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- nex-telegram-orchestrator-pattern
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Nex Telegram Orchestrator Pattern
Deploy a Telegram-driven agent orchestrator on Linux with hard safety limits, cost caps, and persistent task memory.
Free
nex-telegram-orchestrator-pattern
Example session with this skill installed
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.
- Read your context and instructions
- Compiled the nex-telegram-orchestrator-pattern
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.
Connects securely to your tools. The creator never sees your data.
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
Works the same in every agent - Claude, Cursor, Codex, Copilot and 20+ more.
- 1
Download the ZIP
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- 2
Unzip into your skills folder
Every agent reads skills from one folder on your machine. Drop the unzipped folder in there.
- 3
Ask your agent to use it
Restart the agent if it was already running. It picks the skill up automatically - no config needed.
Skills folder by agent
Click the path to copy it. Create the folder if it does not exist yet.
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