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Agent Skill · 研究检索

sub-agents(分 Agent)

sub-agents 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。

sub-agents 是研究检索类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为操作浏览器;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 15,722。

总安装 7,644周安装 325GitHub Stars 公开资料未说明下载 2,678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
1.0.0

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:sub-agents(分 Agent)
来源仓库:https://github.com/bill492/sub-agents
安装命令:
openclaw skills install sub-agents
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 sub-agents

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。

协调多个子代理并行处理研究、编码与分析任务。适用于复杂项目分工与多模型路由管理场景。动态分配任务并聚合子代理输出结果。sub-agents 属于研究检索类 Skill,可作为该场景下的辅助能力补充。需合理设置子代理数量与超时控制策略。注意资源竞争与结果一致性校验机制。

适用宿主包括 OpenClaw,接入前应确认版本、权限和运行环境要求。

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 sub-agents;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

    它需要操作浏览器,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

sub-agents 怎么安装?

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

sub-agents 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。

sub-agents 需要什么权限?

权限级别是操作浏览器。该 Skill 可能涉及浏览器控制能力,使用时可能读取或操作网页内容,需要在受控环境中确认权限边界。

sub-agents 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。

sub-agents 用的人多吗?

公开数据是累计安装 7,644 次,近一周安装 325 次,下载 2,678 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。

sub-agents 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

  • 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
sub-agents

分 Agent · 本页

7,644—操作浏览器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
sub-agents
description
Spawn and coordinate sub-agent sessions for parallel work. Use when delegating tasks (research, code, analysis), routing to appropriate models, or managing multi-agent workflows. Trigger on "spawn", "sub-agent", "delegate", "parallel tasks", or when a task would benefit from a different model.

Sub-Agent Orchestration

Spawn isolated sessions to execute tasks in parallel with appropriate model routing.

Critical: Sub-Agents Are Context-Blind

Sub-agents do not see AGENTS.md, SKILL.md, SOUL.md, MEMORY.md, or any workspace context files. They only see:

  1. The task string you provide
  2. Whatever files you tell them to read (via paths in the task)
  3. Inline attachments you pass at spawn time

Everything the sub-agent needs must be in the task spec or explicitly referenced as a file path. This includes output instructions, announce behavior, constraints, and domain knowledge.

When to Spawn

Spawn when:

  • Task benefits from a specialized model (code → Codex, research → Sonnet)
  • Work can run in parallel while you continue
  • Task is self-contained with clear success criteria
  • Will block you for >10 seconds (the 10-Second Rule)

Don't spawn when:

  • Trivial one-liner (just do it yourself)
  • Task requires real-time conversation with the user
  • Heavy coordination overhead exceeds benefit

Model Selection

Task TypeModel (alias)Full pathNotes
Browser automationgpt54openai-codex/gpt-5.4Default for all browser tasks. Native computer-use. thinking: high auto-applied.
Code implementationcodexopenai-codex/gpt-5.3-codexOptimized for code gen
Quick code/bugscodexopenai-codex/gpt-5.3-codex-sparkFaster, simpler tasks
Research, writing, quick tasksgpt5openai-codex/gpt-5.2Unlimited on Codex sub. Replaces sonnet/haiku for most work.
Complex reasoningopusanthropic/claude-opus-4-6Deep analysis (expensive)
Huge context (>200K tokens)sonnetanthropic/claude-sonnet-4-51M context window fallback

Use aliases when available. GPT-5.2 is unlimited on the Codex subscription — prefer it over Sonnet/Haiku for sub-agents unless you specifically need Sonnet's 1M context window.

sessions_spawn Parameters

ParameterRequiredDefaultDescription
task✅—The full task description (only context the sub-agent gets)
model—parent modelModel alias or full provider/model path
thinking——`off \low \medium \high`
label——Label for logs/UI tracking
runTimeoutSeconds—config default or 0Abort after N seconds
cleanup—keep`delete \keep` — delete removes session after completion
thread—falseThread-bound routing (Discord/Slack)
mode—run`run \session — defaults to session when thread=true`
sandbox—inherit`inherit \require` — require rejects if child isn't sandboxed
agentId—current agentSpawn under another agent (must be in allowlist)
attachments——Inline files: [{ name, content, encoding?, mimeType? }]

Key behaviors

  • Always non-blocking. Returns { status: "accepted", runId, childSessionKey } immediately.
  • Sub-agents cannot spawn sub-agents. No nested spawning. Plan accordingly.
  • Sub-agents get all tools EXCEPT session tools (no sessions_list/history/send/spawn). Configurable via tools.subagents.tools.
  • Auto-archive: Sessions archive after agents.defaults.subagents.archiveAfterMinutes (default: 60).

Announce Mechanism

After a sub-agent completes, OpenClaw runs an announce step that posts results to the requester's chat channel.

  • Announce replies are normalized to Status / Result / Notes format
  • Status comes from runtime outcome (success/failure/timeout), not model text
  • If the assistant's final reply is empty, the latest toolResult is used as Result
  • Include a stats line (runtime, tokens, sessionKey, cost)

Controlling announce behavior

Sub-agent replies with...Effect
Normal textPosted to requester's channel as the announce
ANNOUNCE_SKIPAnnounce is suppressed — nothing posted
Empty replyLatest toolResult becomes the Result

⚠️ Use ANNOUNCE_SKIP, not NO_REPLY. ANNOUNCE_SKIP is the specific mechanism for sub-agent announce suppression. NO_REPLY is a general silent-reply convention that may not suppress the announce step.

Task Specification — Structured Handoff Protocol

Every spawn should follow this template. Remember: this is the only context the sub-agent receives.

Required Fields

  1. Objective — One sentence. What the sub-agent must accomplish.
  2. Context — Structured facts. File paths, API endpoints, constraints, relevant decisions. Not narrative — a reference sheet.
  3. Inputs — What files/data to read before starting. Be specific: paths, line ranges, sections.
  4. Success criteria — How to verify done. Testable, not subjective.
  5. Output contract — Where and how to deliver results. File path, format, schema.

Optional Fields

  1. Constraints — What NOT to do. Boundaries, things that already failed.
  2. Domain knowledge — Project-specific context files to load (e.g., projects/foo/CONTEXT.md).
  3. Decisions already made — Prevent re-litigating settled questions.

Announce instructions (include in every task)

For silent sub-agents (results consolidated by parent):

Write your full analysis to [path].
Your final reply after writing the file should be ONLY: ANNOUNCE_SKIP

For sub-agents that should announce their own results:

Write results to [path].
Your final reply should summarize what you found — this will be posted to the chat.

Example (Good)

OBJECTIVE: Analyze time-of-day patterns in Kalshi BTC spread bot single-fill losses.

CONTEXT:
- Bot code: projects/kalshi-arb/bot/spread_bot.py
- Trade log: projects/kalshi-arb/data/trades.csv (columns: timestamp, action, side, price, fill_type, pnl)
- Key finding: single-fills have 1.8% win rate vs ~83% for dual-fills
- Volume gate already exists at 150K trailing-1

INPUTS:
- Read trades.csv, filter to fill_type="single"
- Read spread_bot.py lines 180-220 (gating logic)

SUCCESS CRITERIA:
- Statistical breakdown of single-fill losses by hour (ET)
- Identify if specific time windows have >2x the loss rate
- Chi-squared or equivalent significance test

OUTPUT:
- Write analysis to projects/kalshi-arb/time-gating-analysis.md
- Include raw data table + recommendation

CONSTRAINTS:
- Don't modify bot code, analysis only
- Flag if sample size < 30 per bucket

Your final reply after writing the file should be ONLY: ANNOUNCE_SKIP

Example (Bad — Don't Do This)

Look at the Kalshi bot trades and figure out if time of day matters
for single fills. The bot is in the projects folder somewhere.
Write up what you find.

The bad example forces the sub-agent to guess file locations, decide its own success criteria, and has no announce instructions.

Monitoring & Management

subagents tool (primary orchestration)

subagents(action="list")                              // List active sub-agents
subagents(action="steer", target="<id>", message="...") // Send follow-up instructions
subagents(action="kill", target="<id>")               // Kill a running sub-agent

Session tools (for history/cross-session)

sessions_list({ kinds: ["other"], activeMinutes: 60 })  // Sub-agents are kind "other"
sessions_history({ sessionKey: "...", limit: 5 })        // Check output
sessions_send({ sessionKey: "...", message: "..." })     // Send to any session

Discovery

agents_list()  // Discover which agentIds are allowed for sessions_spawn

Don't poll in loops. Check on-demand, when prompted, or for debugging.

Fallback Rules

If a model is rate-limited:

  1. Codex/GPT-5.2 limited → Use sonnet for code/research tasks
  2. Sonnet limited → Use gpt5 for research tasks
  3. All limited → Use opus directly (last resort, expensive)

Rate limits typically reset in 30-90 minutes.

Anti-Patterns

❌ Spawning for trivial tasks — Just do simple things yourself ❌ Vague task specs — "Look into X" without success criteria or output contract ❌ Over-parallelization — Too many concurrent spawns = memory pressure ❌ Forgetting announce instructions — Every task must specify ANNOUNCE_SKIP or what to say ❌ Assuming sub-agents have context — They don't see your workspace files ❌ Using kinds: ["isolated"] — The correct kind for sub-agents is "other" ❌ Using NO_REPLY for announce suppression — Use ANNOUNCE_SKIP ❌ Expecting sub-agents to spawn their own sub-agents — They can't

平台分布

OpenClaw

98.82%
按下载量换算2,646

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

通过

权限和风险

操作浏览器

该 Skill 可能涉及浏览器控制能力,使用时可能读取或操作网页内容,需要在受控环境中确认权限边界。

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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