mirror of https://github.com/SuperClaude-Org/SuperClaude_Framework.git synced 2025-12-18 10:16:49 +00:00

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 08:47:09 +05:30

8.5 KiB

Raw Blame History

SuperClaude Advanced Patterns

Advanced Context Usage Patterns: Sophisticated combinations of commands, agents, and flags for experienced SuperClaude users working on complex projects.

Remember: SuperClaude provides context to Claude Code. All patterns here are about guiding Claude's behavior through context, not executing code or coordinating processes.

Context Combination Patterns

Multi-Agent Context Patterns - Combining multiple specialist contexts
Command Sequencing Patterns - Effective command combinations
Flag Combination Strategies - Advanced flag usage

Workflow Patterns

Complex Project Patterns - Large project approaches
Migration Patterns - Legacy system modernization
Review and Audit Patterns - Comprehensive analysis

Multi-Agent Context Patterns

Combining Specialist Contexts

Security + Backend Pattern:

# Security-focused backend development
@agent-security "define authentication requirements"
@agent-backend-architect "design API with security requirements"
/sc:implement "secure API endpoints"

# What happens:
# 1. Security context loaded first
# 2. Backend context added
# 3. Implementation guided by both contexts
# Note: Contexts combine in Claude's understanding, not in execution

Frontend + UX + Accessibility Pattern:

# Comprehensive frontend development
@agent-frontend-architect "design component architecture"
/sc:implement "accessible React components" --magic
@agent-quality-engineer "review accessibility compliance"

# Context layering:
# - Frontend patterns guide structure
# - Magic MCP may provide UI components (if configured)
# - Quality context ensures standards

Manual vs Automatic Agent Selection

Explicit Control Pattern:

# Manually control which contexts load
@agent-python-expert "implement data pipeline"
# Only Python context, no auto-activation

# vs Automatic selection
/sc:implement "Python data pipeline"
# May activate multiple agents based on keywords

Override Auto-Selection:

# Prevent unwanted agent activation
/sc:implement "simple utility" --no-mcp
@agent-backend-architect "keep it simple"
# Limits context to specified agent only

Command Sequencing Patterns

Progressive Refinement Pattern

# Start broad, then focus
/sc:analyze project/
# General analysis

/sc:analyze project/core/ --focus architecture
# Focused on structure

/sc:analyze project/core/auth/ --focus security --think-hard
# Deep security analysis

# Each command builds on previous context within the conversation

Discovery to Implementation Pattern

# Complete feature development flow
/sc:brainstorm "feature idea"
# Explores requirements

/sc:design "feature architecture"
# Creates structure

@agent-backend-architect "review design"
# Expert review

/sc:implement "feature based on design"
# Implementation follows design

/sc:test --validate
# Verification approach

Iterative Improvement Pattern

# Multiple improvement passes
/sc:analyze code/ --focus quality
# Identify issues

/sc:improve code/ --fix
# First improvement pass

@agent-refactoring-expert "suggest further improvements"
# Expert suggestions

/sc:improve code/ --fix --focus maintainability
# Refined improvements

Flag Combination Strategies

Analysis Depth Control

# Quick overview
/sc:analyze . --overview --uc
# Fast, compressed output

# Standard analysis
/sc:analyze . --think
# Structured thinking

# Deep analysis
/sc:analyze . --think-hard --verbose
# Comprehensive analysis

# Maximum depth (use sparingly)
/sc:analyze . --ultrathink
# Exhaustive analysis

MCP Server Selection

# Selective MCP usage
/sc:implement "React component" --magic --c7
# Only Magic and Context7 MCP

# Disable all MCP
/sc:implement "simple function" --no-mcp
# Pure Claude context only

# All available MCP
/sc:analyze complex-system/ --all-mcp
# Maximum tool availability (if configured)

Complex Project Patterns

Large Codebase Analysis

# Systematic exploration of large projects
# Step 1: Structure understanding
/sc:load project/
/sc:analyze . --overview --focus architecture

# Step 2: Identify problem areas
@agent-quality-engineer "identify high-risk modules"

# Step 3: Deep dive into specific areas
/sc:analyze high-risk-module/ --think-hard --focus quality

# Step 4: Implementation plan
/sc:workflow "improvement plan based on analysis"

Multi-Module Development

# Developing interconnected modules
# Frontend module
/sc:implement "user interface module"
@agent-frontend-architect "ensure consistency"

# Backend module
/sc:implement "API module"
@agent-backend-architect "ensure compatibility"

# Integration layer
/sc:implement "frontend-backend integration"
# Context from both previous implementations guides this

Cross-Technology Projects

# Projects with multiple technologies
# Python backend
@agent-python-expert "implement FastAPI backend"

# React frontend
@agent-frontend-architect "implement React frontend"

# DevOps setup
@agent-devops-architect "create deployment configuration"

# Integration documentation
/sc:document --type integration

Migration Patterns

Legacy System Analysis

# Understanding legacy systems
/sc:load legacy-system/
/sc:analyze . --focus architecture --verbose

@agent-refactoring-expert "identify modernization opportunities"
@agent-system-architect "propose migration strategy"

/sc:workflow "create migration plan"

Incremental Migration

# Step-by-step migration approach
# Phase 1: Analysis
/sc:analyze legacy-module/ --comprehensive

# Phase 2: Design new architecture
@agent-system-architect "design modern replacement"

# Phase 3: Implementation
/sc:implement "modern module with compatibility layer"

# Phase 4: Validation
/sc:test --focus compatibility

Review and Audit Patterns

Security Audit Pattern

# Comprehensive security review
/sc:analyze . --focus security --think-hard
@agent-security "review authentication and authorization"
@agent-security "check for OWASP vulnerabilities"
/sc:document --type security-audit

Code Quality Review

# Multi-aspect quality review
/sc:analyze src/ --focus quality
@agent-quality-engineer "review test coverage"
@agent-refactoring-expert "identify code smells"
/sc:improve --fix --preview

Architecture Review

# System architecture assessment
@agent-system-architect "review current architecture"
/sc:analyze . --focus architecture --think-hard
@agent-performance-engineer "identify bottlenecks"
/sc:design "optimization recommendations"

Important Clarifications

What These Patterns Actually Do

✅ Guide Claude's Thinking: Provide structured approaches
✅ Combine Contexts: Layer multiple expertise areas
✅ Improve Output Quality: Better code generation through better context
✅ Structure Workflows: Organize complex tasks

What These Patterns Don't Do

❌ Execute in Parallel: Everything is sequential context loading
❌ Coordinate Processes: No actual process coordination
❌ Optimize Performance: No code runs, so no performance impact
❌ Persist Between Sessions: Each conversation is independent

Best Practices for Advanced Usage

Context Management

Layer Deliberately: Add contexts in logical order
Avoid Overload: Too many agents can dilute focus
Use Manual Control: Override auto-activation when needed
Maintain Conversation Flow: Keep related work in same conversation

Command Efficiency

Progress Logically: Broad → Specific → Implementation
Reuse Context: Later commands benefit from earlier context
Document Decisions: Use /sc:save for important summaries
Scope Appropriately: Focus on manageable chunks

Flag Usage

Match Task Complexity: Simple tasks don't need --ultrathink
Control Output: Use --uc for concise results
Manage MCP: Only activate needed servers
Avoid Conflicts: Don't use contradictory flags

Summary

Advanced SuperClaude patterns are about sophisticated context management and command sequencing. They help Claude Code generate better outputs by providing richer, more structured context. Remember: all "coordination" and "optimization" happens in how Claude interprets the context, not in any actual execution or parallel processing.

8.5 KiB Raw Blame History