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self-prompt自我提示

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

11,257

周安装

451

GitHub Stars

公开资料未说明

下载量

3,644
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:self-prompt(自我提示)
来源仓库:https://github.com/eliranwong/self-prompt
安装命令:
openclaw skills install self-prompt
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 self-prompt

简介

强制代理使用“openclaw 代理”而不是“openclaw 消息发送”来响应自动消息,确保脚本中的任务得到代理回复。

SKILL.md

name
self-prompt
description
Force agent responses to scheduled/automated messages. Use when cron jobs, monitoring scripts, or automated systems send messages that agents ignore. Solves the problem where agents treat system-sent messages as "background info" instead of tasks requiring response. Key pattern: use openclaw agent instead of openclaw message send to trigger actual agent turns.

Self-Prompt: Forcing Agent Responses

The Problem

When automated scripts (cron, monitoring) send messages via openclaw message send:

  • Messages appear in chat as "system messages"
  • Agents may treat them as background info, not tasks
  • Agents respond to user messages but ignore automated ones
  • Accountability checks, alerts, and tasks go unanswered

The Solution

Use openclaw agent instead of openclaw message send for messages requiring agent response:

# OLD (agent may ignore):
openclaw message send --target -GROUP_ID --message "TASK: Do something"

# NEW (agent MUST respond):
RESPONSE=$(openclaw agent \
    --agent AGENT_ID \
    --session-id "agent:AGENT_ID:telegram:group:GROUP_ID" \
    --channel telegram \
    --message "TASK: Do something" \
    --timeout 180)

# Send response to chat:
openclaw message send --target -GROUP_ID --message "$RESPONSE"

Why This Works

  • openclaw message send → Creates chat message → Agent sees as "notification"
  • openclaw agent → Triggers actual agent turn → Agent MUST process and respond

Quick Start

For Bash Scripts

Use scripts/send_agent_task.sh:

# Simple usage:
~/.openclaw/skills/self-prompt/scripts/send_agent_task.sh \
    "AGENT_ID" \
    "GROUP_ID" \
    "Your task message here"

For Python Scripts

Use scripts/send_agent_task.py:

from send_agent_task import send_and_deliver

success, response = send_and_deliver(
    agent_id="stock-trading",
    group_id="-5283045656", 
    message="TASK: Analyze current positions",
    timeout=180
)

Pattern: Data + Task Separation

For monitoring scripts, separate data delivery from task requests:

# 1. Send DATA immediately (informational)
openclaw message send --target "$GROUP_ID" --message "📊 Position Data:
$POSITIONS"

# 2. Send TASK via agent (forces response)
RESPONSE=$(openclaw agent \
    --agent "$AGENT_ID" \
    --session-id "agent:$AGENT_ID:telegram:group:$GROUP_ID" \
    --message "Analyze the data above and report findings" \
    --timeout 180)

# 3. Deliver response
openclaw message send --target "$GROUP_ID" --message "📊 Analysis:
$RESPONSE"

Common Use Cases

Accountability Checks

# Force agent to respond to accountability check
send_agent_task.sh "my-agent" "-123456789" \
    "ACCOUNTABILITY CHECK: Did you complete the required tasks? Respond with status."

Monitoring Alerts

# Force agent to investigate and report
send_agent_task.sh "trading-agent" "-123456789" \
    "ALERT: Position down 5%. Investigate and report findings."

Scheduled Research Tasks

# Force agent to do research
send_agent_task.sh "research-agent" "-123456789" \
    "DAILY TASK: Search for news on $SYMBOLS and summarize findings."

Session Key Format

The session key follows this pattern:

agent:AGENT_ID:CHANNEL:TYPE:TARGET

Examples:

  • agent:stock-trading:telegram:group:-5283045656
  • agent:main:telegram:direct:123456789
  • agent:assistant:discord:channel:987654321

Script Reference

send_agent_task.sh

Location: scripts/send_agent_task.sh

send_agent_task.sh AGENT_ID GROUP_ID "message" [timeout]
  • Sends task via openclaw agent
  • Captures response
  • Sends response to group via message send
  • Logs to ~/agent_task.log

send_agent_task.py

Location: scripts/send_agent_task.py

from send_agent_task import send_and_deliver

success, response = send_and_deliver(
    agent_id="agent-name",
    group_id="-123456789",
    message="Task message",
    timeout=180,
    channel="telegram"
)

Troubleshooting

Agent not responding

  • Verify agent is running: openclaw gateway status
  • Check session key format matches your agent/group
  • Increase timeout for long-running tasks

Response not appearing in chat

  • Ensure script sends response via message send after capturing
  • Check GROUP_ID is correct (negative for groups)
  • Verify channel is correct (telegram/discord/etc)

Timeout errors

  • Increase timeout for research/analysis tasks (300-600 seconds)
  • Check if agent is overloaded with concurrent requests

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

74.28%
按下载量换算2,707

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

未展示

权限和风险

敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

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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