mirror of
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Cross-referenced all docs pages against the actual codebase and fixed: Reference docs (cli-commands.md, slash-commands.md, profile-commands.md): - Fix: hermes web -> hermes dashboard (correct subparser name) - Fix: Wrong provider list (removed deepseek, ai-gateway, opencode-zen, opencode-go, alibaba; added gemini) - Fix: Missing tts in hermes setup section choices - Add: Missing --image flag for hermes chat - Add: Missing --component flag for hermes logs - Add: Missing CLI commands: debug, backup, import - Fix: /status incorrectly marked as messaging-only (available everywhere) - Fix: /statusbar moved from Session to Configuration category - Add: Missing slash commands: /fast, /snapshot, /image, /debug - Add: Missing /restart from messaging commands table - Fix: /compress description to match COMMAND_REGISTRY - Add: --no-alias flag to profile create docs Configuration docs (configuration.md, environment-variables.md): - Fix: Vision timeout default 30s -> 120s - Fix: TTS providers missing minimax and mistral - Fix: STT providers missing mistral - Fix: TTS openai base_url shown with wrong default - Fix: Compression config showing stale summary_model/provider/base_url keys (migrated out in config v17) -> target_ratio/protect_last_n Getting-started docs: - Fix: Redundant faster-whisper install (already in voice extra) - Fix: Messaging extra description missing Slack Developer guide: - Fix: architecture.md tool count 48 -> 47, toolset count 40 -> 19 - Fix: run_agent.py line count 9,200 -> 10,700 - Fix: cli.py line count 8,500 -> 10,000 - Fix: main.py line count 5,500 -> 6,000 - Fix: gateway/run.py line count 7,500 -> 9,000 - Fix: Browser tools count 11 -> 10 - Fix: Platform adapter count 15 -> 18 (add wecom_callback, api_server) - Fix: agent-loop.md wrong budget sharing (not shared, independent) - Fix: agent-loop.md non-existent _get_budget_warning() reference - Fix: context-compression-and-caching.md non-existent function name - Fix: toolsets-reference.md safe toolset includes mixture_of_agents (it doesn't) - Fix: toolsets-reference.md hermes-cli tool count 38 -> 36 Guides: - Fix: automate-with-cron.md claims daily at 9am is valid (it's not) - Fix: delegation-patterns.md Max 3 presented as hard cap (configurable) - Fix: sessions.md group thread key format (shared by default, not per-user) - Fix: cron-internals.md job ID format and JSON structure
261 lines
9.5 KiB
Markdown
261 lines
9.5 KiB
Markdown
---
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sidebar_position: 11
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title: "Automate Anything with Cron"
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description: "Real-world automation patterns using Hermes cron — monitoring, reports, pipelines, and multi-skill workflows"
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---
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# Automate Anything with Cron
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The [daily briefing bot tutorial](/docs/guides/daily-briefing-bot) covers the basics. This guide goes further — five real-world automation patterns you can adapt for your own workflows.
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For the full feature reference, see [Scheduled Tasks (Cron)](/docs/user-guide/features/cron).
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:::info Key Concept
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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.
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:::
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---
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## Pattern 1: Website Change Monitor
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Watch a URL for changes and get notified only when something is different.
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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?).
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Create the monitoring script:
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```bash
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mkdir -p ~/.hermes/scripts
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```
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```python title="~/.hermes/scripts/watch-site.py"
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import hashlib, json, os, urllib.request
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URL = "https://example.com/pricing"
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STATE_FILE = os.path.expanduser("~/.hermes/scripts/.watch-site-state.json")
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# Fetch current content
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req = urllib.request.Request(URL, headers={"User-Agent": "Hermes-Monitor/1.0"})
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content = urllib.request.urlopen(req, timeout=30).read().decode()
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current_hash = hashlib.sha256(content.encode()).hexdigest()
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# Load previous state
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prev_hash = None
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if os.path.exists(STATE_FILE):
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with open(STATE_FILE) as f:
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prev_hash = json.load(f).get("hash")
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# Save current state
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with open(STATE_FILE, "w") as f:
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json.dump({"hash": current_hash, "url": URL}, f)
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# Output for the agent
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if prev_hash and prev_hash != current_hash:
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print(f"CHANGE DETECTED on {URL}")
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print(f"Previous hash: {prev_hash}")
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print(f"Current hash: {current_hash}")
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print(f"\nCurrent content (first 2000 chars):\n{content[:2000]}")
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else:
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print("NO_CHANGE")
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```
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Set up the cron job:
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```bash
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/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
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```
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:::tip The [SILENT] Trick
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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.
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:::
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---
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## Pattern 2: Weekly Report
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Compile information from multiple sources into a formatted summary. This runs once a week and delivers to your home channel.
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```bash
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/cron add "0 9 * * 1" "Generate a weekly report covering:
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1. Search the web for the top 5 AI news stories from the past week
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2. Search GitHub for trending repositories in the 'machine-learning' topic
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3. Check Hacker News for the most discussed AI/ML posts
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Format as a clean summary with sections for each source. Include links.
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Keep it under 500 words — highlight only what matters." --name "Weekly AI digest" --deliver telegram
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```
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From the CLI:
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```bash
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hermes cron create "0 9 * * 1" \
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"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." \
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--name "Weekly AI digest" \
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--deliver telegram
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```
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The `0 9 * * 1` is a standard cron expression: 9:00 AM every Monday.
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---
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## Pattern 3: GitHub Repository Watcher
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Monitor a repository for new issues, PRs, or releases.
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```bash
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/cron add "every 6h" "Check the GitHub repository NousResearch/hermes-agent for:
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- New issues opened in the last 6 hours
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- New PRs opened or merged in the last 6 hours
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- Any new releases
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Use the terminal to run gh commands:
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gh issue list --repo NousResearch/hermes-agent --state open --json number,title,author,createdAt --limit 10
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gh pr list --repo NousResearch/hermes-agent --state all --json number,title,author,createdAt,mergedAt --limit 10
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Filter to only items from the last 6 hours. If nothing new, respond with [SILENT].
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Otherwise, provide a concise summary of the activity." --name "Repo watcher" --deliver discord
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```
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:::warning Self-Contained Prompts
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Notice how the prompt includes the exact `gh` commands. The cron agent has no memory of previous runs or your preferences — spell everything out.
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:::
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---
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## Pattern 4: Data Collection Pipeline
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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).
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```python title="~/.hermes/scripts/collect-prices.py"
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import json, os, urllib.request
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from datetime import datetime
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DATA_DIR = os.path.expanduser("~/.hermes/data/prices")
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os.makedirs(DATA_DIR, exist_ok=True)
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# Fetch current data (example: crypto prices)
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url = "https://api.coingecko.com/api/v3/simple/price?ids=bitcoin,ethereum&vs_currencies=usd"
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data = json.loads(urllib.request.urlopen(url, timeout=30).read())
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# Append to history file
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entry = {"timestamp": datetime.now().isoformat(), "prices": data}
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history_file = os.path.join(DATA_DIR, "history.jsonl")
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with open(history_file, "a") as f:
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f.write(json.dumps(entry) + "\n")
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# Load recent history for analysis
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lines = open(history_file).readlines()
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recent = [json.loads(l) for l in lines[-24:]] # Last 24 data points
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# Output for the agent
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print(f"Current: BTC=${data['bitcoin']['usd']}, ETH=${data['ethereum']['usd']}")
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print(f"Data points collected: {len(lines)} total, showing last {len(recent)}")
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print(f"\nRecent history:")
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for r in recent[-6:]:
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print(f" {r['timestamp']}: BTC=${r['prices']['bitcoin']['usd']}, ETH=${r['prices']['ethereum']['usd']}")
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```
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```bash
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/cron add "every 1h" "Analyze the price data from the script output. Report:
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1. Current prices
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2. Trend direction over the last 6 data points (up/down/flat)
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3. Any notable movements (>5% change)
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If prices are flat and nothing notable, respond with [SILENT].
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If there's a significant move, explain what happened." \
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--script ~/.hermes/scripts/collect-prices.py \
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--name "Price tracker" \
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--deliver telegram
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```
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The script does the mechanical collection; the agent adds the reasoning layer.
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---
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## Pattern 5: Multi-Skill Workflow
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Chain skills together for complex scheduled tasks. Skills are loaded in order before the prompt executes.
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```bash
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# Use the arxiv skill to find papers, then the obsidian skill to save notes
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/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." \
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--skill arxiv \
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--skill obsidian \
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--name "Paper digest"
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```
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From the tool directly:
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```python
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cronjob(
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action="create",
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skills=["arxiv", "obsidian"],
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prompt="Search arXiv for papers on 'language model reasoning' from the past day. Save the top 3 as Obsidian notes.",
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schedule="0 8 * * *",
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name="Paper digest",
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deliver="local"
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)
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```
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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.
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---
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## Managing Your Jobs
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```bash
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# List all active jobs
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/cron list
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# Trigger a job immediately (for testing)
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/cron run <job_id>
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# Pause a job without deleting it
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/cron pause <job_id>
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# Edit a running job's schedule or prompt
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/cron edit <job_id> --schedule "every 4h"
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/cron edit <job_id> --prompt "Updated task description"
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# Add or remove skills from an existing job
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/cron edit <job_id> --skill arxiv --skill obsidian
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/cron edit <job_id> --clear-skills
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# Remove a job permanently
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/cron remove <job_id>
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```
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---
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## Delivery Targets
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The `--deliver` flag controls where results go:
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| Target | Example | Use case |
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|--------|---------|----------|
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| `origin` | `--deliver origin` | Same chat that created the job (default) |
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| `local` | `--deliver local` | Save to local file only |
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| `telegram` | `--deliver telegram` | Your Telegram home channel |
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| `discord` | `--deliver discord` | Your Discord home channel |
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| `slack` | `--deliver slack` | Your Slack home channel |
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| Specific chat | `--deliver telegram:-1001234567890` | A specific Telegram group |
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| Threaded | `--deliver telegram:-1001234567890:17585` | A specific Telegram topic thread |
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---
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## Tips
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**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.
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**Use `[SILENT]` liberally.** For monitoring jobs, always include instructions like "if nothing changed, respond with `[SILENT]`." This prevents notification noise.
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**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.
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**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.
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**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.
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---
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*For the complete cron reference — all parameters, edge cases, and internals — see [Scheduled Tasks (Cron)](/docs/user-guide/features/cron).*
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