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 bdiasti/maestro-bundle-cli --skill deep-agent-hitlgit clone --depth 1 https://github.com/bdiasti/maestro-bundle-cliWrote 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/bdiasti/maestro-bundle-cli/deep-agent-hitl)<a href="https://agentmods.dev/skills/bdiasti/maestro-bundle-cli/deep-agent-hitl"><img src="https://agentmods.dev/badge/skills/bdiasti/maestro-bundle-cli/deep-agent-hitl/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/bdiasti/maestro-bundle-cli/deep-agent-hitl"><img src="https://agentmods.dev/badge/skills/bdiasti/maestro-bundle-cli/deep-agent-hitl.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 | $0.00034 | $0.01045 |
| Opus 5.5 | $0.00014 | $0.00418 |
| Sonnet 5.5 | $0.00007 | $0.00209 |
| Haiku 4.5 | $0.00003 | $0.00104 |
Grade A, and why
deep-agent-hitl 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 2mo 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.
How it starts
The opening of the file, as written. The whole thing — 153 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Deep Agent Human-in-the-Loop
Configure approval gates that pause the agent and wait for human approval before executing sensitive operations.
When to Use
- When the agent should ask before deleting files
- When deploys require human approval
- When sending emails/messages needs confirmation
- When any destructive operation needs a gate
Available Operations
- Configure
interrupt_onper tool - Handle approval/rejection flow
- Resume after approval
- Custom approval decisions
Multi-Step Workflow
Step 1: Configure Interrupts
from deepagents import create_deep_agent
from langgraph.checkpoint.memory import MemorySaver
agent = create_deep_agent(
model="anthropic:claude-sonnet-4-6",
tools=[delete_file, read_file, send_email, deploy],
interrupt_on={
"delete_file": True, # Always ask
"read_file": False, # Never ask
"send_email": True, # Always ask
"deploy": True, # Always ask
"write_file": { # Custom decisions
"allowed_decisions": ["approve", "reject", "modify"]
}
},
checkpointer=MemorySaver() # REQUIRED for interrupts
)
Step 2: Run and Handle Interrupts
config = {"configurable": {"thread_id": "session-1"}}
# Agent runs until it hits an interrupt
result = agent.invoke(
{"messages": [{"role": "user", "content": "Delete the old log files"}]},
config=config
)
# Check if agent is waiting for approval
if result.get("__interrupt__"):
interrupt = result["__interrupt__"]
print(f"Agent wants to: {interrupt['tool']} with args: {interrupt['args']}")
# Approve
result = agent.invoke(
{"messages": [{"role": "user", "content": "approved"}]},
config=config
)
# Or reject
# result = agent.invoke(
# {"messages": [{"role": "user", "content": "rejected, don't delete those"}]},
# config=config
# )
Step 3: Build Approval UI (FastAPI)
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.
- 2mo ago First seen · 153 lines · 34 tokens per session scan A 9cc18ba5e4f2
deep-agent-hitl is a skill published in the GitHub repository bdiasti/maestro-bundle-cli (22 stars, last pushed 7mo ago), licensed MIT. It adds 34 tokens to every session and 1,045 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
deep-agent-hitl compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| deep-agent-hitlbdiasti/maestro-bundle-cli | Skill | 22 | 7mo ago | MIT | A | 34 | Claude Code, Codex |
| imagegenopenai/codex | Skill | 135k | today | Apache-2.0 | A | 113 | Codex |
| playwright-climicrosoft/playwright | Skill | 100k | today | Apache-2.0 | A | 19 | Claude Code |
| browser-automationopenclaw/openclaw | Skill | 394k | today | MIT | A | 36 | Claude Code |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 80k | 6d ago | MIT | A | 200 | Claude Code, Codex |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | today | MIT | A | 95 | Claude Code |
| chat-pet-sprite-creationmicrosoft/vscode | Skill | 195k | today | MIT | A | 53 | Claude Code, Codex |