Claude Code agents, workflow commands, and skills for development productivity. Used by Weaverse team for automation. Connects to Claude AI for task execution.
git clone https://github.com/Weaverse/.claude.git.agents is a markdown-based configuration system for AI coding assistants like Claude Code that automates common development tasks. It includes 11 workflow commands for GitHub integration (creating issues, pull requests, releases), 4 domain-specific skills for code review and feature planning, and 5 coding convention rules to maintain team standards. The system integrates directly with your AI tool through symlinks or file copying, enabling tasks like picking up issues with full context, auto-formatting code, generating commit messages, and creating release PRs. Teams use it to reduce manual context switching, enforce consistent coding practices, and accelerate development velocity across AI-assisted workflows.
[{"step":"Define the task to automate","action":"Write a clear, specific description of the task you want to automate (e.g., 'Generate a weekly sales report from Shopify and Google Analytics'). Include any data sources, tools, or outputs required.","tip":"Use bullet points to list requirements. For example: 'Data sources: Shopify API, Google Analytics; Output: Markdown report saved to /reports/; Tools: claude run, Python scripts'."},{"step":"Set up the workflow","action":"Create or modify a `.claude` workflow file (e.g., `weekly_report.yml`) in your project directory. Define the steps, tools, and dependencies. Example:\n```yaml\nname: weekly_performance_report\nsteps:\n - name: fetch_data\n command: python scripts/fetch_analytics.py\n - name: generate_report\n command: python scripts/generate_report.py\n - name: notify_team\n command: python scripts/send_slack_notification.py\n```","tip":"Test each step individually before chaining them together. Use `claude run --dry-run` to preview the workflow without executing it."},{"step":"Execute the workflow","action":"Run the workflow using the command `claude run [WORKFLOW_NAME]`. Monitor the output for errors or warnings. For example: `claude run weekly_performance_report`.","tip":"Use the `--verbose` flag to get detailed logs if the workflow fails. Example: `claude run weekly_performance_report --verbose`."},{"step":"Review and iterate","action":"Check the outputs and logs. If errors occur, debug the workflow by running individual steps manually. Update the workflow file to fix issues and re-run.","tip":"Save successful workflows to a shared directory (e.g., `/workflows/`) so your team can reuse them. Document any dependencies or prerequisites in a README file."},{"step":"Schedule recurring runs (optional)","action":"Use a cron job or CI/CD pipeline to schedule the workflow to run automatically. For example, add this to your `crontab`:\n```\n0 9 * * 1 /usr/local/bin/claude run weekly_performance_report\n```\nThis runs the report every Monday at 9 AM.","tip":"Use `claude run --help` to explore additional options like scheduling, parallel execution, or conditional steps."}]
Automate GitHub issue pickup with branch creation and spec generation
Generate well-formatted commit messages grouped by related file changes
Perform comprehensive code reviews detecting dead code, duplications, and bugs
Create pull requests with AI-generated descriptions and fix review comments
No install command available. Check the GitHub repository for manual installation instructions.
git clone https://github.com/Weaverse/.claudeCopy 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.
Use the .claude skill to automate the following task: [DESCRIBE_TASK]. Execute the workflow using the command 'claude run [WORKFLOW_NAME]' and provide a summary of the steps taken, outputs generated, and any errors encountered. Include the time taken for execution.
``` Task: Automate the generation of a weekly performance report for the Weaverse marketing team. Workflow Executed: claude run weekly_performance_report Execution Summary: 1. **Data Collection**: Fetched analytics from Google Analytics (45,000 sessions, 12% bounce rate) and Shopify (3,200 orders, $185K revenue) for the week of 2024-05-20 to 2024-05-26. 2. **Data Processing**: Cleaned and merged datasets, removing outliers and calculating key metrics (conversion rate: 2.8%, average order value: $57.81). 3. **Report Generation**: Created a Markdown report with: - Executive Summary: Highlighted a 15% increase in revenue YoY. - Visualizations: Generated a line chart for daily sessions and a bar chart for top-performing products (Product A: 450 units sold, Product B: 380 units sold). - Insights: Noted a 22% drop in mobile traffic but a 10% increase in desktop conversions. 4. **Output**: Saved report to `/reports/weekly_performance_2024-05-26.md` and sent a Slack notification to the #marketing-alerts channel with a download link. Errors Encountered: - Shopify API timeout after 3 retries (resolved by increasing timeout limit in the workflow config). - Minor discrepancy in session counts between GA and Shopify (investigated and attributed to cross-domain tracking; no action required). Time Taken: 4 minutes 22 seconds. ```
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