boss-skill

🥈Silver

Boss Skill automates the entire research and development pipeline from requirement to deployment. It coordinates multiple AI agents to streamline workflows for operations teams. The skill integrates with Claude Code, Cursor, and Trae to enhance productivity and reduce manual effort.

31070Updated 2w ago

Intermediate30min to implementautomation

Saves ~480 min per use

Quick InstallView Source

git clone https://github.com/echoVic/boss-skill.git

Works with:

Claude

Overview

About This Skill

How to Use

1. **Define the Scope**: List the 3-5 key agents needed for your project (e.g., Requirements, Design, Development, QA, Deployment). Specify their roles and tools (Claude Code, Cursor, Trae, etc.). 2. **Set Up Integrations**: Configure API keys/tokens for your logging platform (e.g., Slack, Discord, Jira) and ensure agents have access to your repo (GitHub/GitLab) and cloud environment (AWS/GCP). 3. **Trigger the Pipeline**: Paste the prompt template into your AI tool, replacing placeholders with your project details. Start with a clear initial input (e.g., project brief or requirements doc). 4. **Monitor and Iterate**: Check the logging platform for agent updates. Use the output to refine the workflow—e.g., adjust agent roles or add new steps based on bottlenecks. 5. **Scale**: Once the pipeline runs smoothly, save the prompt as a template in your AI tool's library for reuse. Add conditional triggers (e.g., 'Run when a new GitHub issue is labeled `high-priority`).

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/echoVic/boss-skill

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

Act as a Boss Skill automation orchestrator. Use [COMPANY_NAME]'s [TEAM_NAME] workflow to automate the R&D pipeline for [PROJECT_NAME]. Coordinate between the following AI agents: 1) [AGENT_1_ROLE] for [TASK_1], 2) [AGENT_2_ROLE] for [TASK_2], and 3) [AGENT_3_ROLE] for [TASK_3]. Ensure seamless handoffs between agents and log all actions in [LOGGING_PLATFORM]. Begin with [INITIAL_INPUT].

Example Output

### Boss Skill Automation Report: Project Phoenix
**Company:** TechNova Solutions | **Team:** Advanced R&D | **Project:** AI-Powered IoT Security Suite

**Agent Coordination Log:**
1. **Requirements Agent** (Claude Code) parsed the initial brief: 'Develop a lightweight encryption module for IoT devices with <100ms latency.' It identified 3 critical gaps in the spec and flagged them for the Product Manager via Slack integration.

2. **Design Agent** (Cursor) generated 5 architecture diagrams in Mermaid format, selecting the most scalable option (Option C) based on the team's preference for microservices. The agent auto-generated a 12-page technical design doc with section headers matching the company's template.

3. **Development Agent** (Trae) initialized the repo with:
   - Pre-configured Dockerfile (Alpine-based for minimal footprint)
   - Rust/Cargo.toml with dependencies: `ring = "0.16"`, `tokio = { version = "1.0", features = ["full"] }`
   - GitHub Actions workflow for CI/CD (test matrix: Linux/Windows/macOS)
   - Auto-generated 90% test coverage (fuzz tests included)

**Deployment Agent** (Claude Code) triggered the release pipeline after the QA Agent (Cursor) approved the build. The agent:
- Tagged the release as `v1.0.0-alpha`
- Deployed to staging environment via Terraform scripts
- Sent a summary to the #engineering channel in Discord with:
  ```
  🚀 Project Phoenix Alpha Deployed
  - Build: 342f5a1
  - Staging URL: https://staging.phoenix.technova.com
  - Rollback command: `terraform apply -var="env=staging" -var="rollback=true"`
  ```

**Metrics:**
- Total automation time: 2 hours 17 minutes (vs. 3.5 days manual)
- Agent handoffs: 12 (all successful)
- Manual intervention required: 0

**Next Steps:**
- The QA Agent has scheduled a 1-hour regression test window for tomorrow at 10 AM.
- The Product Manager received the requirements gaps report and will prioritize them in the next sprint planning.
- The Development Agent has paused and is waiting for feedback on the alpha build.

**Recommendation:** Proceed to beta phase if no critical issues are found in staging. The automation pipeline is now ready for continuous deployment triggers.