hermes-agent/website/docs/guides/automate-with-cron.md

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11	Automate Anything with Cron	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. :::

:::tip Don't need the LLM? You have two zero-token options.

• Recurring watchdog where the script already produces the exact message (memory alerts, disk alerts, heartbeats): use script-only cron jobs. Same scheduler, no LLM. You can ask Hermes to set one up for you in chat — the cronjob tool knows when to pick no_agent=True and writes the script for you.
• One-shot from a script that's already running (CI step, post-commit hook, deploy script, externally-scheduled monitor): use hermes send to pipe stdout or a file straight to Telegram / Discord / Slack / etc. without setting up a cron entry. :::

---

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

:::tip The [SILENT] Trick When the agent's final response contains [SILENT], delivery is suppressed. This means you only get notified when something actually happens — no spam on quiet hours. :::

---

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

:::warning Self-Contained Prompts Notice how the prompt includes the exact gh commands. The cron agent has no memory of previous runs or your preferences — spell everything out. :::

---

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. Supported formats: relative delays (30m), intervals (every 2h), standard cron expressions (0 9 * * *), and ISO timestamps (2025-06-15T09:00:00). Natural language like daily at 9am is not supported — use 0 9 * * * instead.

---

For the complete cron reference — all parameters, edge cases, and internals — see Scheduled Tasks (Cron).