AI Assistant Workflow Techniques
New prompting techniques from the steipete/agent-rules repository for structured AI assistant workflows and team collaboration
About Agent Rules Integration
We've integrated cutting-edge techniques from the steipete/agent-rules repository, which provides a comprehensive collection of reusable rules and knowledge documents for AI coding assistants like Claude Code and Cursor. These techniques represent the latest in structured AI assistant workflows and team collaboration patterns.
New Techniques Overview
These techniques enable teams to standardize AI assistant behavior, create reusable workflows, and implement sophisticated development automation.
Workflow Template Prompting (.mdc Pattern)
Using structured markdown templates with YAML frontmatter to create reusable, configurable AI assistant workflows that work across different AI platforms.
Use Cases:
- Standardizing AI assistant behavior across teams
- Creating reusable workflow automation
- Cross-platform AI tool compatibility
Example Structure:
---
description: Create well-structured GitHub issues
globs: "*.md"
alwaysApply: false
---
# GitHub Issue Creation
You are tasked with creating well-structured GitHub issues...AI Assistant Rule Systems
Implementing structured rule hierarchies with global and project-specific configurations to guide AI assistant behavior consistently.
Use Cases:
- Enterprise AI assistant deployments
- Consistent behavior across teams
- Quality standards enforcement
Hierarchy Example:
Global Rule: "Always use conventional commit format"
Project Rule: "Include JIRA ticket number"
Result: "feat(auth): add OAuth support [PROJ-123]"Automated Development Workflows
Structured prompting patterns for common development tasks like commits, PR reviews, issue analysis, and code quality checks.
Available Workflows:
- Commit message generation
- Pull request reviews
- Issue analysis and planning
- Code quality assessments
- Changelog management
MCP Server Integration Patterns
Prompting techniques for integrating and orchestrating Model Context Protocol servers to extend AI capabilities with external tools and services.
Key Features:
- Standardized tool integration
- Error handling patterns
- Configuration management
- Service orchestration
GitHub Integration Prompting
Structured approaches for AI assistants to interact with GitHub repositories, issues, PRs, and project management through systematic research and action patterns.
Workflow Steps:
- Research repository conventions
- Analyze similar issues/PRs
- Structure according to templates
- Generate comprehensive descriptions
- Execute GitHub CLI commands
Agent Configuration Management
Systematic approaches to managing AI agent configurations, including global settings, project-specific rules, and environment-specific adaptations.
Configuration Layers:
- Global: ~/.claude/CLAUDE.md
- Project: .cursor/rules/*.mdc
- Task-specific prompts
- Runtime context
Implementation Guide
Learn how to implement these techniques in your development workflow
Set Up Global Configuration
Create a global AI assistant configuration directory and set up base rules that apply across all projects.
mkdir -p ~/.claude
# Add global rules to ~/.claude/CLAUDE.mdCreate Project Rules
Set up project-specific rules using the .mdc format with YAML frontmatter for configuration metadata.
mkdir -p .cursor/rules
# Copy relevant .mdc files from agent-rules repositoryConfigure MCP Servers
Set up Model Context Protocol servers for external tool integration following the documented best practices.
Test and Iterate
Test your configuration with real development tasks and iterate based on team feedback and usage patterns.
Additional Resources
Original Repository
Explore the complete steipete/agent-rules repository with all available rules and documentation.
View Repository →MCP Documentation
Learn about the Model Context Protocol and how to build your own MCP servers.
MCP Docs →Cursor Rules
Understand how to use .mdc files with Cursor for AI-assisted development.
Cursor Docs →