- Create RALPH-INTEGRATION.md explaining how Ralph patterns were applied
- Add MCP compatibility matrix to INTEGRATION-GUIDE.md
* All 29 MCP tools work with both Anthropic and Z.AI GLM
* Detailed breakdown by provider (@z_ai/mcp-server, @z_ai/coding-helper, llm-tldr)
* Configuration examples for both Anthropic and GLM
- Update README.md to link to RALPH-INTEGRATION.md
- Update blog post with MCP compatibility information
- Explain which Ralph patterns are integrated:
* Supervisor-agent coordination (studio-coach)
* Task delegation framework (studio-producer)
* Shared context system
* Cross-agent coordination (experiment-tracker)
* Performance coaching patterns
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
- Add llm-tldr (95% token reduction) to all installation methods
- Update MASTER-PROMPT.md with all 6 sources and real-life examples
- Update README.md with comprehensive source guide
- Update automation script with TLDR installation and MCP config
- Update INTEGRATION-GUIDE.md to clarify Z.AI MCP tools work with GLM
- Add MCP explanation and link to modelcontextprotocol.io
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
- Agent integration architecture (file-based system)
- MCP tools integration (STDIO/JSON-RPC protocol)
- Ralph framework integration (multi-agent coordination)
- Auto-triggering system (hooks-based automation)
- Multi-model support (Anthropic + Z.AI)
- Benefits and use cases for each integration
- Complete integration stack diagram
- How Ralph patterns benefit Claude Code
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