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 agentmods add skills/bdiasti/maestro-bundle-cli/deep-agent-clinpx skills add bdiasti/maestro-bundle-cli --skill deep-agent-cligit clone --depth 1 https://github.com/bdiasti/maestro-bundle-cliWhat 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 | $0.00039 | $0.00984 |
| Opus 5 | $0.00019 | $0.00492 |
| Sonnet 5 | $0.00008 | $0.00197 |
| Haiku 4.5 | $0.00004 | $0.00098 |
Grade A, and why
deep-agent-cli 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 2d 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 — 159 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Deep Agents CLI
Build a terminal-based coding agent similar to Claude Code using the Deep Agents CLI or by creating your own CLI interface.
When to Use
- When you want a Claude Code-like experience in the terminal
- When building a CLI tool for AI-assisted coding
- When running Deep Agents interactively
Available Operations
- Install and use the official Deep Agents CLI
- Build a custom CLI with rich terminal UI
- Configure CLI with local file access + shell
- Add skills and AGENTS.md to CLI agent
Multi-Step Workflow
Step 1: Install Deep Agents CLI
pip install deepagents
# or
uv tool install deepagents
Step 2: Run the CLI
# Start interactive session
deepagents
# Start with a specific model
deepagents --model anthropic:claude-sonnet-4-6
# Start in a specific directory
deepagents --dir ./my-project
# Start with a prompt
deepagents "Fix the failing tests in src/auth/"
Step 3: Build Custom CLI
# cli.py
import asyncio
import sys
from deepagents import create_deep_agent
from deepagents.backends import LocalShellBackend
from langgraph.checkpoint.memory import MemorySaver
async def main():
# Full coding agent with file + shell access
agent = create_deep_agent(
model="anthropic:claude-sonnet-4-6",
backend=LocalShellBackend(root_dir="."),
skills=["./skills/"],
memory=["/AGENTS.md"],
checkpointer=MemorySaver(),
system_prompt="You are a coding assistant. Follow the project conventions."
)
config = {"configurable": {"thread_id": "cli-session"}}
# One-shot mode
if len(sys.argv) > 1:
prompt = " ".join(sys.argv[1:])
async for event in agent.astream_events(
{"messages": [{"role": "user", "content": prompt}]},
config=config, version="v2"
):
if event["event"] == "on_chat_model_stream":
print(event["data"]["chunk"].content, end="", flush=True)
print()
return
# Interactive mode
print("Coding Agent (type 'exit' to quit)\n")
while True:
try:
user_input = input("> ")
except (EOFError, KeyboardInterrupt):
break
if user_input.lower() in ("exit", "quit"):
break
async for event in agent.astream_events(
{"messages": [{"role": "user", "content": user_input}]},
config=config, version="v2"
):
if event["event"] == "on_chat_model_stream":
print(event["data"]["chunk"].content, end="", flush=True)
print("\n")
asyncio.run(main())
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.
- 2d ago First seen · 159 lines · 39 tokens per session scan A 2f0fa374b49a
deep-agent-cli is a skill published in the GitHub repository bdiasti/maestro-bundle-cli (21 stars, last pushed 5mo ago), licensed MIT. It adds 39 tokens to every session and 984 once invoked, about $0.0002 per session on Opus 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.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
agent-host-chat-contributions
Build and review cross-cutting agent-host chat behavior through lifecycle contributions. Use when adding turn lifecycle side effects, prompt or context injection, restored-history transformation, protocol-action observation, or when reviewing changes that add code to AgentSideEffects or AgentService.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.