What I Built

I built a fully automated daily campaign‑letter summarizer running on an Azure VM using OpenClaw. Every morning at 9am, my AI agent scans a folder of campaign letters, summarizes them, and writes a clean daily report.

The most interesting part?

This project became my first step toward a "one‑person AI company", a system that runs tasks for me automatically, every day.

How I Used OpenClaw

1. Setting Up the Azure VM

I deployed an Ubuntu VM on Azure to host OpenClaw. This gave me a stable, always‑on environment for scheduled tasks.

2. Installing WSL + Ubuntu

wsl --install

3. Installing OpenClaw

I installed OpenClaw using the official script:

curl "fsSL https://get.openclaw.ai/install.sh | bash

Then launched the TUI:

openclaw tui

This dropped me into the interactive agent environment.

4. Upgrading Ollama to Cloud Pro

My agent now runs on:

ollama/kimi-k2.5:cloud

Then in TUI:

/model ollama/kimi-k2.5:cloud

5. Creating the Skill (Just by Asking)

At first, I tried to follow the docs and create skills manually-writing Python files and placing them under:

~/.openclaw/workspaces/main/skills/

Those never loaded properly in my environment.

The real breakthrough came when I stopped fighting the filesystem and simply asked the agent in chat:

"Create the skill that summarizes campaign letters from a folder."

and showed him the example:

from claw import skill
import os

@skill
def summarize_campaign_letters(folder: str = "~/openclaw/campaign_letters"):
    folder = os.path.expanduser(folder)
    summaries = []

    for filename in os.listdir(folder):
        path = os.path.join(folder, filename)
        if not os.path.isfile(path):
            continue

        with open(path, "r", encoding="utf-8") as f:
            content = f.read()

        summary = skill.llm(
            f"Summarize the following campaign letter in 5 bullet points:\n\n{content}"
        )

        summaries.append(f"## {filename}\n{summary}\n")

    if not summaries:
        return "No campaign letters found."

    return "\n".join(summaries)

Then OpenClaw generated the "skill", and the /skill command just worked:

/skill summarize_campaign_letters "~/openclaw/campaign_letters"

Under the hood, the Python script that actually runs lives at:

~/.npm-global/lib/node_modules/openclaw/skills/summarize-campaign-letters/scripts/summarize.py

6. Creating the Daily Automation (Cron via Chat)

My agent didn't have a /schedule command, so OpenClaw offered to create a cron job instead.

I simply chatted:

Yes, create one.
Daily summary at 9am
run /skill summarize_campaign_letters

OpenClaw generated the cron entry automatically.

Reminder: Gateway is running but in read-only mode. The cron job needs write capability to create jobs.

Now every morning at 9am (HKT), the agent runs:

/skill summarize_campaign_letters

and writes the output into my reports folder.

Demo

Here's how the system behaves:

This can easily be extended to:

But even the basic version already saves me time every morning.

What I Learned

1. Chat‑Driven Creation Beats Manual Files

I tried to manually place Python skills under ~/.openclaw/workspaces/main/skills/ and reload them. That path, plus bootstrap and loading behavior, made things fragile.

Simply describing what I wanted in natural language and letting the agent use its own skill-creator flow was:

• Faster
• More robust
• Less error‑prone

2. Cron Is the Real Scheduler in My Setup

My agent didn't have /schedule, but it did know how to:

• Inspect existing cron jobs
• Propose new ones
• Write them for me

So instead of fighting for a missing command, I let the agent set up cron directly.

3. You Can Build Real Automation with Zero Manual Code

• .claw files
• Schedules
• Skill boilerplate

I just described what I wanted, answered questions, and let OpenClaw wire everything together.

ClawCon Michigan

I didn't attend ClawCon Michigan, but I followed the event online and enjoyed seeing how others used OpenClaw creatively. This challenge pushed me to build something practical and automated, and I'm glad I did.

Love AI!