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Cascading Development Framework
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rob da726cb5bf refactor: simplify workflow.py to use AI normalization with minimal fallback 
Simplified marker extraction architecture:
- AI normalization (agents.py) handles natural conversation
- Simple line-start matching for explicit markers as fallback
- Removed complex regex patterns (DECISION_PATTERN, QUESTION_PATTERN, ACTION_PATTERN)
- Participants can now write naturally without strict formatting rules

This implements the original design intent: fast AI model normalizes conversational
text into structured format, then simple parsing logic extracts it.

Benefits:
- More flexible for participants (no strict formatting required)
- Simpler code (startswith() instead of regex)
- Clear separation: AI for understanding, code for mechanical parsing
- Cost-effective (fast models for simple extraction task)

Updated workflow-marker-extraction.puml to show patterns in notes
instead of inline text (fixes PlantUML syntax error).

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>
 		2025-11-02 18:48:12 -04:00
assets feat: allow ramble codex and gemini providers 2025-11-02 00:46:36 -03:00
automation refactor: simplify workflow.py to use AI normalization with minimal fallback 2025-11-02 18:48:12 -04:00
config feat: add multi-provider AI system with model hint optimization 2025-11-01 21:43:13 -03:00
docs refactor: simplify workflow.py to use AI normalization with minimal fallback 2025-11-02 18:48:12 -04:00
src fix: sync installer ramble provider options 2025-11-02 01:13:27 -03:00
tests feat: share ai fallback with agents and add provider status 2025-11-02 00:21:10 -03:00
tools feat: add multi-provider AI system with model hint optimization 2025-11-01 21:43:13 -03:00
.gitignore 1st commit 2025-10-27 20:17:35 -03:00
AGENTS.md fix: Add YAML syntax fix and mock AI script for testing 2025-10-31 09:18:59 -03:00
CLAUDE.md docs: comprehensive AI configuration documentation update 2025-11-01 21:48:25 -03:00
GEMINI.md uptodate agents 2025-10-30 13:17:58 -03:00
README.md docs: comprehensive AI configuration documentation update 2025-11-01 21:48:25 -03:00
VERSION 1st commit 2025-10-27 16:24:50 -03:00
pyproject.toml fix: Add YAML syntax fix and mock AI script for testing 2025-10-31 09:18:59 -03:00

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.yml defines 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