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sub-agents分 Agent

Agent Skill

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。

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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: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

简介

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。

SKILL.md

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
taskThe full task description (only context the sub-agent gets)
modelparent modelModel alias or full provider/model path
thinking`off \low \medium \high`
labelLabel for logs/UI tracking
runTimeoutSecondsconfig default or 0Abort after N seconds
cleanupkeep`delete \keep` — delete removes session after completion
threadfalseThread-bound routing (Discord/Slack)
moderun`run \session — defaults to session when thread=true`
sandboxinherit`inherit \require` — require rejects if child isn't sandboxed
agentIdcurrent agentSpawn under another agent (must be in allowlist)
attachmentsInline 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_SKIPExpecting sub-agents to spawn their own sub-agents — They can't

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

98.82%
按下载量换算2,646

安全审计

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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。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。

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