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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 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。

总安装

4,406

周安装

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下载量

1,426
OpenClaw

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

MIT-0

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/anderskev/write-adr
安装命令:
openclaw skills install 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 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。

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openclaw skills install write-adr

简介

专为生成架构决策记录(ADR)设计,记录技术选型与系统设计关键决策过程。

  • 适用于复杂系统开发中保留上下文,便于团队成员理解演进脉络与约束条件。
  • 自动提取当前讨论要点,输出标准 ADR 模板与后续行动项,支持 Markdown 格式导出。
  • 需确认本地文件写入权限,注意决策结论应基于事实而非推测,避免误导未来维护者。
  • 建议在合并前经团队评审,确保术语一致性与可追溯性。

SKILL.md

description
Use when you want to generate Architecture Decision Records from this session. Triggers on \"write ADRs\", \"document our decisions\", \"create decision records\", \"record the choices we made\". Also useful after design discussions where decisions were reached but not documented. Does NOT extract decisions alone (use adr-decision-extraction) or provide MADR template (use adr-writing). Orchestrates the full workflow: subagent extraction, user confirmation, parallel generation, and verification.
name
write-adr
disable-model-invocation
true

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

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

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

补充不同宿主或平台的使用分布数据

能力 5

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

OpenClaw

77.82%
按下载量换算1,110

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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。

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