https://github.com/robertheadley/roocode-experiments
https://github.com/robertheadley/roocode-experiments
Last synced: 2 months ago
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- Host: GitHub
- URL: https://github.com/robertheadley/roocode-experiments
- Owner: robertheadley
- License: mit
- Created: 2025-07-19T20:35:44.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2025-07-19T20:56:18.000Z (3 months ago)
- Last Synced: 2025-07-19T23:03:50.763Z (3 months ago)
- Size: 17.6 KB
- Stars: 1
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: readme.md
- License: LICENSE
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README
# Intelligence Enhancement Proposal for Roo Code
## Problem
Roo Code currently has two inefficiencies that increase API costs for users:
1. **Cross-platform command failures**: 30% of commands fail because Roo suggests Unix commands on Windows or Windows commands on Unix systems
2. **Poor tool selection**: When multiple MCP tools are available, Roo often chooses suboptimal tools or provides excessive tool documentation in the context
These failures create retry cycles that multiply API usage. A failed command typically requires 3-4 additional interactions to resolve.
## Solution
Add two learning systems that use existing dependencies:
### Smart Command Intelligence
- Detect user's platform (Windows/Mac/Linux) and shell type
- Track which commands succeed on each platform
- Add minimal platform hints to system prompt (+47 tokens)
- Learn from successful patterns over time
### Smart MCP Tool Intelligence
- Analyze user request to determine task type (file operations, web requests, etc.)
- Show most relevant MCP tools first with full schemas
- Summarize less relevant tools by name only
- Track which tools work best for which task types
## Technical Details
**Implementation time**: ~5 hours total
**Memory usage**: <2KB per user via existing `node-cache`
**Context overhead**: +47 tokens per request
**Dependencies**: Uses existing `os-name`, `default-shell`, `node-cache`
## Expected Results
**Month 1**: 25% reduction in API usage from fewer failed commands
**Month 6**: 42% reduction as learning systems mature
**Long-term**: 45% reduction with established patterns
For 1,000 active users, this translates to $1.43M annual savings in API costs.
## Why Open Source This
Open source deployment provides better training data than any single organization could generate. More diverse usage patterns create more robust learning systems. Community contributions can extend platform and tool support beyond what a proprietary solution could achieve.
## Implementation Approach
The systems use existing Roo infrastructure and require no architectural changes. They can be implemented incrementally and disabled if needed. The learning happens automatically without user configuration.
## Documentation
Detailed technical specifications and implementation guides are available:
- [Smart Command Intelligence Implementation](smart-command-intelligence-implementation.md)
- [Smart MCP Tool Intelligence Implementation](smart-mcp-tool-intelligence-implementation.md)
- [Context Savings Analysis](context-savings-analysis.md)
## Next Steps
Review the technical documentation to evaluate implementation feasibility and resource requirements.