How AI‑Powered Chaterm Agent Skills Reduce a 20‑Minute Ops Task to 3 Minutes

Background

At 3 am a cloud‑monitoring alert woke the author. Manual investigation with netstat, top, systemctl and log tailing took 20 minutes and revealed a Confluence service failure caused by high load and a stuck gzip backup process.

Chaterm Overview

Chaterm is an open‑source AI‑enhanced terminal and SSH client for macOS, Windows, Linux, iOS and Android. It integrates Alibaba’s Qwen‑Plus/Turbo models and provides three interaction modes (Chat, Command, Agent). Its core feature for operations is “Skills”, a structured package that lets the AI recognise and execute predefined workflows.

Skill Definition

A Skill is a directory containing:

SKILL.md – a markdown manifest with front‑matter name and description followed by a step‑by‑step workflow.

Executable resources (shell scripts, Python files, etc.).

Reference material (documentation, templates).

Example manifest:

---
name: confluence-health-check
description: Check Confluence service status, ports, CPU, memory, disk and logs
---

# Confluence Health Check

## Workflow
# (list of commands, e.g. systemctl status confluence, netstat -tulpn, top, tail -f /var/log/confluence/*.log)

Skill Lifecycle in Chaterm

Loading phase : On startup Chaterm scans ~/Library/Application Support/Chaterm/skills/, parses each SKILL.md, extracts front‑matter and registers any resource files.

Injection phase : Enabled Skills are injected into the AI system prompt so the model knows which capabilities are available.

Recognition phase : When a user issues a natural‑language request, the model matches the intent to a Skill description.

Execution phase : The model calls the use_skill tool, retrieves the full Skill content and sequentially runs the defined commands via execute_command.

Practical Example – Confluence Health‑Check Skill

Steps to create the Skill:

Plan : name confluence-health-check, description “Check Confluence system status, including service health, ports, CPU, memory, disk and logs”.

Write SKILL.md with the manifest and workflow commands (e.g. systemctl status confluence, netstat -tulpn, top, tail -f /var/log/confluence/*.log).

Create the Skill either via the UI (Settings → Skills → Create) or by creating the directory

~/Library/Application Support/Chaterm/skills/confluence-health-check

and placing SKILL.md inside, then reload Chaterm.

Use : In the chat window type a request such as “检查一下阿里云ECS上的 Confluence 系统状态”. The AI matches the request to the Skill and runs the workflow automatically.

Iterate : Edit the SKILL.md or scripts, reload, and re‑test to optimise performance or add missing steps.

Technical Implementation Details

Skills are stored under the user data directory, for example:

~/Library/Application Support/Chaterm/skills/
├── confluence-health-check/
│   └── SKILL.md
├── log-analyzer/
│   └── SKILL.md
└── mysql-deploy/
    ├── SKILL.md
    ├── scripts/db_init.py
    └── references/workflow.md

During startup Chaterm parses each file, loads the content into an in‑memory map, records the enabled state from a local database, and injects the list of Skills into the AI prompt.

Integration with Qwen Models

Chaterm uses Alibaba’s Qwen‑Plus and Qwen‑Turbo (and optionally Qwen‑3‑Max‑Thinking) for semantic understanding, command generation and agent planning. The AI‑Agent capability enables automated fault‑diagnosis, health‑checks and deployment workflows.

Benefits for Operations

Standardisation : Encapsulates repeatable health‑check, deployment and log‑analysis procedures.

Reusability : Write a Skill once and share it across a team.

Knowledge transfer : New members can invoke complex procedures with a single natural‑language request.

Reduced error risk : Best‑practice steps are enforced automatically.

Reference Repositories

Official Skill examples: https://github.com/chaterm/terminal-skills Anthropic Skills spec: https://github.com/anthropics/skills Chaterm source code:

https://github.com/chaterm/Chaterm