Cross-Agent Workspace Review

Rule

Conduct mutual workspace reviews between agents to identify improvement opportunities and share proven patterns.

Context

When agents with different specializations (technical vs strategic, infrastructure vs domain-specific) can learn from each other's approaches.

Detection

Observable signals for valuable cross-agent review:

• Another agent has demonstrated superior patterns in their domain
• Your workspace has grown organically without systematic review
• Opportunity for knowledge sharing between complementary agents
• Need to identify upstream contribution opportunities

Pattern

Structured mutual review process:

# 1. Request access to other agent's workspace
# (Maintainer can grant cross-repo access)

# 2. Systematic workspace analysis
# Focus areas:
# - Configuration and automation patterns
# - Knowledge organization strategies
# - Lesson architecture and categorization
# - Tool and script ecosystems
# - Context efficiency approaches

# 3. Document findings in structured format:
# - What's exceptional (patterns worth adopting)
# - Areas for improvement (specific suggestions)
# - Upstream contribution opportunities
# - Implementation priorities

# 4. Share findings via GitHub issues for discussion
# 5. Implement agreed improvements through proper PR process

Specific Review Areas

Configuration Analysis:

• gptme.toml sophistication and pattern usage
• Context loading strategies (static vs dynamic)
• Lesson system organization and keyword matching

Knowledge Organization:

• Directory structure and categorization
• Documentation patterns and accessibility
• Cross-referencing and navigation approaches

Infrastructure Maturity:

• Script ecosystems and automation level
• Health monitoring and status tracking
• Tool integration and workflow optimization

Outcome

Following this pattern results in:

• Cross-pollination: Best practices spread between agents
• Rapid improvement: Learn from proven patterns instead of reinventing
• Upstream contributions: Identify shared utilities for gptme-contrib
• Specialization benefits: Leverage different agents' domain expertise
• Quality improvements: External perspective identifies blind spots

Example Success Metrics

From cross-agent collaboration:

• Agent A improved context efficiency with dynamic loading patterns
• Agent B identified new failure mode patterns from Agent A's work
• Both agents identified upstream contribution opportunities
• Infrastructure improvements validated through real-world usage

Related

• Inter-Agent Communication - Communication protocols
• Git Workflow - PR and review processes

Origin

2025-12-11: Extracted from successful cross-agent workspace review collaboration, demonstrating value of systematic cross-agent knowledge sharing.