Claude-rio automatically activates custom workflows by matching skills, agents, and commands in Claude Code. Operations teams benefit from improved efficiency and reduced manual intervention. It connects to Claude Code and integrates with existing workflows.
git clone https://github.com/alex-popov-tech/claude-rio.gitclaude-rio solves the problem of inconsistent skill and command activation in Claude Code by adding explicit keyword-based matching. When you submit a prompt, it checks matcher files in your skills, agents, and commands, then suggests relevant tools to Claude using special JSON formatting. While Claude makes the final decision, this visibility significantly increases the probability that your skills, agents, and commands will be invoked when relevant. The system uses deterministic matchers—simple JavaScript functions that evaluate prompt keywords, file patterns, or conversation history—with zero dependencies and fast execution (~10-20ms when no matches). Operations teams benefit from reduced manual intervention and improved workflow efficiency by ensuring their custom tools are properly surfaced to Claude.
[{"step":"Define the workflow requirements","action":"Identify the skills, agents, and commands needed for your automation. For example, if automating a data pipeline, list the validation tools, anomaly detectors, and notification systems you want to include.","tip":"Use claude-rio's `--list-skills` and `--list-agents` flags to see available components. Match them to your workflow's needs."},{"step":"Configure the workflow","action":"Create a configuration file (e.g., `workflow_config.json`) with the required parameters. Include conditions for triggering the workflow, such as time-based schedules or event-based triggers (e.g., file uploads).","tip":"Test configurations in a staging environment first. Use `--dry-run` to validate the workflow without executing it."},{"step":"Activate the workflow","action":"Run claude-rio with the configuration file. For example: `claude-rio --activate --config workflow_config.json`. Monitor the initial execution to ensure all components are triggered correctly.","tip":"Enable logging with `--log-level debug` to capture detailed output for troubleshooting."},{"step":"Monitor and iterate","action":"Review the execution logs and adjust the workflow as needed. Use claude-rio's `--status` flag to check active workflows and their status.","tip":"Set up alerts for failures or anomalies by integrating with tools like PagerDuty or Slack. Use the `--alert-webhook` flag to configure notifications."},{"step":"Scale and optimize","action":"Once validated, deploy the workflow to production. Use claude-rio's `--schedule` flag to automate recurring runs (e.g., `--schedule \"0 2 * * *\"` for daily runs at 2 AM UTC).","tip":"Optimize performance by reviewing the execution time of each component. Replace slower components with more efficient alternatives if needed."}]
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No install command available. Check the GitHub repository for manual installation instructions.
git clone https://github.com/alex-popov-tech/claude-rioCopy 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 claude-rio to automate [WORKFLOW_NAME] by activating the required skills, agents, and commands in Claude Code. Ensure the workflow runs with [SPECIFIC_CONFIGURATION] and triggers [DESIRED_ACTION] when conditions [CONDITIONS] are met. Monitor the output for any errors or deviations.
Claude-rio successfully activated the 'data-pipeline-validation' workflow at 2:15 PM UTC. The workflow matched the following components: 1. **Skills**: Data quality validation (v2.3), anomaly detection (v1.1), and schema compliance (v3.0). 2. **Agents**: Pipeline orchestrator (auto-triggered), error handler (manual override enabled), and notification dispatcher. 3. **Commands**: - `validate_schema --input /data/raw/sales_2024.csv --schema /schemas/sales_v4.json` - `detect_anomalies --threshold 0.05 --output /reports/anomalies_2024-05-15.json` **Execution Log**: - 2:15 PM: Workflow initiated by claude-rio. - 2:16 PM: Schema validation passed (99.8% compliance). - 2:17 PM: Anomaly detection flagged 12 records (0.3% of dataset) for review. - 2:18 PM: Error handler routed anomalies to the QA team via Slack. - 2:19 PM: Notification dispatched to stakeholders: 'Pipeline validation complete. 12 anomalies detected. See /reports/anomalies_2024-05-15.json for details.' **Result**: The workflow reduced manual intervention by 85% compared to the previous manual process, which took an average of 45 minutes per run. No critical errors were logged, and the team was notified within 2 minutes of detection.
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