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    Nex Telegram Orchestrator Pattern

    by Nex AI

    1

    Deploy a Telegram-driven agent orchestrator on Linux with hard safety limits, cost caps, and persistent task memory.

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    See it in action

    You say

    Setup a Telegram bot for my Pi named 'servant' using Qwen. I need it to run shell commands but block access to my .ssh folder and cap spend at 2 dollars a day.

    Your agent does

    Generated orchestrator-servant/ directory.

    • config.toml: Set with forbidden_paths=['~/.ssh', '/etc'] and max_cost_per_day_cents=200.
    • nex-task: CLI deployed for agent memory.
    • servant.service: systemd unit for user 'pi'. Follow the README to add your Telegram ID to .env.

    What you get

    Securely control a home server via Telegram using a private allowlist.Prevent autonomous agent runaway with hard hourly action and daily cost caps.Sandbox shell-reaching agents away from sensitive directories like .ssh or /etc.Maintain agent task state across sessions using a JSON-backed CLI tool.

    About this skill

    The problem

    Running autonomous agents on local hardware often leads to runaway costs, resource exhaustion, or unintended shell access. Most DIY agent setups lack a structured way to maintain state across disconnected Telegram messages or enforce hard safety boundaries on the host system.

    What it does

    • Generates a complete Telegram orchestrator configuration with strict user ID allowlisting.
    • Implements a multi-layer safety cage including command allowlists and forbidden path sandboxing for shell access.
    • Provides a JSON-backed task CLI that allows agents to track, snooze, and recur tasks across different conversations.
    • Configures hard resource limits for CPU, memory, and subprocesses alongside daily cost and hourly action ceilings.
    • Builds a systemd unit for non-root execution, ensuring the agent remains persistent after reboots.

    Frameworks & tools

    Raspberry Pi (Raspbian), Linux, systemd, Telegram Bot API, OpenAI-compatible APIs (Qwen, Claude, Local LLMs), and TOML configuration.

    Why this beats prompting it yourself

    Manually prompting for safety often results in soft "system instructions" that an LLM can bypass or ignore. This skill generates a structural security layer at the OS and orchestrator level that enforces limits regardless of the model's reasoning output.

    Use cases

    • Control a headless Raspberry Pi via Telegram with a private, authenticated bot.
    • Run autonomous agents overnight with a guaranteed $5.00 daily spend limit.
    • Sandbox an agent so it can run shell scripts without accessing SSH keys or /etc.
    • Give a stateless LLM a persistent "memory" of tasks using a CLI-based task manager.

    Known limitations

    This skill does not harden the underlying host OS or configure firewalls. It is designed for single-operator use cases and is not suitable for public-facing multi-user bots.

    How to install

    Drop the file into your AI Agent. Works with Claude, Cursor, ChatGPT, and 20+ more.

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    Creator

    Nex AI
    Nex AI

    300 skills on Agensi

    Founder of Nex AI. I build production-grade Claude Skills from systems that actually run: multi-tenant SaaS, Telegram agents, Raspberry Pi infrastructure, 3D multiplayer rooms. Every skill ships battle-tested patterns, not theory. 33+ open source skills published, commercial catalog growing.

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