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Major documentation updates to align with multi-provider AI system: 1. Update CLAUDE.md (lines 213-332) - Add new "AI Configuration System" section - Document config/ai.yml structure and three optimization levels - Explain model hint propagation pipeline (rule → runner → patcher) - Add provider setup table (Claude, Codex, Gemini) - Document Claude subagent setup with ./tools/setup_claude_agents.sh - List implementation modules with line number references - Explain environment variable overrides - Document fallback behavior when all providers fail 2. Update docs/DESIGN.md (lines 894-1077) - Add "Automation AI Configuration" section before Stage Model - Document configuration architecture with full YAML example - Explain model hint system with .ai-rules.yml examples - Detail execution flow through 4 steps (rule eval → prompt → chain → fallback) - Show example prompt with TASK COMPLEXITY hint injection - Add provider comparison table with fast/default/quality models - Document implementation modules with line references - Add cost optimization examples (93% savings on simple tasks) - Explain environment overrides and persistence 3. Update docs/AUTOMATION.md (lines 70-148) - Restructure Phase 2 requirements to emphasize config/ai.yml - Add full YAML configuration example with three chains - Explain how model hints work (fast vs quality) - Update Claude subagent documentation - Clarify auto-selection based on TASK COMPLEXITY - Move git config to deprecated status - Emphasize environment variables as optional overrides 4. Update README.md (line 10) - Add "Multi-Provider AI System" to key features - Brief mention of fallback chains and model selection Impact: - AI assistants can now discover the multi-provider system - Users understand how to configure providers via config/ai.yml - Clear explanation of cost optimization through model hints - Complete documentation of the execution pipeline - All major docs now reference the same configuration approach Resolves documentation gap identified in project review. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> |
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| AGENTS.md | ||
| CLAUDE.md | ||
| GEMINI.md | ||
| README.md | ||
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| pyproject.toml | ||
README.md
CascadingDev (CDev)
CDev — short for Cascading Development — is a Git-native AI–human collaboration framework that automates documentation, discussion summaries, and code review directly within your repository.
It lets you build self-documenting projects where AI assists in generating and maintaining feature discussions, design docs, and implementation plans — all version-controlled alongside your code.
✨ Key Features
- Git-Integrated Workflow — every discussion, decision, and artifact lives in Git.
- Multi-Provider AI System — automatic fallback chains (Claude → Codex → Gemini) with intelligent model selection (fast/quality).
- Cascading Rules System — nearest
.ai-rules.ymldefines how automation behaves. - Stage-Per-Discussion Model — separate files for feature, design, implementation, testing, and review.
- Pre-commit Hook — automatically maintains summaries, diagrams, and vote tallies.
- Ramble GUI — friendly PySide6/PyQt5 dialog for capturing structured feature requests.
- Deterministic Builds — a reproducible installer bundle you can unzip and run anywhere.
🚀 Quick Start (Developers)
# 1. Create and activate a virtual environment
python3 -m venv .venv
source .venv/bin/activate
pip install --upgrade pip wheel PySide6
# 2. Build the installer bundle
python tools/build_installer.py
# 3. Test-install into a temporary folder
python install/cascadingdev-*/setup_cascadingdev.py --target /tmp/myproject --no-ramble