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prompt-request提示请求

Agent Skill

用于辅助提示词、系统指令、Agent 行为约束和工作流模板的整理。它适合让 Agent 规范任务边界、统一输出格式、拆分操作步骤或优化提示词可复用性。使用时需要保留真实业务约束,不要把示例当硬规则;涉及自动执行、外部工具或高风险操作时,应在提示词中明确确认步骤、权限边界和失败处理方式。

总安装

16,392

周安装

683

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下载量

5,464
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:prompt-request(提示请求)
来源仓库:https://github.com/shunsukehayashi/prompt-request
安装命令:
openclaw skills install prompt-request
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 prompt-request

简介

实现 GitHub Issue 到代码实现的自动化开发流程。

  • 支持自动分析任务、生成 PR、触发审核与合并的全链路处理。
  • 使用 [auto] 标签标记 Issue 即可启动自动化实施管道。
  • 适用于需要快速响应需求并闭环交付的开发场景。prompt-request 属于开发类 Skill,可作为该场景下的辅助能力补充。
  • 依赖外部 API 与 GitHub 集成,需确保网络连通性与权限配置。

SKILL.md

name
prompt-request
description
>
version
1.0.0
author
ShunsukeHayashi
tags
[github, webhook, automation, pipeline, prompt-request]

Prompt Request Pipeline

Issue を書くだけで、自動実装 → PR → レビュー → マージまで全自動で回るパイプライン。

Overview

[auto] Issue 起票
  → 🚀 実装開始コメント
  → Phase A: タスク分解(Omega-bridge or Issue本文)
  → 📋 分析完了コメント
  → Phase B: 実装 + テスト
  → ✅ 実装完了コメント
  → Phase C: commit → push → PR作成
  → 🔗 PR作成コメント
  → 自動レビュー → 自動マージ → Issue close

Prerequisites

  • OpenClaw Gateway running with hooks enabled
  • GitHub CLI (gh) authenticated
  • Git SSH access to target repository
  • GitHub Webhook pointing to OpenClaw hooks endpoint

Setup

1. Register GitHub Webhook

On your GitHub repo → Settings → Webhooks → Add webhook:

  • Payload URL: https://<your-openclaw-endpoint>/hooks/github
  • Content type: application/json
  • Secret: Your OpenClaw hooks token
  • Events: Select individual events:

- Issues - Pull requests - Pull request reviews - Check runs - Issue comments - Push

2. Configure OpenClaw hooks

Add this to your openclaw.json under hooks.mappings:

{
  "match": { "path": "github" },
  "action": "agent",
  "name": "GitHub",
  "sessionKey": "hook:github:{{repository.name}}:{{headers.x-github-event}}:{{issue.number}}{{pull_request.number}}{{check_run.id}}",
  "messageTemplate": "<see templates/messageTemplate.txt>",
  "deliver": true,
  "allowUnsafeExternalContent": true,
  "channel": "telegram",
  "to": "<your-chat-id>",
  "model": "anthropic/claude-opus-4-6",
  "thinking": "high",
  "timeoutSeconds": 900
}

3. Set working directory

In the messageTemplate, replace the working directory path:

  • WORKDIR variable: where repositories are cloned (e.g., C:\Users\you\Dev or /home/you/dev)

4. (Optional) Omega-bridge

If you have Miyabi's omega-bridge for SWML-based task decomposition:

  • Set the path to omega-bridge.ts in the messageTemplate
  • If not available, the pipeline falls back to implementing directly from Issue body

Usage

Basic: Create an [auto] Issue

Title: [auto] Add utility function X

Body:
## Requirements
- Create scripts/x.sh with function do_x()
- Add tests in tests/test-x.sh

## Acceptance Criteria
- Function returns expected output
- Tests pass

Advanced: With agent personality

Title: [auto] [content] Write article about Y

Body:
Read agents/content-agent/AGENTS.md and SOUL.md first.
Follow the rules defined there.

## Topic
...

## Output
- File: articles/y.md
- Word count: 6000

Advanced: With skill reference

Title: [auto] Generate report with weather data

Body:
Read skills/weather/SKILL.md for API usage.

## Requirements
...

How It Works

Issue Events (action=opened)

  1. Early exit check: If action is closed/labeled/etc → 1-line reply, stop
  2. [auto] check: Title starts with [auto] or body contains <!-- auto-implement -->
  3. Phase A: Task decomposition (omega-bridge or direct)
  4. Phase B: Implementation (branch, code, test)
  5. Phase C: Integration (commit, push, PR)
  6. Progress comments posted at each phase

PR Events (action=opened/synchronize)

  1. Skip bot senders (loop prevention)
  2. Diff review for quality/security
  3. Auto-merge if ALL conditions met:

- PR title contains [auto] or branch starts with feature/issue- - Review is LGTM - CI checks pass (or empty = pass) - No merge conflicts

Safety Rules

  • Never force push
  • Never push directly to main
  • Never run permission commands (icacls/chmod/chown)
  • Max 3 CI fix retries per PR
  • Bot sender events are skipped

Configuration Options

OptionDefaultDescription
modelclaude-opus-4-6Model for hook sessions
thinkinghighThinking level
timeoutSeconds900Max execution time (15 min)
delivertrueSend results to chat
channeltelegramDelivery channel

Performance (measured)

MetricBefore optimizationAfter optimization
close/push events8-12 min, ~500 tokens3 sec, ~15 tokens
[auto] Issue → merged PRN/A (stuck)~5 min
Full pipeline (Issue → merge)N/A~5 min

Tips

  • Keep Issues small: 1 Issue = 1 clear deliverable, ≤300 lines of diff
  • Be specific: The quality of the Issue body directly determines output quality
  • Use templates: Create Issue templates for recurring task types
  • Reference skills: Point the agent to relevant SKILL.md files for domain knowledge
  • Reference agent definitions: Store AGENTS.md/SOUL.md in the repo for consistent behavior

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

98.11%
按下载量换算5,361

安全审计

VirusTotal

可疑

ClawScan

可疑

Static analysis

未展示

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。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。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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