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Agent Skill · 研究检索

write-adr(写地址)

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。

write-adr 是研究检索类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为只读;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 22,960。

总安装 4,406周安装 180GitHub Stars 公开资料未说明下载 1,426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
1.1.2

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。

ClawHubOpenClaw
openclaw skills install write-adr

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。

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。

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 write-adr;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

    它标注为只读,先用只读任务试一次,确认行为符合预期再扩大使用范围。

常见问题

6 条

write-adr 怎么安装?

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

write-adr 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。

write-adr 需要什么权限?

权限级别是只读。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。

write-adr 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。

write-adr 用的人多吗?

公开数据是累计安装 4,406 次,近一周安装 180 次,下载 1,426 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。

write-adr 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。
  • 标注为只读权限,试用阶段不容易误改本地文件。

先想清楚的地方

  • 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

  • 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
write-adr

写地址 · 本页

4,406—只读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

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}

平台分布

OpenClaw

77.82%
按下载量换算1,110

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

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。目前只有一个公开来源,建议同时核对源仓库。

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