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Agent Skill · 研究检索

context-aware-delegation(上下文感知委托)

context-aware-delegation 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。

context-aware-delegation 是研究检索类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为接触敏感数据;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 5,065。

总安装 28,105周安装 1,126GitHub Stars 公开资料未说明下载 9,098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
1.0.2

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:context-aware-delegation(上下文感知委托)
来源仓库:https://github.com/rgba-research/context-aware-delegation
安装命令:
openclaw skills install context-aware-delegation
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 context-aware-delegation

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。

为子代理或定时任务提供完整对话上下文。适用于多会话协作与后台作业场景。适用宿主包括 OpenClaw,接入前应确认版本、权限和运行环境要求。自动注入历史记录,保持任务连续性。需注意上下文大小限制,避免超出模型处理能力。

context-aware-delegation 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

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 context-aware-delegation;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

    它需要接触敏感数据,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

context-aware-delegation 怎么安装?

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

context-aware-delegation 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。

context-aware-delegation 需要什么权限?

权限级别是接触敏感数据。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。

context-aware-delegation 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。

context-aware-delegation 用的人多吗?

公开数据是累计安装 28,105 次,近一周安装 1,126 次,下载 9,098 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。

context-aware-delegation 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。

先想清楚的地方

  • 需要接触敏感数据,装到常用环境前要先评估影响面。
  • 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

  • 环境里有大量密钥、凭证,不方便给第三方 Skill 读取的人。
  • 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
context-aware-delegation

上下文感知委托 · 本页

28,105—接触敏感数据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

name
context-aware-delegation
description
Give isolated sessions (cron jobs, sub-agents, event handlers) full conversation context from your main session using sessions_history. Run cheap background tasks (Haiku) with expensive context (Sonnet-level awareness) — best of both worlds.
homepage
https://gitlab.com/rgba_research/context-aware-delegation
author
RGBA Research
metadata

Context-Aware Delegation

(aka "SmartBeat")

Problem: Isolated sessions (cron jobs, sub-agents) can't see your main session conversation history. They're cheap (use Haiku) but blind to context.

Solution: Use sessions_history to give isolated sessions full awareness of what happened in your main chat — at a fraction of the cost of running everything in main session.

Quick Start

Morning Report Example

You want a daily report that includes "what we accomplished last night" — but running that in main session with Sonnet costs ~$0.30/report. Using an isolated session with Haiku costs ~$0.03, but can't see conversation history.

Solution: Isolated session queries main session history first.

// Inside your cron payload.message:
"1. Query main session history: sessions_history('agent:main:telegram:direct:{userId}', limit=50)
2. Read memory files: memory/YYYY-MM-DD.md
3. Fetch weather for Austin 78721
4. Generate report combining:
   - Recent conversation highlights
   - Memory file summaries
   - Current conditions
5. Send via Telegram + email"

Cost: ~$0.03 with Haiku (10x cheaper than Sonnet main session) Context: Full awareness of overnight work

Pattern Overview

1. Identify Main Session Key

# List sessions to find main
sessions_list(limit=10)
# Typical main session key format:
# agent:main:telegram:direct:{userId}
# agent:main:main

2. Query History from Isolated Session

// In cron job, sub-agent, or event handler:
sessions_history({
  sessionKey: "agent:main:telegram:direct:8264585335",
  limit: 50  // Last 50 messages
})

Returns conversation history even though you're in an isolated session.

3. Use Context + Execute Task

Your isolated session now has:

  • ✅ Conversation history (what was discussed)
  • ✅ Memory files (persistent notes)
  • ✅ Cheap model (Haiku)
  • ✅ Full tool access

Use Cases

Cron Jobs with Context

Morning reports:

Schedule: 8 AM daily
Model: Haiku (~$0.03/run)
Task: Read overnight work, check email, send summary
Context: Last 50 messages from main session

End-of-day summaries:

Schedule: 9 PM daily
Model: Haiku
Task: What got done today? What's pending?
Context: Today's full conversation

Periodic check-ins:

Schedule: Every 2 hours (9 AM - 9 PM)
Model: Haiku
Task: Anything urgent in email/calendar?
Context: Recent discussion about priorities

Sub-Agent Delegation

Background builds:

sessions_spawn({
  task: "Build the AREF product page based on our discussion",
  model: "haiku",
  // In the task prompt:
  // "First, query main session history to see our conversation about AREF requirements..."
})

Research tasks:

sessions_spawn({
  task: "Research Unreal Engine integration patterns. Reference our earlier discussion about AREF goals.",
  model: "haiku"
})

Event-Driven Handlers

Webhook arrives → isolated session handles it:

// Webhook payload triggers isolated session
// Session logic:
"1. Query main session to see: what did J and I agree about this client?
2. Process webhook based on that context
3. Take action or notify"

Cost Comparison

ApproachModelContextCost/RunWhen to Use
Main sessionSonnetFull~$0.30Complex interactive work
Isolated (blind)HaikuNone~$0.03Simple scheduled tasks
Context-aware delegationHaikuFull~$0.03Background tasks needing context

Savings: ~10x cheaper than main session, with same context awareness.

Implementation Tips

Finding Your Main Session Key

sessions_list({ kinds: ["main"], limit: 5 })
// Or:
sessions_list({ limit: 10 })
// Look for: agent:main:telegram:direct:{yourUserId}

How Much History?

  • 10 messages: Just recent context (~2KB)
  • 50 messages: Last few hours of work (~10KB)
  • 100 messages: Full day or multi-session context (~20KB)

Start with 50, adjust based on needs.

Combining History + Memory

Best results come from:

  1. Sessions history: Recent interactive work
  2. Memory files: Persistent decisions/notes
"1. sessions_history(limit=30) → what we discussed today
2. read memory/2026-02-13.md → decisions logged
3. Combine both sources for complete picture"

Morning Report Recipe

Complete example for daily morning report:

Cron Job Setup:

{
  schedule: { kind: "cron", expr: "0 8 * * *", tz: "America/Chicago" },
  sessionTarget: "isolated",
  payload: {
    kind: "agentTurn",
    model: "haiku",
    message: `Generate morning report:

1. Query main session: sessions_history('agent:main:telegram:direct:8264585335', limit=50)
2. Read yesterday's memory: memory/YYYY-MM-DD.md
3. Get weather: Austin 78721
4. Check email (gog or himalaya)
5. Check calendar events for today

Report format:
📍 WEATHER: [conditions]
🌙 OVERNIGHT: [from session history - what we worked on]
📝 PERSISTENT NOTES: [from memory file]
📧 EMAIL: [urgent only]
📅 CALENDAR: [today's events]
🔗 DASHBOARD: [mission control link]

Send to Telegram using message tool.

Note: Email delivery from isolated sessions requires SMTP credentials or is better handled via main session heartbeats for reliability.`
  },
  delivery: { mode: "announce", to: "8264585335", channel: "telegram" }
}

Cost: ~$0.03/report (~$1/month) Context: Full overnight work awareness Timing: Exact (8 AM every day)

Limitations

History truncation:

  • sessions_history returns limited content (typically last N messages)
  • Very long messages may be truncated
  • For deep archives, rely on memory files

Main session must exist:

  • If main session is brand new (no messages), history is empty
  • Isolated sessions can't create main session history, only read it

Not real-time:

  • History reflects state when queried
  • If main session is actively running, very latest messages might not appear immediately

Best Practices

1. Write good memory summaries Even with session history access, persistent memory files are gold. Don't rely solely on conversation history.

2. Query only what you need limit=10 for quick context, limit=50 for substantial work, limit=100 for deep dives.

3. Chain tools effectively

sessions_history → memory_get → web_search → message

Context first, then action.

4. Use Haiku for delegation, Sonnet for decisions

  • Isolated background work: Haiku
  • Interactive problem-solving: Sonnet
  • Morning reports/summaries: Haiku
  • Architecture discussions: Sonnet

Troubleshooting

"Empty session history"

  • Check session key is correct: sessions_list()
  • Main session might be new (no messages yet)
  • Use limit parameter

"Content truncated"

  • Reduce limit (fewer messages = more complete content)
  • Rely on memory files for archival data

"Isolated session can't send messages"

  • Use message tool, not sessions_send
  • Ensure delivery.mode is set in cron config OR use message tool directly

Related Patterns

  • Heartbeats: Main session periodic checks (full context, main model)
  • Sub-agents: Long-running background tasks
  • Cron jobs: Scheduled isolated work
  • Memory files: Persistent cross-session storage

Credits

Discovered by RGBA Research during OpenClaw optimization work. Published to ClawHub as open pattern for the community.

Contact: https://rgbaresearch.com License: MIT (free to use, adapt, share)

平台分布

OpenClaw

89.7%
按下载量换算8,161

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

未展示

权限和风险

接触敏感数据

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。目前只有一个公开来源,建议同时核对源仓库。

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