Andy
d8f4de9a06
* docs(phase-1): research core validation pipeline Phase 1: Core Validation Pipeline - Finding-validator pattern from follow-up reviews documented - Orchestrator integration points identified - Context bug at line 1288 analyzed - Prompt patterns for Read tool instructions catalogued - Evidence/scope validation strategies defined Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * fix(01-01): include AI reviews in follow-up context - Fixed ai_bot_comments_since_review to include ai_reviews - Mirrors contributor_comments + contributor_reviews pattern - AI formal reviews (CodeRabbit, Cursor) now available to follow-up agents * feat(01-02): add tool usage instructions to follow-up agent prompts - Add "CRITICAL: Full Context Analysis" section to follow-up prompts - Require Read tool usage before reporting findings - Require +-20 lines context around flagged lines - Require actual code evidence, not descriptions - Require Grep search for mitigations Files: pr_followup_resolution_agent.md, pr_followup_newcode_agent.md * test(01-01): add tests for AI reviews inclusion in follow-up context - Test AI bot patterns include known bots (CodeRabbit, Gemini, Copilot) - Test FollowupReviewContext has ai_bot_comments_since_review field - Test FollowupContextGatherer.gather() includes AI formal reviews - Test AI reviews are correctly separated from contributor reviews * feat(01-03): add finding-validator agent to parallel orchestrator - Load pr_finding_validator.md prompt in _define_specialist_agents() - Add finding-validator AgentDefinition with tools [Read, Grep, Glob] - Description instructs to validate ALL findings after specialist agents * feat(01-03): add Phase 3.5 validation step to orchestrator prompt - Add finding-validator to Available Specialist Agents section - Add Phase 3.5: Finding Validation (CRITICAL - Prevent False Positives) - Instructions to invoke validator for ALL findings after synthesis - Filter based on validation status (confirmed_valid, dismissed_false_positive) - Re-calculate verdict based only on validated findings * feat(01-03): add validation fields to orchestrator output format - Add validation_summary top-level field (total, confirmed, dismissed, needs_review) - Add validation_status field per finding (confirmed_valid, dismissed_false_positive, needs_human_review) - Add validation_evidence field per finding with actual code snippet - Document that dismissed findings should be removed from output * feat(01-04): add evidence validation function for PR findings - Add _validate_finding_evidence() helper to validate evidence quality - Rejects findings with no evidence or very short evidence (<10 chars) - Filters findings that start with description patterns (not code) - Requires code syntax characters in evidence to pass validation * feat(01-04): add scope pre-filter function for PR findings - Add _is_finding_in_scope() to verify findings are within PR scope - Rejects findings for files not in changed files list - Allows impact findings (affect/break/depend) for unchanged files - Rejects findings with invalid line numbers (<= 0) * feat(01-04): integrate evidence and scope filters into finding processing - Apply _validate_finding_evidence to filter findings with poor evidence - Apply _is_finding_in_scope to filter findings outside PR scope - Log filtered findings with reasons for debugging - Replace unique_findings with validated_findings for verdict/summary * docs(02): create phase 2 plans for context enrichment Phase 02: Context Enrichment - 3 plans in 2 waves - Plans 01 & 02 parallel (Wave 1), Plan 03 sequential (Wave 2) - Ready for execution Plan details: - 02-01: JS/TS import analysis (path aliases, CommonJS, re-exports) - 02-02: Python import analysis via AST - 02-03: Related files enhancement (limit 50, prioritization, reverse deps) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * feat(02-02): add Python import resolution methods - Add ast import for Python AST parsing - Add _resolve_python_import() to resolve module names to file paths - Add _find_python_imports() to extract imports using AST - Handles relative imports (from . import, from .. import) - Handles absolute imports that map to project files - Gracefully handles SyntaxError in Python files * feat(02-02): integrate Python import detection into _find_imports - Replace TODO comment with actual Python import detection - Call _find_python_imports() for .py files in _find_imports() - Python files now have their imports resolved to file paths * fix(02-01): prevent _load_json_safe from mangling path patterns with /* The regex-based comment stripping was incorrectly removing path patterns like "@/*" from tsconfig.json because /* looks like a multi-line comment. Fix: - Try standard JSON parse first (most tsconfigs don't have comments) - Fall back to smarter comment stripping that checks if // appears outside of strings by counting quotes before the comment position This ensures path aliases like "@/*": ["src/*"] are preserved. Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * feat(02-03): add reverse dependency detection - Add _find_dependents() method to find files that import a given file - Use grep with recursive search for import/from statements - Skip generic names (index, main, utils) to avoid too many matches - 5-second timeout protection prevents hanging on large repos - Exclude common non-code directories (node_modules, .git, __pycache__) - Limit results to prevent overwhelming context * feat(02-03): add smart file prioritization - Add _prioritize_related_files() method for relevance-based ordering - Priority: tests > type definitions > configs > other files - Sort alphabetically within each category for consistency - Supports limit parameter (default 50) - Fix .d.ts detection using name_lower.endswith('.d.ts') * feat(02-03): update _find_related_files with reverse deps and prioritization - Add reverse dependency detection call to _find_related_files() - Replace simple sorting with _prioritize_related_files() - Increase limit from 20 to 50 files - Update find_related_files_for_root() static method limit to 50 - Tests pass (1616 passed, 11 skipped) * docs(03): research phase 3 cross-validation domain Phase 3: Cross-Validation - Confidence threshold routing (REQ-011) - Multi-agent cross-validation (REQ-012) - Standard stack identified (built-in Python, existing Pydantic models) - Architecture patterns documented - Common pitfalls catalogued Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * docs(03): create phase 3 plans for cross-validation Phase 03: Cross-Validation - 2 plans in 2 waves - Plan 03-01: Confidence threshold routing (Wave 1) - Plan 03-02: Multi-agent agreement and confidence boost (Wave 2) - Ready for execution Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * fix(03): revise plans based on checker feedback Address checker issues: - 03-01: Add Task 0 to add confidence, source_agents, cross_validated fields to PRReviewFinding dataclass - 03-02: Update Task 1 to clarify it uses the new PRReviewFinding fields (not just pydantic model) - 03-02: Document that AgentAgreement is logged for monitoring, not persisted to PRReviewResult Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * feat(03-01): add cross-validation fields to PRReviewFinding - Add confidence: float = 0.5 field for confidence scoring - Add source_agents: list[str] field to track which agents reported finding - Add cross_validated: bool field to track multi-agent agreement - Update to_dict() to include all three new fields - Update from_dict() to handle all three new fields with defaults - Fix output_validator to treat confidence=0.5 as default (not explicit) * feat(03-01): add confidence routing function - Add ConfidenceTier class with HIGH/MEDIUM/LOW constants (0.8/0.5 thresholds) - Add _apply_confidence_routing() method to ParallelOrchestratorReviewer - HIGH (>=0.8): Include finding as-is - MEDIUM (0.5-0.8): Include with '[Potential]' prefix in title - LOW (<0.5): Log and exclude from output - Handle missing confidence gracefully (default to 0.5) - Log tier distribution after routing * feat(03-01): wire confidence routing into review pipeline - Call _apply_confidence_routing() after evidence/scope validation - Log routing results: included count vs dropped (low confidence) - Use routed findings for verdict and summary generation - Confidence routing happens AFTER validation, BEFORE verdict * docs(03-01): update orchestrator prompt with confidence tier guidance - Add 'Confidence Tiers' section after Phase 3.5 - Document tier thresholds: HIGH (>=0.8), MEDIUM (0.5-0.8), LOW (<0.5) - Include guidelines for assigning confidence scores - Provide examples of confidence score assignments - Placed between validation section and output format * docs(03-01): complete confidence threshold routing plan Tasks completed: 4/4 - Task 0: Add cross-validation fields to PRReviewFinding model - Task 1: Add confidence routing function - Task 2: Wire confidence routing into review pipeline - Task 3: Update orchestrator prompt with confidence tier guidance SUMMARY: .planning/phases/03-cross-validation/03-01-SUMMARY.md * feat(03-02): add _cross_validate_findings method - Groups findings by (file, line, category) for multi-agent agreement detection - Boosts confidence by 0.15 (capped at 0.95) when 2+ agents agree - Sets cross_validated=True and populates source_agents on PRReviewFinding - Returns AgentAgreement tracking object with agreed_findings list - Uses collections.defaultdict for efficient grouping - Merges evidence with '---' separator, keeps highest severity * feat(03-02): wire cross-validation into review pipeline - Call _cross_validate_findings after deduplication - Cross-validated findings flow through evidence/scope validation - Cross-validated findings flow through confidence routing - Log AgentAgreement: info level for summary, debug level for full JSON - Pipeline order: deduplicate -> cross-validate -> validate evidence/scope -> confidence route * docs(03-02): add multi-agent agreement documentation to orchestrator prompt - Add 'Multi-Agent Agreement' section documenting confidence boost behavior - Document +0.15 confidence boost when 2+ agents agree (max 0.95) - Add example showing merged finding with cross_validated and source_agents - Document agent_agreement tracking and logging behavior - Update Phase 3: Synthesis to reference cross-validation and confidence routing * docs(04): create phase plan for integration testing Phase 04: Integration Testing - 1 plan in 1 wave - Tests all Phase 1-3 features - Ready for execution Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * test(04-01): add Phase 1 feature tests - confidence, evidence, scope - Add TestConfidenceTierRouting with 7 tests for tier boundaries - Add TestEvidenceValidation with 6 tests for code syntax detection - Add TestScopeFiltering with 6 tests for scope filtering logic - Import ConfidenceTier, _validate_finding_evidence, _is_finding_in_scope - All 18 Phase 1 tests passing * test(04-01): add Phase 2 and Phase 3 feature tests Phase 2 - Import Detection (5 tests): - Path alias detection (@/utils -> src/utils.ts) - CommonJS require('./utils') detection - Re-export (export * from) detection - Python relative import via AST - Python absolute import resolution Phase 2 - Reverse Dependencies (3 tests): - Grep-based dependent file detection - Generic name skipping (index, main, utils) - Timeout handling for large repos Phase 3 - Cross-Validation (7 tests): - Multi-agent agreement confidence boost (+0.15) - Confidence cap at 0.95 - cross_validated flag on merged findings - Grouping by (file, line, category) tuple - Description combination with ' | ' separator - Single-agent findings not boosted - Highest severity preserved on merge All 33 tests passing * test(04-01): add integration pipeline verification tests TestIntegrationPipeline (9 tests): - Full pipeline flow: high confidence + valid evidence + in scope - Low confidence filtering behavior documentation - Cross-validation elevating MEDIUM to HIGH tier - Invalid evidence rejection regardless of confidence - Out-of-scope rejection - Impact finding allowance for unchanged files - End-to-end review scenario with multiple agents - Empty findings handling - Confidence tier routing documentation Total: 42 integration tests passing * gitignore planning for GSD test * chore: remove .planning/ from git tracking These files are in .gitignore but were committed before the ignore rule was added. Removing from tracking to keep planning files local. Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * fix: cross-platform _find_dependents and improved test assertions - Replace grep subprocess with pure Python os.walk() + re.compile() for cross-platform compatibility (Windows, macOS, Linux) - Add debug logging to _load_json_safe() for troubleshooting - Fix test assertion type (set instead of list) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * fix: address all PR review findings (10 issues) HIGH priority fixes: - Fix path alias resolution to use project root instead of relative path - Rewrite test to mock os.walk instead of subprocess.run - Extract duplicated 'Full Context Analysis' to partials/ with sync comments MEDIUM priority fixes: - Extract _resolve_any_import() helper to eliminate DRY violation - Improve path alias test to verify actual resolution - Add guard for empty target_paths in tsconfig - Convert ConfidenceTier to str, Enum pattern - Add block comment stripping in _load_json_safe LOW priority fixes: - Remove unused tempfile import - Remove duplicate .planning/ gitignore entry Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * fix: restore phase_config module after mock to prevent test pollution The test_integration_phase4.py was mocking phase_config at module level during import, which polluted sys.modules for subsequent tests. This caused test_agent_configs::test_thinking_defaults_are_valid to fail because THINKING_BUDGET_MAP.keys() returned empty from the MagicMock. Fix: Save and restore the original phase_config module after loading the orchestrator module. Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * fix: add env cleanup fixture to test_client.py for test isolation Add autouse fixture to clear AUTH_TOKEN_ENV_VARS before and after each test in TestClientTokenValidation. This ensures test isolation and prevents env var pollution from previous tests in the suite. Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * fix: mock decrypt_token in encrypted token rejection tests Also mock decrypt_token to raise ValueError, ensuring the encrypted token flows through to validate_token_not_encrypted regardless of whether the CI environment has a claude CLI available that might attempt decryption. Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * fix: restore all mocked modules in test_integration_phase4.py The test was mocking core.client, phase_config, and other modules at module level but only restoring phase_config. This caused core.client to remain as a MagicMock, which made validate_token_not_encrypted a MagicMock that never raised ValueError. Now all mocked modules are saved before mocking and restored after the orchestrator module is loaded. Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * fix: normalize path separators for cross-platform test compatibility Windows returns paths with backslashes (src\utils.ts) while the test expected forward slashes (src/utils.ts). Normalize to forward slashes for comparison. Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * fix: normalize path separators in all import detection tests Apply the same Windows path normalization fix to: - test_commonjs_require_detection - test_reexport_detection - test_python_relative_import - test_python_absolute_import Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> --------- Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
Auto Claude
Autonomous multi-agent coding framework that plans, builds, and validates software for you.
Download
Stable Release
| Platform | Download |
|---|---|
| Windows | Auto-Claude-2.7.4-win32-x64.exe |
| macOS (Apple Silicon) | Auto-Claude-2.7.4-darwin-arm64.dmg |
| macOS (Intel) | Auto-Claude-2.7.4-darwin-x64.dmg |
| Linux | Auto-Claude-2.7.4-linux-x86_64.AppImage |
| Linux (Debian) | Auto-Claude-2.7.4-linux-amd64.deb |
| Linux (Flatpak) | Auto-Claude-2.7.4-linux-x86_64.flatpak |
Beta Release
⚠️ Beta releases may contain bugs and breaking changes. View all releases
| Platform | Download |
|---|---|
| Windows | Auto-Claude-2.7.2-beta.10-win32-x64.exe |
| macOS (Apple Silicon) | Auto-Claude-2.7.2-beta.10-darwin-arm64.dmg |
| macOS (Intel) | Auto-Claude-2.7.2-beta.10-darwin-x64.dmg |
| Linux | Auto-Claude-2.7.2-beta.10-linux-x86_64.AppImage |
| Linux (Debian) | Auto-Claude-2.7.2-beta.10-linux-amd64.deb |
| Linux (Flatpak) | Auto-Claude-2.7.2-beta.10-linux-x86_64.flatpak |
All releases include SHA256 checksums and VirusTotal scan results for security verification.
Requirements
- Claude Pro/Max subscription - Get one here
- Claude Code CLI -
npm install -g @anthropic-ai/claude-code - Git repository - Your project must be initialized as a git repo
Quick Start
- Download and install the app for your platform
- Open your project - Select a git repository folder
- Connect Claude - The app will guide you through OAuth setup
- Create a task - Describe what you want to build
- Watch it work - Agents plan, code, and validate autonomously
Features
| Feature | Description |
|---|---|
| Autonomous Tasks | Describe your goal; agents handle planning, implementation, and validation |
| Parallel Execution | Run multiple builds simultaneously with up to 12 agent terminals |
| Isolated Workspaces | All changes happen in git worktrees - your main branch stays safe |
| Self-Validating QA | Built-in quality assurance loop catches issues before you review |
| AI-Powered Merge | Automatic conflict resolution when integrating back to main |
| Memory Layer | Agents retain insights across sessions for smarter builds |
| GitHub/GitLab Integration | Import issues, investigate with AI, create merge requests |
| Linear Integration | Sync tasks with Linear for team progress tracking |
| Cross-Platform | Native desktop apps for Windows, macOS, and Linux |
| Auto-Updates | App updates automatically when new versions are released |
Interface
Kanban Board
Visual task management from planning through completion. Create tasks and monitor agent progress in real-time.
Agent Terminals
AI-powered terminals with one-click task context injection. Spawn multiple agents for parallel work.
Roadmap
AI-assisted feature planning with competitor analysis and audience targeting.
Additional Features
- Insights - Chat interface for exploring your codebase
- Ideation - Discover improvements, performance issues, and vulnerabilities
- Changelog - Generate release notes from completed tasks
Project Structure
Auto-Claude/
├── apps/
│ ├── backend/ # Python agents, specs, QA pipeline
│ └── frontend/ # Electron desktop application
├── guides/ # Additional documentation
├── tests/ # Test suite
└── scripts/ # Build utilities
CLI Usage
For headless operation, CI/CD integration, or terminal-only workflows:
cd apps/backend
# Create a spec interactively
python spec_runner.py --interactive
# Run autonomous build
python run.py --spec 001
# Review and merge
python run.py --spec 001 --review
python run.py --spec 001 --merge
See guides/CLI-USAGE.md for complete CLI documentation.
Development
Want to build from source or contribute? See CONTRIBUTING.md for complete development setup instructions.
For Linux-specific builds (Flatpak, AppImage), see guides/linux.md.
Security
Auto Claude uses a three-layer security model:
- OS Sandbox - Bash commands run in isolation
- Filesystem Restrictions - Operations limited to project directory
- Dynamic Command Allowlist - Only approved commands based on detected project stack
All releases are:
- Scanned with VirusTotal before publishing
- Include SHA256 checksums for verification
- Code-signed where applicable (macOS)
Available Scripts
| Command | Description |
|---|---|
npm run install:all |
Install backend and frontend dependencies |
npm start |
Build and run the desktop app |
npm run dev |
Run in development mode with hot reload |
npm run package |
Package for current platform |
npm run package:mac |
Package for macOS |
npm run package:win |
Package for Windows |
npm run package:linux |
Package for Linux |
npm run package:flatpak |
Package as Flatpak (see guides/linux.md) |
npm run lint |
Run linter |
npm test |
Run frontend tests |
npm run test:backend |
Run backend tests |
Contributing
We welcome contributions! Please read CONTRIBUTING.md for:
- Development setup instructions
- Code style guidelines
- Testing requirements
- Pull request process
Community
- Discord - Join our community
- Issues - Report bugs or request features
- Discussions - Ask questions
License
AGPL-3.0 - GNU Affero General Public License v3.0
Auto Claude is free to use. If you modify and distribute it, or run it as a service, your code must also be open source under AGPL-3.0.
Commercial licensing available for closed-source use cases.
