2025-08-15 15:15:51 +02:00
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# SuperClaude PyPI Publishing Scripts
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This directory contains scripts for building and publishing SuperClaude to PyPI.
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## Scripts
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### `publish.sh` - Main Publishing Script
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Easy-to-use shell script for common publishing tasks:
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```bash
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# Test upload to TestPyPI
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./scripts/publish.sh test
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# Test installation from TestPyPI
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./scripts/publish.sh test-install
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# Production upload to PyPI
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./scripts/publish.sh prod
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# Build package only
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./scripts/publish.sh build
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# Clean build artifacts
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./scripts/publish.sh clean
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# Validate project structure
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./scripts/publish.sh check
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```
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### `build_and_upload.py` - Advanced Build Script
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Python script with detailed control over the build and upload process:
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```bash
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# Build and upload to TestPyPI
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python scripts/build_and_upload.py --testpypi
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# Test installation from TestPyPI
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python scripts/build_and_upload.py --testpypi --test-install
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# Production upload (with confirmation)
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python scripts/build_and_upload.py
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# Skip validation (for faster builds)
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python scripts/build_and_upload.py --skip-validation --testpypi
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# Clean only
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python scripts/build_and_upload.py --clean
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```
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## Prerequisites
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1. **PyPI Account**: Register at https://pypi.org/account/register/
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2. **API Tokens**: Generate tokens at https://pypi.org/manage/account/
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3. **Configuration**: Create `~/.pypirc`:
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```ini
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[pypi]
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username = __token__
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password = pypi-[your-production-token]
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[testpypi]
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repository = https://test.pypi.org/legacy/
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username = __token__
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password = pypi-[your-test-token]
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```
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## GitHub Actions
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The `.github/workflows/publish-pypi.yml` workflow automates publishing:
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- **Automatic**: Publishes to PyPI when a GitHub release is created
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- **Manual**: Can be triggered manually for TestPyPI uploads
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- **Validation**: Includes package validation and installation testing
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### Required Secrets
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Set these in your GitHub repository settings → Secrets and variables → Actions:
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- `PYPI_API_TOKEN`: Production PyPI token
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- `TEST_PYPI_API_TOKEN`: TestPyPI token
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## Publishing Workflow
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### 1. Development Release (TestPyPI)
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```bash
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# Test the build and upload process
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./scripts/publish.sh test
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# Verify the package installs correctly
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./scripts/publish.sh test-install
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```
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### 2. Production Release (PyPI)
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#### Option A: Manual
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```bash
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# Upload directly (requires confirmation)
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./scripts/publish.sh prod
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```
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#### Option B: GitHub Release (Recommended)
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1. Update version in code
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2. Commit and push changes
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3. Create a new release on GitHub
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4. GitHub Actions will automatically build and publish
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## Version Management
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Before publishing, ensure version consistency across:
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- `pyproject.toml`
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refactor: PEP8 compliance - directory rename and code formatting (#425)
* fix(orchestration): add WebFetch auto-trigger for infrastructure configuration
Problem: Infrastructure configuration changes (e.g., Traefik port settings)
were being made based on assumptions without consulting official documentation,
violating the 'Evidence > assumptions' principle in PRINCIPLES.md.
Solution:
- Added Infrastructure Configuration Validation section to MODE_Orchestration.md
- Auto-triggers WebFetch for infrastructure tools (Traefik, nginx, Docker, etc.)
- Enforces MODE_DeepResearch activation for investigation
- BLOCKS assumption-based configuration changes
Testing: Verified WebFetch successfully retrieves Traefik official docs (port 80 default)
This prevents production outages from infrastructure misconfiguration by ensuring
all technical recommendations are backed by official documentation.
* feat: Add PM Agent (Project Manager Agent) for seamless orchestration
Introduces PM Agent as the default orchestration layer that coordinates
all sub-agents and manages workflows automatically.
Key Features:
- Default orchestration: All user interactions handled by PM Agent
- Auto-delegation: Intelligent sub-agent selection based on task analysis
- Docker Gateway integration: Zero-token baseline with dynamic MCP loading
- Self-improvement loop: Automatic documentation of patterns and mistakes
- Optional override: Users can specify sub-agents explicitly if desired
Architecture:
- Agent spec: SuperClaude/Agents/pm-agent.md
- Command: SuperClaude/Commands/pm.md
- Updated docs: README.md (15→16 agents), agents.md (new Orchestration category)
User Experience:
- Default: PM Agent handles everything (seamless, no manual routing)
- Optional: Explicit --agent flag for direct sub-agent access
- Both modes available simultaneously (no user downside)
Implementation Status:
- ✅ Specification complete
- ✅ Documentation complete
- ⏳ Prototype implementation needed
- ⏳ Docker Gateway integration needed
- ⏳ Testing and validation needed
Refs: kazukinakai/docker-mcp-gateway (IRIS MCP Gateway integration)
* feat: Add Agent Orchestration rules for PM Agent default activation
Implements PM Agent as the default orchestration layer in RULES.md.
Key Changes:
- New 'Agent Orchestration' section (CRITICAL priority)
- PM Agent receives ALL user requests by default
- Manual override with @agent-[name] bypasses PM Agent
- Agent Selection Priority clearly defined:
1. Manual override → Direct routing
2. Default → PM Agent → Auto-delegation
3. Delegation based on keywords, file types, complexity, context
User Experience:
- Default: PM Agent handles everything (seamless)
- Override: @agent-[name] for direct specialist access
- Transparent: PM Agent reports delegation decisions
This establishes PM Agent as the orchestration layer while
respecting existing auto-activation patterns and manual overrides.
Next Steps:
- Local testing in agiletec project
- Iteration based on actual behavior
- Documentation updates as needed
* refactor(pm-agent): redesign as self-improvement meta-layer
Problem Resolution:
PM Agent's initial design competed with existing auto-activation for task routing,
creating confusion about orchestration responsibilities and adding unnecessary complexity.
Design Change:
Redefined PM Agent as a meta-layer agent that operates AFTER specialist agents
complete tasks, focusing on:
- Post-implementation documentation and pattern recording
- Immediate mistake analysis with prevention checklists
- Monthly documentation maintenance and noise reduction
- Pattern extraction and knowledge synthesis
Two-Layer Orchestration System:
1. Task Execution Layer: Existing auto-activation handles task routing (unchanged)
2. Self-Improvement Layer: PM Agent meta-layer handles documentation (new)
Files Modified:
- SuperClaude/Agents/pm-agent.md: Complete rewrite with meta-layer design
- Category: orchestration → meta
- Triggers: All user interactions → Post-implementation, mistakes, monthly
- Behavioral Mindset: Continuous learning system
- Self-Improvement Workflow: BEFORE/DURING/AFTER/MISTAKE RECOVERY/MAINTENANCE
- SuperClaude/Core/RULES.md: Agent Orchestration section updated
- Split into Task Execution Layer + Self-Improvement Layer
- Added orchestration flow diagram
- Clarified PM Agent activates AFTER task completion
- README.md: Updated PM Agent description
- "orchestrates all interactions" → "ensures continuous learning"
- Docs/User-Guide/agents.md: PM Agent section rewritten
- Section: Orchestration Agent → Meta-Layer Agent
- Expertise: Project orchestration → Self-improvement workflow executor
- Examples: Task coordination → Post-implementation documentation
- PR_DOCUMENTATION.md: Comprehensive PR documentation added
- Summary, motivation, changes, testing, breaking changes
- Two-layer orchestration system diagram
- Verification checklist
Integration Validated:
Tested with agiletec project's self-improvement-workflow.md:
✅ PM Agent aligns with existing BEFORE/DURING/AFTER/MISTAKE RECOVERY phases
✅ Complements (not competes with) existing workflow
✅ agiletec workflow defines WHAT, PM Agent defines WHO executes it
Breaking Changes: None
- Existing auto-activation continues unchanged
- Specialist agents unaffected
- User workflows remain the same
- New capability: Automatic documentation and knowledge maintenance
Value Proposition:
Transforms SuperClaude into a continuously learning system that accumulates
knowledge, prevents recurring mistakes, and maintains fresh documentation
without manual intervention.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
* docs: add Claude Code conversation history management research
Research covering .jsonl file structure, performance impact, and retention policies.
Content:
- Claude Code .jsonl file format and message types
- Performance issues from GitHub (memory leaks, conversation compaction)
- Retention policies (consumer vs enterprise)
- Rotation recommendations based on actual data
- File history snapshot tracking mechanics
Source: Moved from agiletec project (research applicable to all Claude Code projects)
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
* feat: add Development documentation structure
Phase 1: Documentation Structure complete
- Add Docs/Development/ directory for development documentation
- Add ARCHITECTURE.md - System architecture with PM Agent meta-layer
- Add ROADMAP.md - 5-phase development plan with checkboxes
- Add TASKS.md - Daily task tracking with progress indicators
- Add PROJECT_STATUS.md - Current status dashboard and metrics
- Add pm-agent-integration.md - Implementation guide for PM Agent mode
This establishes comprehensive documentation foundation for:
- System architecture understanding
- Development planning and tracking
- Implementation guidance
- Progress visibility
Related: #pm-agent-mode #documentation #phase-1
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
* feat: PM Agent session lifecycle and PDCA implementation
Phase 2: PM Agent Mode Integration (Design Phase)
Commands/pm.md updates:
- Add "Always-Active Foundation Layer" concept
- Add Session Lifecycle (Session Start/During Work/Session End)
- Add PDCA Cycle (Plan/Do/Check/Act) automation
- Add Serena MCP Memory Integration (list/read/write_memory)
- Document auto-activation triggers
Agents/pm-agent.md updates:
- Add Session Start Protocol (MANDATORY auto-activation)
- Add During Work PDCA Cycle with example workflows
- Add Session End Protocol with state preservation
- Add PDCA Self-Evaluation Pattern
- Add Documentation Strategy (temp → patterns/mistakes)
- Add Memory Operations Reference
Key Features:
- Session start auto-activation for context restoration
- 30-minute checkpoint saves during work
- Self-evaluation with think_about_* operations
- Systematic documentation lifecycle
- Knowledge evolution to CLAUDE.md
Implementation Status:
- ✅ Design complete (Commands/pm.md, Agents/pm-agent.md)
- ⏳ Implementation pending (Core components)
- ⏳ Serena MCP integration pending
Salvaged from mistaken development in ~/.claude directory
Related: #pm-agent-mode #session-lifecycle #pdca-cycle #phase-2
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
* fix: disable Serena MCP auto-browser launch
Disable web dashboard and GUI log window auto-launch in Serena MCP server
to prevent intrusive browser popups on startup. Users can still manually
access the dashboard at http://localhost:24282/dashboard/ if needed.
Changes:
- Add CLI flags to Serena run command:
- --enable-web-dashboard false
- --enable-gui-log-window false
- Ensures Git-tracked configuration (no reliance on ~/.serena/serena_config.yml)
- Aligns with AIRIS MCP Gateway integration approach
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
* refactor: rename directories to lowercase for PEP8 compliance
- Rename superclaude/Agents -> superclaude/agents
- Rename superclaude/Commands -> superclaude/commands
- Rename superclaude/Core -> superclaude/core
- Rename superclaude/Examples -> superclaude/examples
- Rename superclaude/MCP -> superclaude/mcp
- Rename superclaude/Modes -> superclaude/modes
This change follows Python PEP8 naming conventions for package directories.
* style: fix PEP8 violations and update package name to lowercase
Changes:
- Format all Python files with black (43 files reformatted)
- Update package name from 'SuperClaude' to 'superclaude' in pyproject.toml
- Fix import statements to use lowercase package name
- Add missing imports (timedelta, __version__)
- Remove old SuperClaude.egg-info directory
PEP8 violations reduced from 2672 to 701 (mostly E501 line length due to black's 88 char vs flake8's 79 char limit).
* docs: add PM Agent development documentation
Add comprehensive PM Agent development documentation:
- PM Agent ideal workflow (7-phase autonomous cycle)
- Project structure understanding (Git vs installed environment)
- Installation flow understanding (CommandsComponent behavior)
- Task management system (current-tasks.md)
Purpose: Eliminate repeated explanations and enable autonomous PDCA cycles
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
* feat(pm-agent): add self-correcting execution and warning investigation culture
## Changes
### superclaude/commands/pm.md
- Add "Self-Correcting Execution" section with root cause analysis protocol
- Add "Warning/Error Investigation Culture" section enforcing zero-tolerance for dismissal
- Define error detection protocol: STOP → Investigate → Hypothesis → Different Solution → Execute
- Document anti-patterns (retry without understanding) and correct patterns (research-first)
### docs/Development/hypothesis-pm-autonomous-enhancement-2025-10-14.md
- Add PDCA workflow hypothesis document for PM Agent autonomous enhancement
## Rationale
PM Agent must never retry failed operations without understanding root causes.
All warnings and errors require investigation via context7/WebFetch/documentation
to ensure production-quality code and prevent technical debt accumulation.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
* feat(installer): add airis-mcp-gateway MCP server option
## Changes
- Add airis-mcp-gateway to MCP server options in installer
- Configuration: GitHub-based installation via uvx
- Repository: https://github.com/oraios/airis-mcp-gateway
- Purpose: Dynamic MCP Gateway for zero-token baseline and on-demand tool loading
## Implementation
Added to setup/components/mcp.py self.mcp_servers dictionary with:
- install_method: github
- install_command: uvx test installation
- run_command: uvx runtime execution
- required: False (optional server)
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
---------
Co-authored-by: kazuki <kazuki@kazukinoMacBook-Air.local>
Co-authored-by: Claude <noreply@anthropic.com>
2025-10-14 12:17:09 +09:00
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- `superclaude/__init__.py`
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- `superclaude/__main__.py`
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2025-08-15 15:15:51 +02:00
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- `setup/__init__.py`
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The build script validates version consistency automatically.
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## Troubleshooting
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### Build Failures
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- Check Python version compatibility (≥3.8)
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- Ensure all required files are present
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- Validate `pyproject.toml` syntax
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### Upload Failures
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- Verify API tokens are correct
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- Check if version already exists on PyPI
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- Ensure package name is available
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### Import Failures
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- Check package structure (`__init__.py` files)
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- Verify all dependencies are listed
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- Test local installation first
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## Security Notes
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- Never commit API tokens to version control
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- Use environment variables or `.pypirc` for credentials
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- Tokens should have minimal required permissions
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- Consider using Trusted Publishing for GitHub Actions
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