deep-agents-orchestration

deep-agents-orchestration is a skill for Claude Code, Codex from joonlab/joonlab-claudecode-setting-for-share. It costs 43 tokens per session (3,241 once invoked), scanned A, a copy of deep-agents-orchestration, MIT.

A set of building blocks for Deep Agents, an agent setup for complex, multi-step work. It covers delegating tasks to specialist agents, tracking plans, and pausing for human approval.

In plain words
What is it for?
Use it to create agents that split research or implementation into subtasks, track progress with task lists, and request approval before selected operations.
Why use it?
It helps keep large tasks organized and separates specialist work from the main agent's context. Approval steps can prevent sensitive actions from happening without a person's review.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: mentions subagents.

Good fit Use it to create agents that split research or implementation into subtasks, track progress with task lists, and request approval before selected operations.

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Install with agentmods
npx agentmods add skills/joonlab/joonlab-claudecode-setting-for-share/deep-agents-orchestration
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add joonlab/joonlab-claudecode-setting-for-share --skill deep-agents-orchestration
Clone the repo
git clone --depth 1 https://github.com/joonlab/joonlab-claudecode-setting-for-share

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for deep-agents-orchestration

README.md
[![agentmods](https://agentmods.dev/badge/skills/joonlab/joonlab-claudecode-setting-for-share/deep-agents-orchestration/github.svg)](https://agentmods.dev/skills/joonlab/joonlab-claudecode-setting-for-share/deep-agents-orchestration)
Your own site
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Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for deep-agents-orchestration

Your own site · 80×15
<a href="https://agentmods.dev/skills/joonlab/joonlab-claudecode-setting-for-share/deep-agents-orchestration"><img src="https://agentmods.dev/badge/skills/joonlab/joonlab-claudecode-setting-for-share/deep-agents-orchestration.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 43 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,241 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin 95% copy Near-identical to another mod in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00043 $0.03241
Opus 5 $0.00022 $0.01621
Sonnet 5 $0.00009 $0.00648
Haiku 4.5 $0.00004 $0.00324

Measured 10d ago against content hash 2a68af73c8c0, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

deep-agents-orchestration scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

Origin

This is a copy

95% identical to deep-agents-orchestration — 24 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

claude/skills/deep-agents-orchestration/SKILL.md · 472 lines

How it starts

The opening of the file, as written. The whole thing — 472 lines — stays where its author put it; the contents beside it link to each section on GitHub.

  1. SubAgentMiddleware: Delegate work via task tool to specialized agents
  2. TodoListMiddleware: Plan and track tasks via write_todos tool
  3. HumanInTheLoopMiddleware: Require approval before sensitive operations

All three are automatically included in create_deep_agent().


Subagents (Task Delegation)

Use Subagents When Use Main Agent When
Task needs specialized tools General-purpose tools sufficient
Want to isolate complex work Single-step operation
Need clean context for main agent Context bloat acceptable

Default subagent: "general-purpose" - automatically available with same tools/config as main agent.

@tool def search_papers(query: str) -> str: """Search academic papers.""" return f"Found 10 papers about {query}"

agent = create_deep_agent( subagents=[ { "name": "researcher", "description": "Conduct web research and compile findings", "system_prompt": "Search thoroughly, return concise summary", "tools": [search_papers], } ] )

Main agent delegates: task(agent="researcher", instruction="Research AI trends")

</python>
<typescript>
Create a custom "researcher" subagent with specialized tools for academic paper search.
```typescript
import { createDeepAgent } from "deepagents";
import { tool } from "@langchain/core/tools";
import { z } from "zod";

const searchPapers = tool(
  async ({ query }) => `Found 10 papers about ${query}`,
  { name: "search_papers", description: "Search papers", schema: z.object({ query: z.string() }) }
);

const agent = await createDeepAgent({
  subagents: [
    {
      name: "researcher",
      description: "Conduct web research and compile findings",
      systemPrompt: "Search thoroughly, return concise summary",
      tools: [searchPapers],
    }
  ]
});

// Main agent delegates: task(agent="researcher", instruction="Research AI trends")

agent = create_deep_agent( subagents=[ { "name": "code-deployer", "description": "Deploy code to production", "system_prompt": "You deploy code after tests pass.", "tools": [run_tests, deploy_to_prod], "interrupt_on": {"deploy_to_prod": True}, # Require approval } ], checkpointer=MemorySaver() # Required for interrupts )

</python>
</ex-subagent-with-hitl>

<fix-subagents-are-stateless>
<python>
Subagents are stateless - provide complete instructions in a single call.
```python
# WRONG: Subagents don't remember previous calls
# task(agent='research', instruction='Find data')
# task(agent='research', instruction='What did you find?')  # Starts fresh!

# CORRECT: Complete instructions upfront
# task(agent='research', instruction='Find data on AI, save to /research/, return summary')

// CORRECT: Complete instructions upfront // task research: Find data on AI, save to /research/, return summary

</typescript>
</fix-subagents-are-stateless>

Read the full file on GitHub · 472 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 10d ago First seen · 472 lines · 43 tokens per session scan A 2a68af73c8c0

Subscribe to this mod's changes

deep-agents-orchestration is a skill published in the GitHub repository joonlab/joonlab-claudecode-setting-for-share (10 stars, last pushed 1mo ago), licensed MIT. It adds 43 tokens to every session and 3,241 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 95% identical to deep-agents-orchestration, differing in 24 lines, and is treated as a copy.

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