Updated PROGRESS.md:
- Changed Last Updated to 2025-11-02
- Updated Overall Completion to 57% (was 55%)
- Stage 2 Feature Discussion now explicitly notes AI normalization
- Added AI normalization details to Multi-Provider AI System section
- Documented two-tier architecture (AI primary, fallback secondary)
Updated DESIGN.md:
- Changed Updated date to 2025-11-02
- Added "Multi-Provider Fallback" to implemented features
- Added "AI Normalization" to implemented features
- Reflects current system capabilities
Both documents now accurately track the completed AI normalization work
that enables natural conversation in discussions without strict formatting.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Updated all documentation to reflect the new two-tier extraction system:
**workflow-marker-extraction.puml:**
- Completely rewritten to show AI normalization flow
- Documents agents.normalize_discussion() as primary method
- Shows simple line-start fallback for explicit markers
- Includes natural conversation examples vs. explicit markers
- Demonstrates resilience and cost-effectiveness
**AUTOMATION.md:**
- Restructured "Conversation Guidelines" section
- Emphasizes natural conversation as recommended approach
- Clarifies AI normalization extracts from conversational text
- Documents explicit markers as fallback when AI unavailable
- Explains two-tier architecture benefits
**diagrams-README.md:**
- Already updated in previous commit
All documentation now accurately reflects:
✅ AI-powered extraction (agents.py) for natural conversation
✅ Simple fallback parsing (workflow.py) for explicit markers
✅ Multi-provider resilience (claude → codex → gemini)
✅ No strict formatting requirements for participants
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Updated workflow-marker-extraction diagram description to emphasize:
- AI-powered normalization for natural conversation (agents.py)
- Simple line-start fallback for explicit markers (workflow.py)
- Two-tier extraction system design
Benefits of this approach:
- Participants write naturally without strict formatting rules
- Fast AI model handles conversation → structured data conversion
- Simple fallback code when AI unavailable
- Clear separation of concerns
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
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>
Added missing outer if statement for Claude output check to fix
"Cannot find if" syntax error. The else clause on line 56 now
properly matches the provider output check.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
- Updated commit-workflow.puml to show claude→codex→gemini fallback chain
- Updated patcher-pipeline.puml with provider fallback logic and model hints
- Updated voting-system.puml for multi-stage promotions (READY_FOR_DESIGN)
- Created ai-provider-fallback.puml documenting provider chain in detail
- Created discussion-stages.puml showing complete feature lifecycle
- Created workflow-marker-extraction.puml documenting regex patterns
- Updated diagrams-README.md with all new diagrams and workflows
- Increased diagram count from 7 to 10 total
- All diagrams now reflect current system architecture
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Add docs/PROGRESS.md - a living document that tracks implementation
status across all milestones with detailed checklists.
**Structure:**
- Quick status overview table (Milestone completion %)
- Milestone-by-milestone breakdown (M0-M4)
- Checkbox lists for every deliverable
- Stage-by-stage breakdown for M2
- File/line references for verification
- Next steps sections for each component
- Update instructions for maintainers
**Key Features:**
- Visual progress tracking (✅❌🚧)
- Completion percentages per section
- Specific missing items highlighted
- Clear next steps for each stage
- Easy to update (just check boxes)
- 431 lines covering all aspects
**Current Status Snapshot:**
- M0: Process Foundation - 100% ✅
- M1: Orchestrator MVP - 100% ✅
- M2: Stage Automation - 40% 🚧 (3/7 stages)
- M3: Gitea Integration - 0% ❌
- M4: Python Migration - 100% ✅
- Overall: ~55% complete
**Benefits:**
- No need to reassess entire project each time
- Quick reference for what's done vs. what's left
- Clear roadmap for contributors
- Tracks implementation vs. design intent
- Shows where to focus effort next
This replaces ad-hoc status assessments with a maintained living document.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
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>
Added clear implementation status tracking to DESIGN.md to show what's
currently working vs. what's planned.
Changes:
- Updated document version to v2.1
- Added "Implementation Status (2025-11-01)" section at top
- Created DESIGN.md.old backup for easy comparison
- Categorized features into: Implemented, In Progress, Planned
Current Status:
✅ Implemented: Stages 1-2, cascading rules, AI patch generation, voting
🚧 In Progress: Stage 3 (Design Discussion Gate) - being implemented now
📋 Planned: Stages 4-7, moderator protocol, bug sub-cycles
The three-stage workflow (Feature → Design → Implementation) was always
documented correctly in DESIGN.md. The current implementation just skips
the Design stage, which we're now fixing.
This status section will be updated as each milestone is completed.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
- Fix missing space after colon in features.ai-rules.yml
- Add tools/mock_ai.sh for testing automation without real AI
- Ensures installer has valid YAML templates
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Major refactoring to support flexible AI provider configuration instead of
requiring direct API access. Users can now use whatever AI CLI tool they have
installed (claude, gemini, codex, etc.) without API keys.
## Changes to automation/agents.py
**New Functions:**
- `get_ai_config()` - Reads config from env vars or git config
- Environment: CDEV_AI_PROVIDER, CDEV_AI_COMMAND (highest priority)
- Git config: cascadingdev.aiprovider, cascadingdev.aicommand
- Default: claude-cli with "claude -p '{prompt}'"
- `call_ai_cli()` - Execute AI via CLI command
- Passes content via temp file to avoid shell escaping
- Supports {prompt} placeholder in command template
- 60s timeout with error handling
- Parses JSON from response (with/without code blocks)
- `call_ai_api()` - Direct API access (renamed from call_claude)
- Unchanged functionality
- Now used as fallback option
- `call_ai()` - Unified AI caller
- Try CLI first (if configured)
- Fall back to API (if ANTHROPIC_API_KEY set)
- Graceful failure with warnings
**Updated Functions:**
- `normalize_discussion()` - calls call_ai() instead of call_claude()
- `track_questions()` - calls call_ai() instead of call_claude()
- `track_action_items()` - calls call_ai() instead of call_claude()
- `track_decisions()` - calls call_ai() instead of call_claude()
**Configuration Precedence:**
1. Environment variables (session-scoped)
2. Git config (repo-scoped)
3. Defaults (claude-cli)
## Changes to docs/AUTOMATION.md
**Updated Sections:**
- "Requirements" - Now lists CLI as Option 1 (recommended), API as Option 2
- "Configuration" - Complete rewrite with 5 provider examples:
1. Claude CLI (default)
2. Gemini CLI
3. OpenAI Codex CLI
4. Direct API (Anthropic)
5. Custom AI command
- "Troubleshooting" - Added "AI command failed" section, updated error messages
**New Configuration Examples:**
```bash
# Claude Code (default)
git config cascadingdev.aicommand "claude -p '{prompt}'"
# Gemini
git config cascadingdev.aiprovider "gemini-cli"
git config cascadingdev.aicommand "gemini '{prompt}'"
# Custom
git config cascadingdev.aicommand "my-ai-tool --prompt '{prompt}' --format json"
```
## Benefits
1. **No API Key Required**: Use existing CLI tools (claude, gemini, etc.)
2. **Flexible Configuration**: Git config (persistent) or env vars (session)
3. **Provider Agnostic**: Works with any CLI that returns JSON
4. **Backward Compatible**: Still supports direct API if ANTHROPIC_API_KEY set
5. **User-Friendly**: Defaults to "claude -p" if available
## Testing
- ✅ get_ai_config() tests:
- Default: claude-cli with "claude -p '{prompt}'"
- Git config override: gemini-cli with "gemini '{prompt}'"
- Env var override: codex-cli with "codex '{prompt}'"
- ✅ extract_mentions() still works (no AI required)
- ✅ All 6 workflow tests pass
## Impact
Users with Claude Code installed can now use the automation without any
configuration - it just works! Same for users with gemini or codex CLIs.
Only requires git config setup if using non-default command.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Major documentation improvements:
1. Add CLAUDE.md
- Comprehensive guide for AI assistants working in this repo
- Repository architecture and directory structure
- Common development commands and workflows
- Build system explanation
- Key concepts and design philosophy
2. Restructure DESIGN.md
- Clarify three directory contexts: CascadingDev Repo, Install Bundle, User Project
- Add clear section headers and visual separation
- Mark future/unimplemented features with 🚧 emoji
- Document undocumented wins: META system and Ramble providers
- Add comprehensive Ramble documentation (mock and claude providers)
- Document template META system with code examples
3. Fix setup_project.py
- Correct error message path (was scripts/hooks/pre-commit, now assets/hooks/pre-commit)
These changes address confusion between tooling source, distribution bundle, and
generated user projects while highlighting sophisticated features like the
self-describing template system and multi-provider Ramble GUI.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
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