turbo-flow-claude

🥈Silver

Advanced Agentic Development Environment Supporting Devpods, Rackspace Spot Instances, Github Codespaces, Google Cloud Shell, and more! Features 600+ AI agents, Claude Flow, SPARC methodology, and automatic context loading! Deploy intelligent multi-agent swarms, coordinate autonomous workflows.

158280Updated 2w ago

Intermediate30min to implementautomation

Saves ~420 min per use

Quick InstallView Source

git clone https://github.com/marcuspat/turbo-flow-claude.git

Works with:

Claude

Overview

About This Skill

How to Use

[{"step":"Define your task and environment. Specify the development environment (e.g., GitHub Codespaces, Google Cloud Shell), the task to automate (e.g., 'deploy a React app with Docker'), and the number of agents needed (e.g., '3: frontend-builder, backend-deployer, test-runner').","tip":"Use the turbo-flow-claude CLI to list available environments: `turbo-flow-claude list-envs`. For complex tasks, start with 3-5 agents and scale up."},{"step":"Configure agent roles and tools. Assign each agent a specific role (e.g., 'code-reviewer', 'test-automator') and specify the tools they should use (e.g., 'eslint', 'pytest', 'terraform'). Load context from your repository or documentation.","tip":"Use the SPARC methodology as a template: Setup (initialize agents), Planning (define workflow), Action (execute tasks), Review (validate results), Completion (generate outputs)."},{"step":"Deploy the swarm and monitor progress. Use the turbo-flow-claude dashboard to track agent interactions, logs, and outputs. Set up alerts for critical errors (e.g., failed health checks, security vulnerabilities).","tip":"For real-time monitoring, integrate with tools like Prometheus (metrics), Grafana (visualization), or Slack (notifications). Example: `turbo-flow-claude monitor --dashboard grafana`."},{"step":"Review results and iterate. Analyze the final report generated by the swarm. Identify bottlenecks, errors, or incomplete tasks. Reassign agents to address issues and redeploy.","tip":"Use the `turbo-flow-claude report` command to export results in JSON or Markdown format. Focus on metrics like runtime, cost, and task completion rate to optimize future deployments."},{"step":"Scale and optimize. Gradually increase the number of agents or complexity of tasks. Experiment with different agent configurations (e.g., adding a 'performance-tester' agent for load testing).","tip":"Benchmark against manual efforts to quantify efficiency gains. For example, compare turbo-flow-claude runtime vs. your team's average deployment time."}]

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/marcuspat/turbo-flow-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

Deploy a multi-agent swarm in [DEVELOPMENT_ENVIRONMENT] to automate [TASK_DESCRIPTION]. Use the turbo-flow-claude framework with [NUMBER_OF_AGENTS] specialized agents (e.g., code-reviewer, test-automator, deployment-coordinator). Configure the agents to follow the SPARC methodology: [SPECIFIC_STEPS]. Ensure all agents load context from [REPOSITORY_OR_DOCS] and use [PREFERRED_TOOLS_OR_SERVICES]. Monitor progress via [MONITORING_TOOL] and provide a final report with [REQUIRED_OUTPUTS].

Example Output

### Turbo-Flow-Claude Multi-Agent Swarm Deployment Report
**Environment**: Google Cloud Shell (GCP)
**Task**: End-to-end deployment of a Python web app with CI/CD pipeline
**Agents Deployed**: 5 (Code-Reviewer, Test-Automator, Security-Scanner, Deployment-Coordinator, Documentation-Generator)

**Agent Configuration**:
- **Code-Reviewer**: Loaded context from `repo/main` and analyzed 47 files for PEP8 compliance. Flagged 3 critical issues (SQL injection risk in `db_utils.py`, missing type hints in `api/handlers.py`).
- **Test-Automator**: Executed 127 unit tests in parallel across 3 Spot Instances (Rackspace). 98% pass rate (2 failures in `test_integration.py` due to race conditions). Auto-generated a JUnit report.
- **Security-Scanner**: Scanned dependencies using `snyk` and identified 7 vulnerabilities (4 high-severity in `requests` and `flask` libraries). Generated a remediation plan with patched versions.
- **Deployment-Coordinator**: Orchestrated a blue-green deployment to `rackspace-devpod-01` using Terraform. Monitored via `Prometheus`; rollback triggered for 2 failed health checks (503 errors in `/health` endpoint).
- **Documentation-Generator**: Updated `README.md` with deployment steps, API endpoints, and troubleshooting guide. Generated Swagger docs for `/api/v1` endpoints.

**SPARC Methodology Execution**:
1. **Setup**: Agents initialized with shared context (GitHub repo, Terraform modules, Dockerfiles).
2. **Planning**: Code-Reviewer and Security-Scanner agents drafted a 3-phase plan (code review → testing → deployment).
3. **Action**: Agents executed tasks in parallel with dependencies (e.g., tests blocked until code review passed).
4. **Review**: Deployment-Coordinator validated rollback and reran tests. Documentation-Generator finalized outputs.
5. **Completion**: All agents logged results to `turbo-flow-claude` dashboard. Final report generated with 92% task completion (8% blocked by unresolved race conditions).

**Next Steps**:
- Fix race conditions in `test_integration.py` (assigned to Code-Reviewer agent).
- Re-run deployment pipeline after patching dependencies (Security-Scanner agent).
- Merge PR #47 with fixes and redeploy.

**Metrics**:
- Total runtime: 18 minutes (vs. 2.5 hours manual effort).
- Cost: $0.42 (Spot Instances) + $0.15 (GCP Cloud Shell).
- Agent collaboration: 12 inter-agent messages, 4 context reloads.