rob
372d24b30d
Major automation enhancements for flexible AI provider configuration: 1. Add config/ai.yml - Centralized AI configuration - Three command chains: default, fast, quality - Multi-provider fallback (Claude → Codex → Gemini) - Configurable per optimization level - Sentinel token configuration 2. Extend automation/ai_config.py - Add RunnerSettings with three chain support - Add get_chain_for_hint() method - Load and validate all three command chains - Proper fallback to defaults 3. Update automation/runner.py - Read model_hint from .ai-rules.yml - Pass model_hint to generate_output() - Support output_type hint overrides 4. Update automation/patcher.py - Add model_hint parameter throughout pipeline - Inject TASK COMPLEXITY hint into prompts - ModelConfig.get_commands_for_hint() selects chain - Fallback mechanism tries all commands in chain 5. Add design discussion stage to features.ai-rules.yml - New design_gate_writer rule (model_hint: fast) - New design_discussion_writer rule (model_hint: quality) - Update feature_request to create design gate - Update feature_discussion to create design gate - Add design.discussion.md file associations - Proper status transitions: READY_FOR_DESIGN → READY_FOR_IMPLEMENTATION 6. Add assets/templates/design.discussion.md - Template for Stage 3 design discussions - META header with tokens support - Design goals and participation instructions 7. Update tools/setup_claude_agents.sh - Agent descriptions reference TASK COMPLEXITY hint - cdev-patch: "MUST BE USED when TASK COMPLEXITY is FAST" - cdev-patch-quality: "MUST BE USED when TASK COMPLEXITY is QUALITY" 8. Fix assets/hooks/pre-commit - Correct template path comment (process/templates not assets/templates) 9. Update tools/mock_ai.sh - Log prompts to /tmp/mock_ai_prompts.log for debugging Impact: - Users can configure AI providers via config/ai.yml - Automatic fallback between Claude, Codex, Gemini - Fast models for simple tasks (vote counting, gate checks) - Quality models for complex tasks (design, implementation planning) - Reduced costs through intelligent model selection - Design stage now properly integrated into workflow 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> |
||
|---|---|---|
| assets | ||
| automation | ||
| config | ||
| docs | ||
| src | ||
| tests | ||
| tools | ||
| .gitignore | ||
| AGENTS.md | ||
| CLAUDE.md | ||
| GEMINI.md | ||
| README.md | ||
| VERSION | ||
| 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.
- 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