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.
npx skills add joonlab/joonlab-claudecode-setting-for-share --skill deep-agents-orchestrationgit clone --depth 1 https://github.com/joonlab/joonlab-claudecode-setting-for-shareWrote 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.
[](https://agentmods.dev/skills/joonlab/joonlab-claudecode-setting-for-share/deep-agents-orchestration)<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/github.svg" alt="Measured on agentmods" height="20"></a>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.
<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>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.
| Model | Per session | Once 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 |
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.
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.
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.
- SubAgentMiddleware: Delegate work via
tasktool to specialized agents - TodoListMiddleware: Plan and track tasks via
write_todostool - 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>
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.
- 10d ago First seen · 472 lines · 43 tokens per session scan A 2a68af73c8c0
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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