360 Secure SkillHub
360 安全技能中心

sub-agents

核心功能:创建独立子代理会话,并行执行任务,可按任务类型路由至专属模型(如代码用Codex、浏览器任务用gpt54),子代理仅获取任务明确提供的信息。典型场景:需专属模型的任务、可并行处理的工作、耗时超10秒的独立任务。输入需含完整任务规格,可选指定模型等参数;输出为任务结果,支持公告控制,可通过工具监控、操控子代理。

bill492
bill492
热度 57.23
最近更新 2026-05-11 17:33:05
分类:自动化依赖:MCP 服务 / 内部调用大模型应用场景:自动化脚本 / 工作流 / 任务管理

安装技能

复制下面的话术发给你的 Agent,它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill: npx secure-skills-cli add sub-agents
Metadata
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 Type Model (alias) Full path Notes
Browser automation gpt54 openai-codex/gpt-5.4 Default for all browser tasks. Native computer-use. thinking: high auto-applied.
Code implementation codex openai-codex/gpt-5.3-codex Optimized for code gen
Quick code/bugs codex openai-codex/gpt-5.3-codex-spark Faster, simpler tasks
Research, writing, quick tasks gpt5 openai-codex/gpt-5.2 Unlimited on Codex sub. Replaces sonnet/haiku for most work.
Complex reasoning opus anthropic/claude-opus-4-6 Deep analysis (expensive)
Huge context (>200K tokens) sonnet anthropic/claude-sonnet-4-5 1M 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

Parameter Required Default Description
task ✅ — The full task description (only context the sub-agent gets)
model — parent model Model alias or full provider/model path
thinking — — off | low | medium | high
label — — Label for logs/UI tracking
runTimeoutSeconds — config default or 0 Abort after N seconds
cleanup — keep delete | keep — delete removes session after completion
thread — false Thread-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 agent Spawn 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 text Posted to requester's channel as the announce
ANNOUNCE_SKIP Announce is suppressed — nothing posted
Empty reply Latest 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