
来源仓库:https://github.com/existential-birds/beagle
仓库路径:skills/write-adr
安装命令:
npx skills add https://github.com/existential-birds/beagle --skill write-adr
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/existential-birds/beagle --skill write-adr

简介

write-adr 自动生成架构决策记录(ADR),从会话中的关键决策中提取并结构化输出。

  • 适合在分析代码变更、规划技术方案或沉淀团队决策时使用。
  • 通过 GitHub 仓库安装,需确认本地是否有 ADR 目录及 Git 环境支持。
  • 涉及文件写入时建议先备份,避免覆盖重要文档或触发意外提交。
  • write-adr 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

Write ADR

Generate Architecture Decision Records (ADRs) from decisions made during the current session.

Workflow Overview

  1. Context - Gather repository context and existing ADRs
  2. Extract - Analyze conversation for decisions using a subagent
  3. Confirm - Present decisions to user for selection
  4. Write - Generate ADRs in parallel using subagents
  5. Report - Summarize created files and status
  6. Verify - Validate generated ADRs against Definition of Done

Step 1: Gather Context

# Get current branch and recent commits
git branch --show-current
git log --oneline -5

# Check for existing ADRs
ls docs/adrs/ 2>/dev/null || echo "No ADR directory found"

# Count existing ADRs for numbering
find docs/adrs -name "*.md" 2>/dev/null | wc -l

This context helps the ADR writer:

  • Reference related commits in the ADR
  • Avoid duplicate ADRs for already-documented decisions
  • Determine correct sequence numbering

Step 2: Extract Decisions

Launch a subagent to analyze the current conversation for architectural decisions:

Task(
  description: "Analyze conversation and extract architectural decisions",
  model: "sonnet",
  prompt: |
    Load the skill: Skill(skill: "beagle-analysis:adr-decision-extraction")

    Analyze the conversation for decisions that warrant ADRs:
    - Technology choices, architecture patterns, design trade-offs
    - Rejected alternatives, significant implementation approaches

    Return JSON:
    {
      "decisions": [
        {
          "id": 1,
          "title": "Use PostgreSQL for primary datastore",
          "context": "Brief context about why this came up",
          "decision": "What was decided",
          "alternatives": ["What was considered but rejected"],
          "rationale": "Why this choice was made"
        }
      ]
    }
)

If the subagent returns an empty decisions array, skip to Step 5 with message: "No architectural decisions detected in this session."

Step 3: Confirm with User

Display all extracted decisions with full details, then ask user to select:

## Detected Decisions

### 1. Use PostgreSQL for primary datastore
**Confidence:** high

**Problem:** Need ACID transactions for financial records

**Decision:** PostgreSQL for user data storage

**Alternatives discussed:**
- MongoDB
- SQLite

**Rationale:** ACID compliance, team familiarity, mature ecosystem

**Source:** Discussion about database selection in planning phase

---

### 2. Implement event sourcing for audit trail
**Confidence:** medium

**Problem:** Compliance requires complete audit history

**Decision:** Event sourcing pattern for state changes

**Alternatives discussed:**
- Database triggers
- Application-level logging

**Rationale:** Immutable audit trail, temporal queries, debugging capability

**Source:** Compliance requirements discussion

---

## Selection

Which decisions should I write ADRs for?
- Enter numbers (e.g., "1,2" or "1-2"), "all", or "none" to skip

Important: Always display the full decision details (problem, decision, alternatives, rationale) from the extraction output BEFORE asking for selection. Do not truncate to just title and context.

Parse user response:

  • "all" - Process all decisions
  • "none" or empty - Skip with message "No ADRs will be created."
  • "1,2" or "1-2" - Process specified decisions

Step 4: Write ADRs (Parallel)

Pre-allocate ADR numbers before launching subagents to prevent numbering conflicts:

# Pre-allocate numbers for all confirmed decisions
# Example: If user selected 3 decisions
python skills/adr-writing/scripts/next_adr_number.py --count 3
# Output:
# 0003
# 0004
# 0005

Assign each pre-allocated number to its corresponding decision before launching subagents.

For each confirmed decision, launch an ADR Writer subagent in background with its pre-assigned number:

Task(
  description: "Write ADR for: {decision.title}",
  model: "sonnet",
  run_in_background: true,
  prompt: |
    Load the skill: Skill(skill: "beagle-analysis:adr-writing")

    Write an ADR for this decision:

{decision JSON}


    **IMPORTANT: Use this pre-assigned ADR number: {assigned_number}**

    Instructions:
    1. Explore codebase for additional context
    2. Write MADR-formatted ADR to docs/adr/
    3. Use the pre-assigned number {assigned_number} - DO NOT call next_adr_number.py
    4. Filename format: {assigned_number}-slugified-title.md
    5. Return created file path
)

Critical: Pass the pre-allocated number to each subagent. Subagents must NOT call next_adr_number.py themselves - this causes duplicate numbers when running in parallel.

All subagents run in parallel. Wait for all to complete before proceeding.

Step 5: Report Results

Collect outputs from all subagents and present summary:

## ADR Generation Complete

| File | Decision | Status |
|------|----------|--------|
| docs/adr/0003-use-postgresql.md | Use PostgreSQL for primary datastore | Draft |

### Next Steps
- Review generated ADRs for accuracy
- Update status from "proposed" to "accepted" when finalized

### Gaps Requiring Investigation
- [List any decisions where subagent noted missing context]

If no decisions were processed:

No ADRs were created. Run this command again after making architectural decisions.

Step 6: Verify Generated ADRs

For each created ADR, validate against Definition of Done:

## Verification Checklist

| ADR | E | C | A | D | R | Status |
|-----|---|---|---|---|---|--------|
| 0003-use-postgresql.md | ✓ | ✓ | ✓ | ⚠ | ✗ | Incomplete |

Legend: E=Evidence, C=Criteria, A=Agreement, D=Documentation, R=Realization

Verification steps:

  1. Open each generated ADR file
  2. Confirm filename follows NNNN-slugified-title.md pattern
  3. Verify YAML frontmatter exists at file start:

- File MUST begin with --- - Contains status: draft (or valid status) - Contains date: YYYY-MM-DD (actual date) - Ends with --- before title - If frontmatter is missing, add it immediately

  1. Review for [INVESTIGATE] prompts - these need follow-up
  2. Verify at least 2 alternatives are documented
  3. Confirm consequences section has both Good and Bad items

If gaps exist:

  • Keep status as draft until gaps are resolved
  • Use [INVESTIGATE] prompts to guide follow-up session
  • Schedule review with stakeholders before changing to accepted

Output Location

ADRs are written to docs/adr/. If no ADR directory exists, create it with an initial 0000-use-madr.md template record.

MADR Format Reference

---
status: draft
date: YYYY-MM-DD
---

# {TITLE}

## Context and Problem Statement

{What is the issue motivating this decision?}

## Decision Drivers

* {driver 1}
* {driver 2}

## Decision Outcome

Chosen option: "{option}", because {reason}.

### Consequences

* Good, because {positive}
* Bad, because {negative}

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

36.45%
按下载量换算40

Claude

33.26%
按下载量换算36

Cursor

16.84%
按下载量换算18

Gemini CLI

9.99%
按下载量换算11

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

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

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