Prose is a programming language for long-running AI sessions, enabling Turing-complete computation. It allows operations teams to automate complex workflows, integrate with various tools, and build custom applications. Ideal for developers and operations professionals.
git clone https://github.com/openprose/prose.githttps://prose.md
["Install Prose CLI: `npm install -g prose-cli`","Create a new script file: `prose new server_monitor.prose`","Edit the script to include your specific logic using the Prose syntax (import tools, define functions, set loops)","Test locally: `prose run server_monitor.prose --dry-run`","Deploy to production: `prose deploy server_monitor.prose --schedule \"*/5 * * * *\"`","Monitor execution: `prose logs server_monitor`\n\nTips:\n- Start with simple scripts and gradually add complexity\n- Use Prose's built-in logging for debugging\n- Leverage existing libraries (http, json, os) before writing custom code\n- Set appropriate timeouts for long-running operations","For complex workflows, break them into smaller Prose functions and compose them together"]
Automate research tasks by querying multiple sources and compiling findings into a structured report.
Streamline content creation by integrating AI writing agents that refine drafts based on research data.
Facilitate competitive analysis by running parallel sessions that gather and analyze market data.
Implement error handling and retry mechanisms in workflows to ensure robustness in automation tasks.
No install command available. Check the GitHub repository for manual installation instructions.
git clone https://github.com/openprose/proseCopy the install command above and run it in your terminal.
Launch Claude Code, Cursor, or your preferred AI coding agent.
Use the prompt template or examples below to test the skill.
Adapt the skill to your specific use case and workflow.
Write a Prose script to [TASK]. Use [TOOLS/APIS] for [FUNCTIONALITY]. Include error handling for [SPECIFIC_ERRORS]. The script should run for [DURATION] and output results to [OUTPUT_DESTINATION].
```prose
// Prose script to monitor server health and auto-scale based on CPU load
// Runs every 5 minutes for 24 hours, logs to /var/log/server_monitor.log
import http, json, os
const MAX_CPU = 80
const MIN_CPU = 30
const SCALING_FACTOR = 0.5
fn check_cpu() {
let response = http.get("http://localhost:9100/metrics")
if response.status != 200 {
log.error("Failed to fetch metrics")
return null
}
let metrics = json.parse(response.body)
return metrics.cpu_usage
}
fn scale_servers(target_cpu) {
let current = check_cpu()
if current == null return
if target_cpu > MAX_CPU {
let new_servers = Math.ceil((target_cpu - MAX_CPU) / SCALING_FACTOR)
log.info(`Scaling up by ${new_servers} servers`)
os.system(`aws ec2 run-instances --image-id ami-123456 --count ${new_servers} --instance-type t3.medium`)
} else if target_cpu < MIN_CPU {
let remove_servers = Math.ceil((MIN_CPU - target_cpu) / SCALING_FACTOR)
log.info(`Scaling down by ${remove_servers} servers`)
os.system(`aws ec2 terminate-instances --instance-ids $(aws ec2 describe-instances --filters "Name=tag:Name,Values=web-server" --query "Reservations[].Instances[].InstanceId" --output text | head -${remove_servers})`)
}
}
for 24 hours every 5 minutes {
let cpu = check_cpu()
if cpu != null {
scale_servers(cpu)
log.info(`CPU at ${cpu}%, current servers: ${os.system("aws ec2 describe-instances --filters 'Name=tag:Name,Values=web-server' --query 'length(Reservations[].Instances[])'").trim()}`)
}
}
```
Output:
2024-05-15 14:05:00 INFO: CPU at 85%, current servers: 12
2024-05-15 14:05:00 INFO: Scaling up by 7 servers
2024-05-15 14:10:00 INFO: CPU at 72%, current servers: 19
2024-05-15 14:15:00 INFO: CPU at 68%, current servers: 19
... [continues for 24 hours] ...
2024-05-16 13:55:00 INFO: CPU at 28%, current servers: 5
2024-05-16 14:00:00 INFO: Scaling down by 2 serversYour one-stop shop for church and ministry supplies.
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