sidebar_position: 11 title: “Automate Anything with Cron” description: “Real-world automation patterns using Hermes cron — monitoring, reports, pipelines, and multi-skill workflows”
Automate Anything with Cron
The daily briefing bot tutorial covers the basics. This guide goes further — five real-world automation patterns you can adapt for your own workflows.
- For the full feature reference, see Scheduled Tasks (Cron).
-
::info Key Concept Cron jobs run in fresh agent sessions with no memory of your current chat. Prompts must be completely self-contained — include everything the agent needs to know.
-
::
Pattern 1: Website Change Monitor
Watch a URL for changes and get notified only when something is different.
The script parameter is the secret weapon here. A Python script runs before each execution, and its stdout becomes context for the agent. The script handles the mechanical work (fetching, diffing); the agent handles the reasoning (is this change interesting?).
Create the monitoring script:
mkdir -p ~/.hermes/scripts
import hashlib, json, os, urllib.request
URL = "https://example.com/pricing"
STATE_FILE = os.path.expanduser("~/.hermes/scripts/.watch-site-state.json")
# Fetch current content
req = urllib.request.Request(URL, headers={"User-Agent": "Hermes-Monitor/1.0"})
content = urllib.request.urlopen(req, timeout=30).read().decode()
current_hash = hashlib.sha256(content.encode()).hexdigest()
# Load previous state
prev_hash = None
if os.path.exists(STATE_FILE):
with open(STATE_FILE) as f:
prev_hash = json.load(f).get("hash")
# Save current state
with open(STATE_FILE, "w") as f:
json.dump({"hash": current_hash, "url": URL}, f)
# Output for the agent
if prev_hash and prev_hash != current_hash:
print(f"CHANGE DETECTED on {URL}")
print(f"Previous hash: {prev_hash}")
print(f"Current hash: {current_hash}")
print(f"\nCurrent content (first 2000 chars):\n{content[:2000]}")
else:
print("NO_CHANGE")
Set up the cron job:
/cron add "every 1h" "If the script output says CHANGE DETECTED, summarize what changed on the page and why it might matter. If it says NO_CHANGE, respond with just [SILENT]." --script ~/.hermes/scripts/watch-site.py --name "Pricing monitor" --deliver telegram
Pattern 2: Weekly Report
Compile information from multiple sources into a formatted summary. This runs once a week and delivers to your home channel.
/cron add "0 9 * * 1" "Generate a weekly report covering:
1. Search the web for the top 5 AI news stories from the past week
2. Search GitHub for trending repositories in the 'machine-learning' topic
3. Check Hacker News for the most discussed AI/ML posts
Format as a clean summary with sections for each source. Include links.
Keep it under 500 words — highlight only what matters." --name "Weekly AI digest" --deliver telegram
From the CLI:
hermes cron create "0 9 * * 1" \
"Generate a weekly report covering the top AI news, trending ML GitHub repos, and most-discussed HN posts. Format with sections, include links, keep under 500 words." \
--name "Weekly AI digest" \
--deliver telegram
The 0 9 * * 1 is a standard cron expression: 9:00 AM every Monday.
Pattern 3: GitHub Repository Watcher
Monitor a repository for new issues, PRs, or releases.
/cron add "every 6h" "Check the GitHub repository NousResearch/hermes-agent for:
- New issues opened in the last 6 hours
- New PRs opened or merged in the last 6 hours
- Any new releases
Use the terminal to run gh commands:
gh issue list --repo NousResearch/hermes-agent --state open --json number,title,author,createdAt --limit 10
gh pr list --repo NousResearch/hermes-agent --state all --json number,title,author,createdAt,mergedAt --limit 10
Filter to only items from the last 6 hours. If nothing new, respond with [SILENT].
Otherwise, provide a concise summary of the activity." --name "Repo watcher" --deliver discord
Pattern 4: Data Collection Pipeline
Scrape data at regular intervals, save to files, and detect trends over time. This pattern combines a script (for collection) with the agent (for analysis).
import json, os, urllib.request
from datetime import datetime
DATA_DIR = os.path.expanduser("~/.hermes/data/prices")
os.makedirs(DATA_DIR, exist_ok=True)
# Fetch current data (example: crypto prices)
url = "https://api.coingecko.com/api/v3/simple/price?ids=bitcoin,ethereum&vs_currencies=usd"
data = json.loads(urllib.request.urlopen(url, timeout=30).read())
# Append to history file
entry = {"timestamp": datetime.now().isoformat(), "prices": data}
history_file = os.path.join(DATA_DIR, "history.jsonl")
with open(history_file, "a") as f:
f.write(json.dumps(entry) + "\n")
# Load recent history for analysis
lines = open(history_file).readlines()
recent = [json.loads(l) for l in lines[-24:]] # Last 24 data points
# Output for the agent
print(f"Current: BTC=${data['bitcoin']['usd']}, ETH=${data['ethereum']['usd']}")
print(f"Data points collected: {len(lines)} total, showing last {len(recent)}")
print(f"\nRecent history:")
for r in recent[-6:]:
print(f" {r['timestamp']}: BTC=${r['prices']['bitcoin']['usd']}, ETH=${r['prices']['ethereum']['usd']}")
/cron add "every 1h" "Analyze the price data from the script output. Report:
1. Current prices
2. Trend direction over the last 6 data points (up/down/flat)
3. Any notable movements (>5% change)
If prices are flat and nothing notable, respond with [SILENT].
If there's a significant move, explain what happened." \
--script ~/.hermes/scripts/collect-prices.py \
--name "Price tracker" \
--deliver telegram
The script does the mechanical collection; the agent adds the reasoning layer.
Pattern 5: Multi-Skill Workflow
Chain skills together for complex scheduled tasks. Skills are loaded in order before the prompt executes.
# Use the arxiv skill to find papers, then the obsidian skill to save notes
/cron add "0 8 * * *" "Search arXiv for the 3 most interesting papers on 'language model reasoning' from the past day. For each paper, create an Obsidian note with the title, authors, abstract summary, and key contribution." \
--skill arxiv \
--skill obsidian \
--name "Paper digest"
From the tool directly:
cronjob(
action="create",
skills=["arxiv", "obsidian"],
prompt="Search arXiv for papers on 'language model reasoning' from the past day. Save the top 3 as Obsidian notes.",
schedule="0 8 * * *",
name="Paper digest",
deliver="local"
)
Skills are loaded in order — arxiv first (teaches the agent how to search papers), then obsidian (teaches how to write notes). The prompt ties them together.
Managing Your Jobs
# List all active jobs
/cron list
# Trigger a job immediately (for testing)
/cron run <job_id>
# Pause a job without deleting it
/cron pause <job_id>
# Edit a running job's schedule or prompt
/cron edit <job_id> --schedule "every 4h"
/cron edit <job_id> --prompt "Updated task description"
# Add or remove skills from an existing job
/cron edit <job_id> --skill arxiv --skill obsidian
/cron edit <job_id> --clear-skills
# Remove a job permanently
/cron remove <job_id>
Delivery Targets
The --deliver flag controls where results go:
| Target | Example | Use case |
|---|---|---|
origin | --deliver origin | Same chat that created the job (default) |
local | --deliver local | Save to local file only |
telegram | --deliver telegram | Your Telegram home channel |
discord | --deliver discord | Your Discord home channel |
slack | --deliver slack | Your Slack home channel |
| Specific chat | --deliver telegram:-1001234567890 | A specific Telegram group |
| Threaded | --deliver telegram:-1001234567890:17585 | A specific Telegram topic thread |
Tips
Make prompts self-contained. The agent in a cron job has no memory of your conversations. Include URLs, repo names, format preferences, and delivery instructions directly in the prompt.
Use [SILENT] liberally. For monitoring jobs, always include instructions like “if nothing changed, respond with [SILENT].” This prevents notification noise.
Use scripts for data collection. The script parameter lets a Python script handle the boring parts (HTTP requests, file I/O, state tracking). The agent only sees the script’s stdout and applies reasoning to it. This is cheaper and more reliable than having the agent do the fetching itself.
Test with /cron run. Before waiting for the schedule to trigger, use /cron run <job_id> to execute immediately and verify the output looks right.
Schedule expressions. Human-readable formats like every 2h, 30m, and daily at 9am all work alongside standard cron expressions like 0 9 * * *.
For the complete cron reference — all parameters, edge cases, and internals — see Scheduled Tasks (Cron).