dot-claude

🥈Silver

dot-claude automates workflows using Claude Code config. Operations teams benefit from streamlined processes. It connects to Python-based tools and workflows, enhancing productivity.

75100Updated 1mo ago

Intermediate30min to implementautomation

Saves ~15 min per use

Quick InstallView Source

git clone https://github.com/PaulRBerg/dot-claude.git

Works with:

Claude

Overview

About This Skill

dot-claude automates workflows using Claude Code config. Operations teams benefit from streamlined processes. It connects to Python-based tools and workflows, enhancing productivity.

How to Use

["Install dot-claude: `pip install dot-claude` and verify with `dot-claude --version`","Create a configuration file (.claude/config.json) with your workflow steps. Example: `{ \"automations\": { \"jira_cleanup\": { \"command\": \"python jira_cleanup.py\", \"schedule\": \"0 3 * * 1\" } } }`","Define your automation task in a Python script (or use existing templates) that connects to your tools (Jira, GitHub, etc.)","Run manually first: `dot-claude run jira_cleanup` to test your automation","For scheduled runs, use cron syntax in your config or trigger via CI/CD pipelines. Monitor execution with `dot-claude logs`"]

Setup & Installation

Quick Install

No install command available. Check the GitHub repository for manual installation instructions.

Alternative Install (Git Clone)

git clone https://github.com/PaulRBerg/dot-claude

Requirements

Claude Code or compatible AI agent
Works with: Claude

Quick Start Guide

Install the Skill

Copy the install command above and run it in your terminal.

Open Your AI Agent

Launch Claude Code, Cursor, or your preferred AI coding agent.

Try It Out

Use the prompt template or examples below to test the skill.

Customize

Adapt the skill to your specific use case and workflow.

Usage Examples

Prompt Template

Use dot-claude to automate [TASK] in my [WORKFLOW/TOOL]. The task involves [SPECIFIC_ACTION]. Configure Claude Code to handle this by [STEP_1] and [STEP_2]. Ensure the output is saved to [OUTPUT_LOCATION] with [FORMAT_REQUIREMENTS].

Example Output

```python
# dot-claude automation for Jira ticket cleanup
# Generated by dot-claude v1.2.3

from jira import JIRA
from datetime import datetime, timedelta

# Connect to Jira API
jira = JIRA(server='https://company.atlassian.net', basic_auth=('email@example.com', 'api-token'))

# Find stale tickets (last updated >30 days ago)
stale_date = datetime.now() - timedelta(days=30)
stale_tickets = jira.search_issues('project=DEV AND updated <= "{}"'.format(stale_date.strftime('%Y-%m-%d')))

# Process each ticket
for ticket in stale_tickets:
    print(f"Processing {ticket.key} - {ticket.fields.summary}")
    
    # Add comment
    jira.add_comment(ticket, f"This ticket hasn't been updated in 30+ days. Please review or close by {datetime.now().strftime('%Y-%m-%d')}.")
    
    # Transition to 'Stale' status if not already
    if ticket.fields.status.name != 'Stale':
        jira.transition_issue(ticket, transition='Stale', fields={'resolution': {'name': 'Unresolved'}})

# Save results to CSV
import csv
with open('stale_tickets_cleanup.csv', 'w', newline='') as csvfile:
    writer = csv.writer(csvfile)
    writer.writerow(['Key', 'Summary', 'Status', 'Last Updated'])
    for ticket in stale_tickets:
        writer.writerow([ticket.key, ticket.fields.summary, ticket.fields.status.name, ticket.fields.updated])

print(f"Processed {len(stale_tickets)} stale tickets. Results saved to stale_tickets_cleanup.csv")
```

**Execution Summary:**
- Processed 47 stale tickets across DEV project
- Added comments to all tickets with 30-day stale warning
- Transitioned 32 tickets to 'Stale' status
- Generated report: stale_tickets_cleanup.csv
- Total execution time: 2 minutes 14 seconds

The automation successfully cleaned up 47 stale tickets that had been sitting idle for over 30 days, helping maintain a clean Jira backlog. The CSV report provides audit trail for compliance purposes.